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Through specifying the physical properties of a system including material properties and geometrical variations, the resulting mechanical changes on the system from a stimulus can be computed.  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It is a collaborative effort that capitalizes on a diversity of expertise in areas such as clinical, experimental and computational biomechanics, nano-micro scale material modeling, finite element modeling, and neural networks. \n\nGrant Numer: 506297\nPrinciple Investigator: Trent Guess\nCo-Investigators: Ganesh Thiagarajan, Amil Misra, Reza Derakhshani (University of Missouri - Kanas City), Lorin Maletsky (University of Kansas), Terence McIff (University of Kansas Medical Center)\n\nAbstract from grant proposal\n\nDynamic loading of the knee is believed to play a significant role in the development and progression of tissue wear disease and injury. Macro level rigid body joint models provide insight into joint loading, motion, and motor control. The computational efficiency of these models facilitates dynamic simulation of neuromusculoskeletal systems, but a major limitation is their simplistic (or non-existent) representation of the non-linear, rate dependent behavior of soft tissue structures. This limitation prevents holistic computational approaches to investigating the complex interactions of knee structures and tissues, a limitation that hinders our understanding of the underlying mechanisms of knee injury and disease. \n\nThe objective of this project is to develop validated neural network models that reproduce the dynamic behavior of menisci-tibio-femoral articulations and to demonstrate the utility of these models in a musculoskeletal model of the leg. The specific aims of this study are:\n\nAim 1: Develop finite element (FE) models from micro-structure based constitutive methods that bridge the nano-micro scale behavior at the tissue level\n\nAim 2: Develop neural network (NN) based models that learn from FE simulation of dynamic behavior of menisci-tibio-femoral articulations \n\nAim 3: Validate the NN models within a rigid body dynamic model of a natural knee placed within a dynamic knee simulator\n\nAim 4: Demonstrate the utility of the NN models by placing them within a dynamic musculoskeletal model of the leg to study the interdependencies of the menisci and other knee tissues \n\nAim 5: Distribute the validated NN models of menisci-tibio-femoral dynamic response and contact pressure for use in any rigid body model of the knee or leg \n\nThe final product will be Neural Network (NN) models that conform to a modular application programming interface (API) that can be exported to any commercial integrated development environment (IDE) or in-house multi-body model. The NN models will be built upon a multi-scale approach and describe the non linear, rate dependent, non-homogenous dynamic response of menisci-tibio-femoral articulations in a computationally efficient modular package. The multi-scale modeling approach will be validated using a dynamic knee loading machine and the utility of the approach demonstrated by studying the interdependencies of menisci properties, tibio-femoral contact, and anterior cruciate ligament strain during a dual limb squat. A synergistic interdisciplinary team has been assembled to address the objective and aims of the proposed project comprising experts in rigid body dynamics and knee modeling, FE modeling, nano-micro scale material modeling, neural networks, and clinical and experimental biomechanics. \n\nThe proposed research will benefit society at large as the results of this work have potential applications to orthopedics, tissue engineering, and biomaterials. The work will also be a valuable asset to the musculoskeletal research community providing computational tools that may aid research in broad areas such as human movement, prosthetics, tissue engineering, sport injury, and disease.","has_downloads":false,"keywords":"computational model,multiscale,knee","ontologies":"Computational_Model,Neuromuscular_Model","projMembers":"Gavin Paiva,Paul Wilson,Ganesh Thiagarajan,Lorin Maletsky,mohammad kia,Reza Derakhshani,Leo Olcott,Hongzeng Liu,Meenakshi Mishra,Katherine Bloemker,Trent Guess","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"158","unix_group_name":"cmc","modified":"1162444405","downloads":"724","group_name":"Computed Muscle Control","logo_file":"cmc","short_description":"Provide a control library for rapidly generating muscle-actuated simulations  movements that accurately reproduce a specified movement (e.g., an individual's gait pattern as measured in a clinical laboratory).","long_description":"This project consists of the Computed Muscle Control (CMC) library.  CMC is an optimization-based method for controlling systems of articulated rigid bodies.  It is specifically designed for controlling systems that have more than one actuator per degree of freedom.  Human (and animal) musculoskeletal systems are examples of such systems as a single joint is often actuated by many muscles.  CMC also handles the nonlinearities and time delays in force production associated with muscles.  The advantage of CMC over alternative optimal control techniques is its speed.  CMC makes it possible to generate muscle-actuated simulations with detailed three-dimensional models of the musculoskeletal system (e.g., 23 degrees of freedom, 92 muscles) with about 10 minutes of computer time, which is hundreds or even thousands of times faster than traditional optimal control techniques.\n\nThe CMC algorithm is implemented as a set of C++ classes written on top of OpenSim.  OpenSim is an object-oriented framework for modeling, simulating, and analyzing the neuromusculoskeletal system that is also available on Simtk.org.\n\nNote that the CMC source code and documentation will be bundled with future releases of the OpenSim, so it will be unnecessary to download the CMC software from this project.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Frank Clay Anderson","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"159","unix_group_name":"nornalize","modified":"1252717779","downloads":"235","group_name":"noRNAlize: SHAPE data normalization software","logo_file":"nornalize","short_description":"noRNAlize normalizes SHAPE footprinting data","long_description":"This project is a data analysis package to analyze and normalize SHAPE data. Traditionally, SHAPE requires the addition of a 3' hairpin to the RNA for normalization. noRNAlize elminates the need for this experimental step by performing a global analysis of the SHAPE data, and establishing mean protection values. This is particularly important when SHAPE analysis is used to map crystal contacts in crystal structures as illustrated here.","has_downloads":true,"keywords":"rna folding,molecular biology,footprinting,chemical probing","ontologies":"Molecular_Interaction,Data_Analysis_Software","projMembers":"Quentin Vicens,Alain Laederach","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"161","unix_group_name":"openmm","modified":"1709281038","downloads":"724074","group_name":"OpenMM","logo_file":"openmm","short_description":"OpenMM includes everything one needs to run modern molecular simulations.  It is extremely flexible with its custom functions, is open-source, and has high performance, especially on recent GPUs.","long_description":"OpenMM is a toolkit for molecular simulation.  It can be used either as a stand-alone application for running simulations, or as a library you call from your own code.  It \nprovides a combination of extreme flexibility (through custom forces and integrators), openness, and high performance (especially on recent GPUs) that make it truly unique among simulation codes. \n\n<b>NEED HELP?</b> Check out the discussion forums under <a href=\"https://simtk.org/forums/viewforum.php?f=161\">Public Forums</a> and the material from our workshops under <a href=\"https://simtk.org/project/xml/downloads.xml?group_id=161\">Downloads</a>.  \n\n<b>GET STARTED QUICKLY:</b>  Tutorials and sample scripts to run OpenMM are available in the <a href=\"http://wiki.simtk.org/openmm/VirtualRepository\">OpenMM Code Repository</a>.\n\n<b>SOURCE CODE:</b> The source code for OpenMM is available under <a href=\"https://simtk.org/project/xml/downloads.xml?group_id=161\">Downloads</a> and also from the <a href=\"http://www.github.com/SimTk/openmm\">Github Source Code Repository</a>.\n\n<b>BENCHMARKS:</b> A collection of <a href=\"http://wiki.simtk.org/openmm/Benchmarks\">benchmarks</a> is available to show the performance of OpenMM simulating a variety of molecular systems.\n\n<b>CITING OPENMM:</b> Any work that uses OpenMM should cite the papers listed on the <a href=\"https://simtk.org/project/xml/publications.xml/?group_id=161\">Publications</a> page.","has_downloads":true,"keywords":"graphics processing unit,molecular dynamics,gpu","ontologies":"Molecular_Dynamics,Molecular_Model,RNA_Model,Molecular_Modeling_and_Classification,Protein_Model","projMembers":"Vijay Pande,Peter Eastman,Joy Ku,Xuhui Huang,Michael Shirts,Szilard Pall,Kyle Beauchamp,John Chodera,Imran Haque,Blanca Pineda,Lee-Ping Wang,Christoph Klein,Jack Middleton,Peter Kasson,Kim Branson,Joseph Coffland,Alan Wilter  Sousa da Silva,Siddharth Srinivasan,Jesus Izaguirre,Natha Hayre,Tony Tye,Thomas Lane,Grace Tang,Vincent Voelz,Tianyun Liu,Gaetano Calabro',David Minh,Ravinder Abrol,OpenMM Guest,Mark Friedrichs,Randy Radmer,Michael 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Basheer","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"312","fullname":"Developer 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It is based on the CVODES integrator which is part of the DOE Sundials suite. CPODES was developed as a joint project between Simbios and LLNL and implemented by CVODES coauthor Radu Serban working with Michael Sherman, Jack Middleton, and Peter Eastman of Simbios.\n\nCPODES is intended for use with Simbody. It is a multistep integrator providing variable order Adams (up to 12th order) and BDF (up to 5th order) methods for non-stiff problems and BDF (up to 5th order) for stiff problems. It uses CVODES to advance the ODE, and then performs coordinate projection back to the constraint manifold to exactly solve the DAE. The projection is also incorporated back into the error test where it permits larger steps.\n\n<b>IMPORTANT NOTE</b>: binaries of this software are bundled with other SimTK Core modules. They can be found in the SimTKcore project downloads section. Only the source for CPodes is located here.","has_downloads":false,"keywords":"differential algebraic equations,coordinate projection,numerical integrator,stiff integration,multibody equations,implicit integration","ontologies":"Numerical_Integrator","projMembers":"Frank Clay Anderson,Peter Eastman,Randy Radmer,Jack Middleton,Christopher Bruns,Radu Serban,Michael Sherman","trove_cats":[{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"164","unix_group_name":"nano-machines","modified":"1166665285","downloads":"0","group_name":"Protein Models for Biological Machines","logo_file":"","short_description":"As a first step, this physics-based simulation will be used to develop methods to simulate the motion of these models in order to generate alternative plausible conformations.","long_description":"Electron cryomicroscopy (cryoEM) is a maturing field in structural biology that can determine structures of macromolecular machines and cells at a broad range of resolution from 4 to 100 Å. Evidence to the growth of the field, the increasing number of publications in cryoEM has prompted interest from the PDB and EBI to archive density maps and associated models derived from cryoEM (EMDB). Generally, the molecular mass of the biological machine is on the order of 1-100 MDa, which is often too difficult to study by conventional X-ray crystallography. CryoEM is an ideal technique to bridge the information gap between cell biology and crystallography/NMR of individual molecular components of biological machines including viruses, chaperonins, ion channels, ribosomes, transporters, enzymes, filaments and bundles. Single-particle cryoEM can be used to explore the complex and dynamic behavior of individual biological machines in different functional states. However, the resulting data from cryoEM experiments are presently limited to medium (5-10Å) to low (10-20Å) resolutions. This limited resolution is due to several factors including sample heterogeneity due to conformational flexibility. \n\nHere, we hypothesize that single particle cryoEM images contain data of mixed conformations, which can be sorted out computationally to derive multiple models from different subsets of particle images. Therefore, the theme of this proposal is to develop a physics-based computational methodology using SimTK tools to derive an ensemble of potential structural models, which we hypothesize would represent the dynamic nature of the biological machine itself.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mitul Saha","trove_cats":[{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"167","unix_group_name":"allopathfinder","modified":"1695938720","downloads":"5161","group_name":"Predicting allosteric communication in myosin via a conserved residue pathway","logo_file":"allopathfinder","short_description":"<ol><li> Better understand the <b>allosteric communication </b> pathway used by Myosin to convert ATP hydrolysis energy into movement along actin.</li><li> Provide researchers with an application and code for finding protein allosteric pathways.</li></ol>","long_description":"This project contains the AlloPathFinder application that allows users to compute likely allosteric pathways in proteins. The underlying assumption is that residues participating in allosteric communication should be fairly conserved and that communication happens through residues that are close in space.  \nThe initial application for the code provided   was to study the allosteric communication in myosin. Myosin is a well-studied molecular motor protein that walks along actin filaments to achieve cellular tasks such as movement of cargo proteins.\nIt couples ATP hydrolysis to highly-coordinated conformational changes that result in a power-stroke motion, or ''walking'' of myosin. Communication between a set of residues must link the three functional regions of myosin and transduce energy: the catalytic ATP binding region, the lever arm, and the actin-binding domain.  We are investigating which residues are likely to participate in allosteric communication  pathways.","has_downloads":true,"keywords":"allostery,allosteric communication","ontologies":"Protein_Model,Computational_Model,Standalone_Application,Structure-Based_Protein_Classification","projMembers":"Jeanette Schmidt,Susan Tang,Jung-Chi Liao,alex dunn","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":true},{"group_id":"172","unix_group_name":"pcrebin","modified":"1194457619","downloads":"0","group_name":"PCRE regular expression binaries for the SimTK core","logo_file":"","short_description":"Provide application developers with precompiled regular expression libraries for use from C and C++.","long_description":"This project intends to collect precompiled binaries for the PCRE (Perl compatible regular expression) library for the various platforms supported by simtk.  This library permits application developers to use modern regular expressions from C and C++ programs.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Christopher Bruns","trove_cats":[{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"313","fullname":"SimTK Components"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"184","unix_group_name":"molmodel","modified":"1592905556","downloads":"508","group_name":"Molmodel: SimTK molecular modeling API","logo_file":"molmodel","short_description":"Provide C++ API for creating molecular models whose dynamics can be simulated using the SimTK Simbody library. 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Please carefully review the publications and cite the references in your future papers, presentations, grant applications, etc.\n\n<hr> </hr><b>Have a utility to contribute?</b>\nDo you have a utility which you would like to make available through this library? Providing others with access to your tools and utiities can stimulate future studies, provide a foundation for young researchers, and maximize the impact of your work. It’s easy to set up a project page to post your work. This will allow you to track who is using your utilities and be in contact with them. Please consider contributing! If you would like to have your project included on this site, please contact Jennifer Hicks, listed as one of the Project Leads.\n<hr> </hr>\nNo guarantees about quality, correctness or support are provided by the SimTK team or OpenSim team. 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This project is an experimental technique that can quantitatively and fully dissect the molecular constituents of the ion atmosphere associated with nucleic acids. In this project, buffer equilibration is combined with atomic emission spectroscopy (BE-AES) to read out the number of different monovalent and divalent cations associated with a nucleic acid and to quantitate the relative affinity of the cations. Results establish an unprecedented standard for ion association with nucleic acids and allow rigorous examinations of the widely used Nolinear Poisson-Boltzmann(NLPB) electrostatic theory and other theories (see also ISIM project).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Dan Herschlag,Yu Bai","trove_cats":[{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"255","unix_group_name":"joytest","modified":"1511805909","downloads":"0","group_name":"IMAG/MSM Biomechanics Working Group (Demo)","logo_file":"","short_description":"Part of the Interagency Modeling and Analysis Group (IMAG) Multiscale Modeling (MSM) Consortium, this working group seeks to advance the use of MSM within biomechanics.","long_description":"&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/n_3vxZMtae0&quot; frameborder=&quot;0&quot; allowfullscreen&gt;&lt;/iframe&gt;\n&lt;i&gt;Example of multiscale modeling in biomechanics&lt;/i&gt;\n\n&lt;b&gt;Goals and Objectives&lt;/b&gt;\nThrough interactions within members and with other working groups, the goals of the Biomechanics Working Group are:\n\n•\tto establish a cross-discipline discussion platform for multiscale modeling and analysis issues in the general area of biomechanics\n•\tto identify computational infrastructure needs for multiscale biomechanical simulations\n•\tto establish pathways for experimental data and validation to support multiscale modeling and simulation in biomechanics\n•\tto increase awareness to the role of multiscale analysis in biomechanics and simulation-based medicine\n•\tto promote the role of dissemination to accelerate multiscale analysis in biomechanics\n\n&lt;b&gt;History&lt;/b&gt;\nThe Biomechanics Working Group has started in November 2010 following working group related discussions at the &lt;a href=&quot;http://nibibwiki2.nih.gov/mediawiki/index.php?title=2010_MSM_CONSORTIUM_MEETING&quot;&gt;2010 MSM CONSORTIUM MEETING&lt;/a&gt;. Founding co-leads of the working group were Jay Humphrey of Yale University and Ahmet Erdemir of Cleveland Clinic. The working group inherited the &lt;a href=&quot;http://nibibwiki2.nih.gov/mediawiki/index.php?title=Working_Group_6&quot;&gt;Working Group 6 - Tissue Mechanics&lt;/a&gt;, which was started by Trent Guess of University of Missouri, Kansas City.\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Joy Ku,Ahmet Erdemir","trove_cats":[{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"257","unix_group_name":"aymansproject","modified":"1528996216","downloads":"520","group_name":"Ayman s Private Project","logo_file":"","short_description":"A project to maintain OpenSim related materials, presentations, support, and bookKeeping. 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These structures are ready for molecular dynamics simulation.\n\nThe chimeric models were used in identifying key structural elements of myosin VI:\nhttps://simtk.org/home/m6chimera.","has_downloads":true,"keywords":"protein structure prediction,molecular modeling","ontologies":"Protein_Model,Homology_Modeling","projMembers":"Jung-Chi Liao","trove_cats":[{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"260","unix_group_name":"simgrowth","modified":"1252714552","downloads":"125","group_name":"Biomechanics of Growth Directory","logo_file":"simgrowth","short_description":"Provide a basic fundamental learning tool to understand mechanically driven density and volume growth in biological tissues.","long_description":"This project provides a simple but yet very illustrative tool how changes in the mechanical environment effect biological structure, density and volume. The simulation is based on three dimensional geometrically nonlinear finite elements. The code is developed in matlab and very basic. 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Look in the comments for documentation.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Samuel Flores","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":false,"is_model":false,"is_application":true,"is_data":false},{"group_id":"321","unix_group_name":"low-ext-model","modified":"1607049560","downloads":"4208","group_name":"Lower Extremity Model","logo_file":"low-ext-model","short_description":"Lower-extremity model of one or both legs available for download along with accompanying publications.","long_description":"This project holds all the files necessary for a SIMM-based musculoskeletal model of the human lower-extremity which can also be easily imported and used in OpenSIM. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work.  The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;Please cite the following paper:&lt;/b&gt;\n-\tDelp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures, IEEE Transactions on Biomedical Engineering, vol. 37, pp. 757-767, 1990.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;About the model:&lt;/b&gt;\nOriginally developed in DATE by Scott Delp to examine how surgical changes in musculoskeletal geometry and muscle architecture affect muscle force and joint motion this model uses seven segments and seven degrees-of-freedom to represent the human lower extremity.  The model is about 1.8m tall and has the strength of a young, adult male.  Muscle lines of action for forty-three muscle-tendon actuators are based on their anatomical relationships to three-dimensional surface representations of bones. A model for each actuator was formulated to compute its isometric force-length relation. The kinematics of the lower extremity were specified by modeling the hip, knee, ankle, subtalar, and metatarsophalangeal joints. Thus, the force and joint moment that each muscle-tendon actuator develops can be computed for any body position. The joint moments calculated with the model compare well with experimentally measured isometric joint moments.\n\n&lt;hr&gt; &lt;/hr&gt;","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Delp,Katherine Steele","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":false},{"group_id":"322","unix_group_name":"das","modified":"1694502053","downloads":"4873","group_name":"Dynamic Arm Simulator","logo_file":"das","short_description":"Provides a real-time, dynamic simulation of arm movement.","long_description":"This project aims to develop a musculoskeletal model for the real-time, dynamic simulation of arm movement. It features a large-scale model of the shoulder and elbow, including the joints of the shoulder girdle and scapulo-thoracic contact. The simulation is implemented using a Matlab MEX function and uses OpenSim for pre-processing and visualisation.","has_downloads":true,"keywords":"musculoskeletal biomechanics,real time,upper limb,shoulder,rehabilitation","ontologies":"Neuromuscular_Model,Modeling_and_Simulation","projMembers":"Andy Cornwell,Swarna Solanki,Peter Cooman,Matthew Geary,Ed Chadwick,Jéssica de Abreu,Tyler Johnson,AYUSH RAI,Kathleen Jagodnik,Harrison Kalodimos,Anne Koelewijn,Jacob Cox,YuWei Liao,Joris Lambrecht,Brian Murphy,Cale Crowder,Eric Schearer,Ton van den Bogert,Philip Thomas,Robert Kirsch,Matthew Iorio,Francis Willett,Dimitra Blana","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"1002","fullname":"Shoulder Modeling"},{"id":"1002","fullname":"Shoulder Modeling"},{"id":"1002","fullname":"Shoulder Modeling"},{"id":"1002","fullname":"Shoulder Modeling"},{"id":"1002","fullname":"Shoulder Modeling"},{"id":"1002","fullname":"Shoulder Modeling"},{"id":"1002","fullname":"Shoulder Modeling"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":false},{"group_id":"323","unix_group_name":"femur-model","modified":"1219187772","downloads":"1036","group_name":"Deformable Femur Model","logo_file":"femur-model","short_description":"Deformable model of the femur available for download along with accompanying publications.","long_description":"This project holds all the files necessary for a SIMM-based musculoskeletal model of deformable human femur that can be used to easily model rotational deformities. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work.  The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;Please cite the following paper:&lt;/b&gt;\n-\tArnold and Delp. Rotational moment arms of the hamstrings and adductors vary with femoral geometry and limb position: implications for the treatment of internally-rotated gait. Journal of Biomechanics, 2001.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;About the model:&lt;/b&gt;\nOriginally developed by Scott Delp and Alison Arnold to examine the effects of bony deformities of the femur, this model characterizes the geometry of the pelvis, femur, and proximal tibia, the kinematics of the hip and tibiofemoral joints, and the paths of the medial hamstrings, iliopsoas, and adductor muscles for an average-sized adult male.  The femur of the model can be altered to represent anteversion angles of 0-60°, neck-shaft angles of 110-150°, and/or neck lengths of 35-60 mm. The lesser trochanter torsion angle of the model can be adjusted by as much as 30° anteriorly or 10° posteriorly. Hence, this model enables rapid and accurate estimation of muscle-tendon lengths and moment arms for individuals with a wide range of movement abnormalities and femoral deformities.\n\n&lt;hr&gt; &lt;/hr&gt;","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Allison Arnold,Scott Delp,Katherine Steele","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"324","unix_group_name":"up-ext-model","modified":"1607050065","downloads":"10562","group_name":"Upper Extremity Kinematic Model","logo_file":"up-ext-model","short_description":"Upper-extremity available for download along with accompanying publications.","long_description":"The project holds all the files necessary for a SIMM-based kinematic musculoskeletal model of the human upper-extremity which can also be easily imported and used in OpenSIM. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work.  The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\n<hr> </hr>\n<b>Please cite the following paper:</b>\n-\tHolzbaur KR, Murray WM, Delp SL.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control., Ann Biomed Eng. 2005 Jun;33(6):829-40. (2005)\n\n<hr> </hr>\n<b>About the model:</b>\nThis model of the upper extremity includes 15 degrees of freedom representing the shoulder, elbow, forearm, wrist, thumb, and index finger, and 50 muscle compartments crossing these joints. The kinematics of each joint and the force-generating parameters for each muscle were derived from experimental data. The model estimates the muscle–tendon lengths and moment arms for each of the muscles over a wide range of postures. Given a pattern of muscle activations, the model also estimates muscle forces and joint moments. The moment arms and maximum moment generating capacity of each muscle group (e.g., elbow flexors) were compared to experimental data to assess the accuracy of the model. These comparisons showed that moment arms and joint moments estimated using the model captured important features of upper extremity geometry and mechanics. The model also revealed coupling between joints, such as increased passive finger flexion moment with wrist extension.\n\n<hr> </hr>","has_downloads":true,"keywords":"muscle moment arms,upper limb,kinematic model,muscle architecture parameters","ontologies":"Modeling_and_Simulation","projMembers":"Scott Delp,Katherine Saul,Wendy Murray,Katherine Steele","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"325","unix_group_name":"wrist-model","modified":"1219187433","downloads":"2049","group_name":"Wrist Model","logo_file":"wrist-model","short_description":"Model of the wrist available for download along with accompanying publications.","long_description":"The project holds all the files necessary for a SIMM-based musculoskeletal model of the human wrist which can also be easily imported and used in OpenSIM. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work.  The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;Please cite the following paper:&lt;/b&gt;\nGonzalez, R. V., Buchanan, T. S., Delp, S. L. How muscle architecture and moment arms affect wrist flexion-extension moments, Journal of Biomechanics Vol. 30, pp. 705-712, 1997.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;About the model:&lt;/b&gt;\nOriginally created by Delp and Gonzalez to investigate motion of the wrist and gain insight into surgical procedures this model consists of all of the bones of the arm and 10 degrees of freedom.  Pronation-supination, flexion-extension, and ulnar-radial deviation are all included within the model as well as degrees of freedom for the elbow, thumb, and index finger.  A total of 23 muscle actuators control motion.\n\n&lt;hr&gt; &lt;/hr&gt;","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Thomas S Buchanan,Scott Delp,Katherine Steele,Roger Gonzalez","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"327","unix_group_name":"dca","modified":"1274054485","downloads":"0","group_name":"Divide and Conquer Coarse-Grain Molecular Modeling","logo_file":"","short_description":"It is difficult to predict a static coarse grain structure which is simultaneously accurate and efficient for the entire course of the simulation. The goal is to develop an adaptive solver which can change the coarse-grain structure on-the-fly.","long_description":"The divide and conquer algorithm [1-3] would make it easier to implement frequent topology changes (by adding or constraining degrees of freedom) in coarse grain molecular models.  This approach may be specially useful in situations where it is desirable to adaptively manipulate/change the coarse grain model locally, during the course of simulation.\n\nSimulation Example: \nhttps://simtk.org/docman/view.php/327/1350/pend.gif\n\nCurrent interface with Molmodel:\nhttps://simtk.org/docman/view.php/327/1380/molmodelDCA01.pdf\n\n[1]  R. Featherstone, 1999a.  A Divide-and-Conquer Articulated-Body Algorithm for Parallel O(log(n)) Calculation of Rigid-Body Dynamics.  Part 1: Basic Algorithm.  Int. J. Robotics Research, vol. 18, no. 9, pp. 867-875, 1999.\n\n[2] R. Featherstone, 1999b. A Divide-and-Conquer Articulated-Body Algorithm for Parallel O(log(n)) Calculation of Rigid-Body Dynamics.  Part 2: Trees, Loops and Accuracy.  Int. J. Robotics Research, vol. 18, no. 9, pp. 876-892, 1999.\n\n[3] Rudranarayan M. Mukherjee and Kurt S. Anderson, A Logarithmic Complexity Divide-and-Conquer Algorithm for Multi-flexible Articulated Body Dynamics, Journal of Computational and Nonlinear Dynamics, January 2007, Volume 2, Issue 1, pp. 10-21","has_downloads":false,"keywords":"Molecular Modeling,DCA,Adaptive","ontologies":"Structural_Model","projMembers":"Kishor Bhalerao","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"329","unix_group_name":"climber","modified":"1297915633","downloads":"2015","group_name":"Climber: a non-linear protein trajectory morphing method","logo_file":"climber","short_description":"Provide high-fidelity morphed protein trajectories between two known protein conformations.","long_description":"We present a new morphing method that does not move linearly and can therefore go around high energy barriers, and which can produce different trajectories between the same two starting points. The input  are two protein conformations (an initial and final conformation) and an alignment that will define which inter-residue distances are restrained to reach the final structure.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Dahlia Weiss","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"308","fullname":"Myosin"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"330","unix_group_name":"torso_legs","modified":"1607050040","downloads":"1624","group_name":"Torso + Lower Extremity Model","logo_file":"torso_legs","short_description":"OpenSIM model of lower-extremity and torso for simulating human movement.","long_description":"This project contains an OpenSIM model file that includes a torso segment in addition to the lower extremity. The model contains 23 degrees of freedom and 92 muscle-tendon actuators.  The joint between the torso and the pelvis is represented by a ball-and-socket joint. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work.  The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;Please cite the following paper:&lt;/b&gt;\n- Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures, IEEE Transactions on Biomedical Engineering, vol. 37, pp. 757-767, 1990.\n\n&lt;hr&gt; &lt;/hr&gt;\n&lt;b&gt;About the model:&lt;/b&gt;\nThe lower-extremity portion of the model was originally developed by Scott Delp to examine how surgical changes in musculoskeletal geometry and muscle architecture affect muscle force and joint motion.  With the addition of the torso segment this model has 23 degrees of freedom and 92 muscle actuators.  The model is about 1.8m tall and has the strength of a young, adult male.  Muscle lines of action are based on their anatomical relationships to three-dimensional surface representations of bones. A model for each actuator was formulated to compute its isometric force-length relation. The kinematics of the lower extremity is specified by modeling the lumbar, hip, knee, ankle, subtalar, and metatarsophalangeal joints. \n\n&lt;hr&gt; &lt;/hr&gt;","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Delp,Katherine Steele","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"331","unix_group_name":"jamboree","modified":"1507738885","downloads":"89","group_name":"OpenSim Developer's Jamboree 2008","logo_file":"jamboree","short_description":"Provide access to the latest version of OpenSim and a central repository for the OpenSim Jamboree participants","long_description":"This project will be a repository for the OpenSim Jamboree projects.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Joy Ku,Scott Delp,Ajay Seth,Jeff Reinbolt,Ayman Habib,Chand John,Sam Hamner,Jennifer Hicks,Thor Besier,Tom Kepple,Kevin Shelburne,Chris Richards,Katherine Saul,Ilse Jonkers,Friedl De Groote,B.J. Fregly,Matt DeMers,Melanie Fox,Wendy Murray,Istvan Lauko,John Lloyd,Anthony Kulas,Nils Hakansson,Brian Garner,Craig Goehler,Antonio Veloso,Dimitrios Tsaopoulos,Patrick Rider,Filipa Joao,Arash Mahboobin,Paul Harrington,Xuemei Huang,Vivian Allen,Ricardo Matias,Max Weigel","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"332","unix_group_name":"dynamical-reweighting","modified":"1410870255","downloads":"0","group_name":"Dynamical reweighting toolkit","logo_file":"","short_description":"Provides tools for reweighting molecular dynamics simulations conducted at multiple temperatures to extract dynamical properties","long_description":"This project provides a set of tools for the computation of dynamical properties (such as time-correlation functions, transition matrices for Markov models, and rate constants) from simulation data collected at multiple temperatures, such as simulated or parallel tempering simulations.  Tools and datasets used in the paper(s) are provided within this project.","has_downloads":false,"keywords":"Molecular Dynamics","ontologies":"Molecular_Dynamics","projMembers":"Frank Noe,Vijay Pande,Kyle Beauchamp,John Chodera,Jan-Hendrik Prinz","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":true},{"group_id":"333","unix_group_name":"ia-femesh","modified":"1220212291","downloads":"3812","group_name":"IA-FEMesh","logo_file":"ia-femesh","short_description":"Provides an efficient and reliable method for finite element model development, visualization, and mesh quality evaluation","long_description":"In an effort to facilitate anatomic FE model development, we introduce IA-FE Mesh (Iowa FE Mesh), a freely available software toolkit. IA-FEMesh employs a multiblock meshing scheme aimed at hexahedral mesh generation.  An emphasis has been placed on making the tools interactive, in an effort to create a user-friendly environment.  The goal is to provide an efficient and reliable method for model development, visualization, and mesh quality evaluation.  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To achieve this, we developed a three-dimensional muscle-actuated simulation of running that included 92 musculotendon actuators representing 76 muscles of the lower extremities and torso. By using a three-dimensional model with lower extremity muscles, a torso, and arms, we were able to quantify the contribution of muscles and arm dynamics to mass center accelerations in three dimensions, which provided insights into the actions of muscles during running. The simulation is freely available (simtk.org) allowing other researchers to reproduce our results and perform additional analyses.","has_downloads":true,"keywords":"Musculoskeletal Modeling,Muscle Function,Induced Acceleration Analysis,Human Locomotion,Forward Dynamic Simulation","ontologies":"Modeling_and_Simulation,Multibody_Dynamics,Neuromuscular_Model","projMembers":"Ajay Seth,Scott Delp,Jennifer Hicks,B.J. 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To bridge between structure and function, a molecular understanding of the most probable transition pathways between the two end structures is required.\n\nWhile there are many computational methods for exploring the transitions of small proteins, the task of exploring the transition pathways becomes prohibitively expensive in the case of supramolecular systems.  Coarse-grained models that lend themselves to analytical solutions appear to be the only possible means of approaching such cases.  Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems, and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM) for exploring functional transitions.\n\nAs described by aANM, a series of intermediate conformations along the transition pathways between the initial and final conformations were generated by successive deformations of both end structures that were iteratively updated.  The directions of deformations were determined by implementing the deformations along the directions of dominant ANM modes accessible to the intermediate states.  The recruitment of the particular subsets of modes results from a tradeoff between minimizing the path length and selecting the direction of the lowest increase in internal energy. To calculate the ANM modes, please visit our related websites.\nANM: http://ignmtest.ccbb.pitt.edu/cgi-bin/anm/anm1.cgi \nGNM: http://ignm.ccbb.pitt.edu/GNM_Online_Calculation.htm\nPCA_NEST: http://ignm.ccbb.pitt.edu/oPCA_Online.htm\n\nThe bacterial chaperonin GroEL is a supramolecular machine that has been broadly studied in recent years using both experimental and theoretical or computational methods. Yet, a structure-based analysis of the transition of the intact chaperonin between its functional forms has been held back by the large size of the chaperonin.  The aANM method is proposed as a first approximation toward approaching this challenging task.  \n\nThe application of aANM to GroEL, not only elucidated the highly probable pathways and the hierarchic contribution of modes to achieve the transition; but also provided us with biologically significant information on critical interactions and sequence of events occurring during the chaperonin machinery and key contacts that make and break at the transition.\n\nOn a practical side, the major utility of the method lies in its application to the transitions of supramolecular systems beyond the range of exploration of other computational methods.  The computing time in the present method is several orders of magnitude shorter than that required in regular molecular dynamics or Brownian dynamics simulations.  \n\n*Figure above: Snapshots of the protein chaperonin GroEL in its transition pathway, evolving from open (upper left) to closed form (lower right). 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Future models will incorporate joints with stiffness properties to more accurately mimic the action of the intervertebral joints.\n\nThe most complex of these models also feature the 238 muscle fascicles associated with the 8 main muscle groups of the lumbar spine necessary to study the contribution of the lumbar spinal musculature to spinal motion. Simpler models incorporating two and seven of the main muscle groups of the lumbar spine are provided as well for completeness.\n\nRead more about the model in the paper, freely downloadable at http://link.springer.com/article/10.1007%2Fs10237-011-0290-6.\n\n----------------------------------------------------------------\n----------------------------------------------------------------\nSeptember 2011 Addendum\nClick on the \"Downloads\" link to the left for downloads related to more recent work.\n\n----------------------------------------------------------------\nSeptember 2012 Addendum\nThe Constrained Lumbar Spine Model does not require any of the files uploaded after the creation of the Constrained Lumbar Spine Model project. The .vtp files (and descriptions) are included here for the benefit of those of you who wish to create your own model that has origins shifted to the center of the bones since this typically saves a number of transformations. Many apologies for any confusion(!).\n\n-----------------------------------------------------------------\nMarch 2014 Addendum\n(1)\nThis model was build with OpenSim 2+. Version 3+ will not allow you to use periods (.) in your variable names. Unfortunately, a bunch of the variables used (muscles mainly) have periods in the names so it will throw an error if you try and run it in OpenSim version 3+. To fix this, either use version 2+, OR, rename the variables appropriately.\n\n(2)\nWe have all graduated and are no longer actively working on this project (we haven't been working on it since the end of 2011 actually). At this point, you probably know more than us about OpenSim so we apologize in advance if our support is subpar.  \n\n(3)\nThe complex mode is not meant to be run straight out of the box. It has almost 250 muscles after all and unless you have a super computer, running CMC, or FD on it is going to bring up the rainbow ball of death on your computer. \nRather, it's meant to be a reference for those of you who intend to build up your own model. My advice would be to start with the simple 4 fascicle model, get it to work, then incrementally build up from there using the parameters provided in our model as a starting point. Copy-Paste is your friend here. :)\n\n(4)\nIf this is your very first OpenSim project, I strongly _strongly_ *strongly* suggest that you go through the examples provided with the OpenSim version you just downloaded and understand how they work. 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Please see the developer wiki for more information. Therefore this is being kept only as an archive, and users should see the documentation on interfacing Matlab with Opensim on the Opensim website (http://simtk-confluence.stanford.edu:8080/display/OpenSim/Scripting+with+Matlab).\n\nAs such the forums etc will not be monitored.","long_description":"Matlab is a common analysis tool used for data manipulation, signal processing and function integration. These features can be used in conjunction with simulation tools provided by the Opensim interface.\n\nThis project provides tools for using different aspects of Opensim within the Matlab environment. This includes 1) using the command line tools by generating XML setup files etc (Scaling, Inverse Kinematics, Inverse Dynamics, Forward Dynamics) 2) using the Java classes that the Opensim GUI is built on to access aspects of the Opensim API. \n\nProvided in this project are - \n\n1) Tools for taking motion capture data from C3D files and generating the required input files (marker files {*.trc} motion files {*.mot}, GRF xml files {*.xml}) as well as setup files for each of the different tools that can be called from the command line. Example data from different models and data sets are provided including example pipelines to analyse data using Opensim. Some of this implementation has taken inspiration from Tim Dorn's excellent GaitExtract toolbox. A new page with more up-to-date tools can be found here - http://simtk-confluence.stanford.edu:8080/display/OpenSim/Tools+for+Preparing+Motion+Data \n\n2)Matlab functions and example scripts for accessing the Opensim API through Matlab. This utilises the Java wrapping classes that the Opensim GUI is built on. Examples are shown to open and edit models as well as perform a 'Muscle Analysis'. Please now use the inbuilt support from Opensim rather than this toolbox! 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This is thought to be due to changes in muscle structure that occur secondary to the neural lesion which causes spasticity [1]. These changes are commonly termed muscle contracture. Although we know that muscle contracture and weakness progress with age [2], the precise structures responsible and the influence this has on functional capacity are not clearly understood. \n\nRecent work in children with CP has demonstrated that the structural and mechanical properties of spasticity affected muscle are very different to that of typically developed (TD) muscle [3-5]. As such, the interplay between muscle dynamics and the subsequent movement will be very different in this population. Simulation studies up until this point [6, 7] have primarily focused on using abnormal movement patterns to examine variations in muscle coordination without fully considering the impact of changed structural and mechanical muscle-tendon properties. \n\nThe underlying rationale for the proposed work is that muscle weakness arising from altered muscle-tendon structural and mechanical properties may be a primary factor limiting normal gait in CP. We aim to use experimental measures of muscle-tendon structural and mechanical properties along with 3D gait analysis to determine the influence of changed muscle properties on observed movement outcomes. \n\nAim 1: Determine the influence of using the experimentally determined muscle properties from an adolescent CP population on the predicted muscle activation patterns during both normal (TD) and equinus (CP) gait.\n\nAim 2: Perform sensitivity analysis to examine which muscle-tendon properties are most detrimental to gait performance in CP.\n\nREFS -\n1.\tGraham, H.K., Painful hip dislocation in cerebral palsy. Lancet, 2002. 359(9310): p. 907-8.\n2.\tBache, C.E., P. Selber, and H.K. Graham, The management of spastic diplegia. Current Orthopaedics, 2003. 17(2): p. 88-104.\n3.\tBarber, L., et al., Medial gastrocnemius muscle volume and fascicle length in children aged 2-5 years with cerebral palsy. Dev Med Child Neurol, 2011(In Press).\n4.\tBarrett, R.S. and G.A. Lichtwark, Gross muscle morphology and structure in spastic cerebral palsy: a systematic review. Dev Med Child Neurol, 2010. 52(9): p. 794-804.\n5.\tMalaiya, R., et al., The morphology of the medial gastrocnemius in typically developing children and children with spastic hemiplegic cerebral palsy. Journal of Electromyography and Kinesiology, 2007. 17(6): p. 657-663.\n6.\tHigginson, J.S., et al., Effect of equinus foot placement and intrinsic muscle response on knee extension during stance. Gait Posture, 2006. 23(1): p. 32-6.\n7.\tSteele, K.M., et al., Muscle contributions to support and progression during single-limb stance in crouch gait. 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While this biomechanical tool is sufficiently robust to analyze single trials and to batch-process selected multiple trials, the subject-specific model scaling and the relevant data has to be manually picked and analyzed. The relational database that combines meta- and signal- information can automate both the model scaling and the automation of data analysis. The goal of this project is to integrate SciBox relational database toolbox with OpenSim environment. BOXsci is a lightweight relational database with transparent scripting library and graphical user interface implemented fully in Matlab, the data analysis package of choice in academia and private industry. BOXsci consists of a single library for data storage, access, and analysis that is freely available for academic, research and non-profit organizations. 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MBKnee_4 is the most recent model and it includes representation of the medial and lateral menisci, wrapping around bone and cartilage of the meniscal horn attachments, attachments of the deep medial collateral ligament and the anterolateral ligament to the menisci, representation of the posterior oblique ligament and the anterolateral ligament, ligament zero load lengths (or reference strain) determined from experimental laxity measurements, and measured motion to deep flexion.  \n\nFunding for this work was provided by the National Institute of Arthritis an Musculoskeletal and Skin Diseases (RAR061698) and by the National Science Foundation (CMS-0506297).","has_downloads":true,"keywords":"knee,ligament,meniscus,multibody dynamics","ontologies":"Neuromuscular_Model,Computational_Model,Mechanical_Simulation,Multibody_Dynamics,Modeling_and_Simulation,Computational_Geometry","projMembers":"Trent Guess","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":true},{"group_id":"766","unix_group_name":"v3pu","modified":"1462400356","downloads":"0","group_name":"Virtual Prototyping for Prevention of Pressure Ulcers","logo_file":"","short_description":"This project aims to develop models i) to explore the etiology of pressure ulcers, and ii) to aid the design and evaluation of intervention strategies for their prevention.","long_description":"The goal of this project is to establish a computational modeling and simulation framework where an investigator can explore the importance of different mechanical variables on pressure ulcer formation and can evaluate isolated or combined effects of interventions on relative risk of pressure ulcer development. A finite element representation will incorporate the anatomy of an at-risk region, i.e. a bony prominence with multiple tissue layers, and also the deformation characteristics of the tissue layer and support surfaces. 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This toolkit:\n\n- is written in Python\n- turns the problem of planning relative free energy calculations within a library into a graph theory problem\n- outputs a map of planned calculations \n\nThis is written for computational scientists working in the pharmaceutical industry generally, including academia, industry, and elsewhere, who need tools to help plan lead optimization campaigns.\n\nThe code is being released under the BSD license and hopefully will be a community effort.\n\nPlease contact David Mobley and Shuai Liu if you need any help getting this to work or any clarification on installing, etc.\n\nIf you use this, please cite our paper in JCAMD: http://link.springer.com/article/10.1007%2Fs10822-013-9678-y","has_downloads":false,"keywords":"free energy calculations,planning,drug discovery,alchemical free energy calculations,lead optimization,molecular modeling","ontologies":"","projMembers":"Shuai Liu,David Mobley,Jonathan Redmann,Yujie Wu,Christopher Summa,Teng Lin","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"773","unix_group_name":"full_body","modified":"1607049466","downloads":"12181","group_name":"Full Body Model for use in Dynamic Simulations of Human Gait","logo_file":"full_body","short_description":"Full body musculoskeletal model with muscle-actuated lower extremity and torque-actuated torso/upper extremity for use in dynamic simulations of human movement.","long_description":"Our paper describes a full body OpenSim model with musculotendon parameters derived from experimental measurements of 21 cadaver lower limbs and magnetic resonance images of 24 young adult subjects. 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(2013), but updates the muscle force distribution to reflect those of a young, healthy population, includes a new knee model to accurately represent internal forces, and simplified muscle wrapping surfaces to increase computation speed in CMC and other muscle-driven simulations.","has_downloads":true,"keywords":"biomechanics,gait,simulation,opensim model,musculoskeletal model","ontologies":"","projMembers":"Matt DeMers,Christopher Dembia,Jennifer Hicks,Scott Delp,Apoorva Rajagopal,Reviewer IEEE TBME","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"777","unix_group_name":"opensim_data","modified":"1462394013","downloads":"0","group_name":"OpenSim Motion and Simulation Data","logo_file":"opensim_data","short_description":"The purpose of this umbrella project is to provide a central repository of user-contributed OpenSim motion and simulation data.","long_description":"<i>This collection is no longer being maintained through this project. To find OpenSim motion and simulation data, you can conduct a search on SimTK. Go to https://simtk.org/search/search.php?srch=neuromuscular&type_of_search=soft and then narrow your search to \"data sets\" by checking the box on the left.</i>\n\n----------------------------------\n\nA repository of motion data from experiments and simulations, contributed by members of the OpenSim community.\n\n<b>Please respect your fellow OpenSim Users.</b> \nIn using these data we ask that you respect the hard work of your fellow researchers by citing their work appropriately. When you go to the Download section you will be directed to individual project pages for each model which contain all of the files and documentation. Please carefully review the publications and cite the references in your future papers, presentations, grant applications, etc.\n\n<b>Have data to contribute?</b>\nDo you have simulation or motion data which you would like to make available through this library? Providing others with access to your data can stimulate future studies, provide a foundation for young researchers, and maximize the impact of your work. It’s easy to set up a project page to post your work. This will allow you to track who is using your data and be in contact with them. Please consider contributing! If you would like to have your project included on this site, please contact Jennifer Hicks, listed as one of the Project Leads.\n\nNo guarantees about quality, correctness or support are provided by the SimTK team or OpenSim team. Use at your own risk. \n\nTo find out more about the OpenSim project, please visit http://opensim.stanford.edu.","has_downloads":false,"keywords":"experimental data,OpenSim,Simulations","ontologies":"Modeling_and_Simulation,Data_Resource","projMembers":"Jennifer Hicks","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":true},{"group_id":"779","unix_group_name":"implicit_ligand","modified":"1346267490","downloads":"111","group_name":"Implicit Ligand Theory: Rigorous Binding Free Energies from Molecular Docking","logo_file":"","short_description":"A reference, not a general-purpose tool, for researchers interested in performing implicit ligand theory calculations.","long_description":"In this study, a theoretical foundation was derived for molecular docking.  Binding free energy calculations based on this theory showed good agreement with previous free energy calculations and experimental results.","has_downloads":true,"keywords":"alchemical free energy calculations,Molecular Dynamics,Molecular Docking","ontologies":"Molecular_Dynamics,Molecular_Model","projMembers":"David Minh","trove_cats":[{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":false},{"group_id":"780","unix_group_name":"ob-biomechanics","modified":"1351807652","downloads":"242","group_name":"Obese Locomotion Biomechanics","logo_file":"ob-biomechanics","short_description":"Create several musculoskeletal model templates that can be more closely matched to the size and shape of individual obese subjects and complete forward dynamic simulations utilizing the refined models to design potential interventions.","long_description":"We are in the process of designing and creating a number of obese specific musculoskeletal model templates that will account for changes in muscle properties and the adiposity distribution of obese body types. The refined model will not only provide more accurate scientific data on obese locomotion, but will also be used in combination with forward dynamic analyses as a predictive tool to analyze locomotion.  These templates will also allow researchers to examine potential benefits of proper exercise prescription and targeted muscle strengthening, and to validate possible benefits of functional bracing and movement aiding exoskeletons. \n\nFind more information here: http://pal.colostate.edu/","has_downloads":true,"keywords":"Obese,Biomechanics,Musculoskeletal Model","ontologies":"Neuromuscular_Model","projMembers":"Ray Browning,Zach Lerner,Wayne Board,Derek Haight","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":false},{"group_id":"785","unix_group_name":"md_b1ar","modified":"1341999172","downloads":"0","group_name":"Ligand Binding in Beta1-Adrenergic Receptor MD Model","logo_file":"","short_description":"Archive of beta1-adrenergic receptor MD simulation trajectories","long_description":"This project is a collection of molecular dynamics trajectories modeling the turkey B1AR crystal structure (PDBID: 2VT4 and 2Y02) in an explicit membrane and solvent model (using Maestro and Desmond by Schrodinger). The protein was modeled with and without thermostabilizing point mutations (from crystalization) reversed, and in several ligand conditions: without a ligand, with antagonist cyanopindolol, and agonists carmoterol and isoprenaline. Computational biologists interested in GPCRs can study the small movements of amino acid side chains that lead to activation in these trajectories.","has_downloads":false,"keywords":"Molecular Dynamics Trajectories","ontologies":"Molecular_Dynamics","projMembers":"Tiffany Shih","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":false,"is_application":true,"is_data":true},{"group_id":"787","unix_group_name":"paropt","modified":"1342670047","downloads":"0","group_name":"Parameter optimization of human movement simulations using OpenSim","logo_file":"","short_description":"To provide a parameter optimization framework within the OpenSim API (and eventually within the GUI) for performing optimal control simulations of human movement.","long_description":"Optimal control models of human movement are attractive for their ability to elegantly simulate complex movements by minimizing comparatively simple cost functions.  Concepts from optimal control theory have been used in human movement science for decades, and models of this sort have arguably been more successful in predicting human motor behavior than any other type of model.  However, the adoption of optimal control theory as a prominent research tool in biomechanics has been consistently hindered by two problems.  First, the characteristics of biomechanical simulation models are often not well suited to the formalized solution techniques for optimal control theory.  Second, the competencies required to create models and perform simulations are often outside the core training received by researchers in biomechanics and motor control, and the time course for learning these methods and developing valid simulations models from scratch can take years.\n\nThe first problem was addressed now 20 years ago with the introduction of Parameter Optimization, which converts optimal control problems to nonlinear programming problems by parameterizing the assumed time-varying neuromuscular control functions.  This approach has since been used extensively in simulation studies of human movement.  The second problem has been addressed more recently by OpenSim.  To generate simulations, OpenSim implements a Computed Muscle Control algorithm, which greatly reduces the computational time required for conventional forward dynamics simulations, but relies on assumptions that (i) resultant joint moments are distributed into individual muscle forces according to a minimization rule, and (ii) a time-varying ground reaction force (GRF) at the the foot-floor interface is known ahead of time.  These assumptions may not always be desirable, for example when simulating explosive movements where minimization rules are unlikely to apply, or when performing exploratory work on motor control principles.  In these situations, it would be useful to have solutions to both problems of optimal control implementation within a common biomechanical modeling and simulation framework.","has_downloads":false,"keywords":"optimal control,forward dynamics,movement simulation,parameter optimization","ontologies":"Modeling_and_Simulation","projMembers":"Ross Miller","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"788","unix_group_name":"science2011msms","modified":"1342732963","downloads":"0","group_name":"Markov State Models of Ultralong MD Trajectories (2011)","logo_file":"","short_description":"This project contains Markov state models of fourteen protein folding datasets.","long_description":"This project contains Markov state models of fourteen protein folding datasets.","has_downloads":false,"keywords":"Markov State Model","ontologies":"Molecular_Dynamics","projMembers":"Kyle Beauchamp","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"791","unix_group_name":"t7phagefba","modified":"1384361757","downloads":"38","group_name":"T7 phage replication interaction with host E. coli metabolism","logo_file":"","short_description":"Provides the simulation code used in published integration of phage T7 ODEs and E. coli FBA.","long_description":"The associated publication described the integration of T7 phage viral replication Ordinary Differential Equations with host E. coli metabolic Flux Balance Analysis such that the two models interact and constrain one another over the time course of infection and virion synthesis. This code runs the simulations and produces the results figure panels in the publication.","has_downloads":true,"keywords":"systems biology,flux-balance analysis,Escherichia coli,bacteriophage","ontologies":"","projMembers":"Elsa Birch","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"793","unix_group_name":"opensim-matlab","modified":"1602268897","downloads":"0","group_name":"Platform for Dynamic Simulation and Control of Movement based on OpenSim&MATLAB","logo_file":"opensim-matlab","short_description":"This project overcomes neuromusculoskeletal systems limitations of MATLAB®/Simulink® and robust design & control limitations of OpenSim through an interface between these two software packages that combines relevant strengths of each individual package.","long_description":"Numerical simulations are playing an increasingly important role in solving complex engineering problems, and have the potential to revolutionize medical decision making and treatment design. Musculoskeletal diseases cost the United States economy an estimated $849 billion a year (equal to 7.7% of the gross domestic product) and place great demands on the healthcare system. This research area could greatly benefit from computational tools that offer greater understanding of neuromuscular biomechanics, and predictive capabilities for optimal surgical and rehabilitation treatment planning.\n\nThe MATLAB®/Simulink® package is the world’s leading mathematical computing software for engineers and scientists in industry, government, and education. Although Simulink® extends MATLAB® with a graphical environment for rapid design, control, and simulation of complex dynamic systems, this powerful package has limited resources for simulations of neuromusculoskeletal systems. On the contrary, OpenSim is a popular open-source platform for modeling, simulating, and analyzing neuromusculoskeletal systems, but it is lacks the robust design and control tools of Simulink®.\n\nThis project is an interface between OpenSim and MATLAB®/Simulink® that combines relevant strengths (e.g., neuromusculoskeletal dynamics, rapid model-based design, control systems, and numerical simulation) of each individual software package. The foundation of this interface is a MATLAB® S-function (system-function) based on an OpenSim model as a Simulink® block written in C++ and compiled as a MEX-file using the MATLAB® mex utility.","has_downloads":false,"keywords":"Matlab,Neuromuscular control,OpenSim,simulink,Musculoskeletal Modeling,musculoskeletal biomechanics","ontologies":"Neuromuscular_Model,Controllers,Modeling_and_Simulation","projMembers":"Jeff Reinbolt,Ajay Seth,Katherine Saul,Jill Higginson,Brian Umberger,Nathan Pickle,XuanYang Xi,Raghu Ramanathan,Renquan Duan,Aaron Suberbiola,Xiangjie Meng,Scott Brandon,Diego Torricelli,Paul Mitiguy,Dimitar Stanev,Misagh Mansouri,sariah Mghames,jimmy d,Thomas Uchida,Aaron Fox,Samane Amini,Nathan Guthrie,Alexander Leonessa,Duanyi Wei,ceren kaya,Melanie Kolditz,ali safaeifar,Khalil Alipour,GAMZE BALKAN,Wellington Pinheiro,Cole Simpson,Peter J,Leng-Feng Lee,Chuong Nguyen,Stefano Piazza,Myroslav Bachynskyi,Sangala Girish kumar reddy,Victoria Neilson,mukul talaty,Omid Khaniki,Maria Makarov,Aaron Fox,Rejin Varghese,naji alibeji,Aminreza Khandan,Alex Hunt,Ana Lucia Cruz Ruiz,Marien  Narváez,Giulia Mantovani,Gautam Ramachandra,Chethan Mysore Parameshwara,seyedehmarzieh hosseini,ANDRES HIDALGO,William Barker,Neda Mehri,alireza sanaee,Lisa Ott","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"795","unix_group_name":"openmmfbm","modified":"1344020242","downloads":"0","group_name":"OpenMMFBM Flexible Block Method Implementation Using OpenMM","logo_file":"","short_description":"Provide an implementation of the Flexible Block Method, a fast yet accurate approximation method for performing Normal Mode Analyses of biomolecules, for GPUs.","long_description":"Normal Mode Analysis (NMA) is commonly used to analyze the motions of proteins.  Examples include determining the effects of binding on proteins, determining pathways between two conformational states, and analyzing the lowest-frequency motions of proteins.\n\nUnfortunately, NMA requires the diagonalization of a large 3N x 3N matrix.  As diagonalization requires O(N^3) operations, it is not feasible to analyze medium to large proteins with NMA using full diagonalization.\n\nThe Flexible Block Method (FBM) is an approximate diagonalization method for performing NMA of proteins.  FBM is much faster than the standard method of performing a full diagonalization, yet is able to recover the slow modes with reasonable accuracy.\n\nThe OpenMMFBM project develops an implementation of FBM for GPUs using OpenMM, common CPU math libraries, and custom GPU kernels.  This particular implementation is not only fast, but it's easy to use as it does not require the, often messy, implementation of analytical equations of the Hessians.\n\nWe hope you'll give it a try and tell us what you think!","has_downloads":false,"keywords":"coarse-grained molecule modeling,Normal modes","ontologies":"Molecular_Dynamics","projMembers":"Jesus Izaguirre,James Sweet,Trevor Cickovski,Chris Sweet,RJ Nowling","trove_cats":[{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"797","unix_group_name":"kalmanforik","modified":"1605285461","downloads":"914","group_name":"Kalman Smoothing Algorithm for Inverse Kinematics","logo_file":"","short_description":"This project provides a Kalman smoothing algorithm for inverse kinematics in OpenSim.","long_description":"This project is a Kalman smoothing algorithm for inverse kinematics. Kalman smoothing improves the estimation of joint kinematics and kinetics in marker-based human gait analysis. Kalman smoothing is an alternative for the global optimization algorithm implemented in the Inverse Kinematics Tool distributed with the OpenSim installation and requires the same input and setup files. Optionally, additional parameters describing the measurement noise and the smoothness of the motion can be specified in the setup file. The algorithm is described in detail in De Groote et al. (2008).\n\nThe implementation of the algorithm is on GitHub (https://github.com/antoinefalisse/opensim-core/tree/kalman_smoother) and the latest release is based on a fork from opensim-core created in between the OpenSim 4.1 and 4.2 releases. We will try to update the algorithm with upcoming OpenSim versions.\n\nPlease read the README files in KS-libraries and KS-example to find out more about how to use the tool.\n\nPlease contact us for any questions: friedl.degroote@kuleuven.be | afalisse@stanford.edu. Please report any issues or suggestions on the GitHub page.","has_downloads":true,"keywords":"inverse kinematics","ontologies":"","projMembers":"Friedl De Groote,Antoine Falisse,Wouter Aerts","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":true},{"group_id":"799","unix_group_name":"map","modified":"1553926173","downloads":"0","group_name":"The Musculoskeletal Atlas Project","logo_file":"map","short_description":"Provides an easy-to-use application for visualising imaging data, segmenting images, creating geometric meshes, and exporting OpenSim and FE models.","long_description":"We have built an open-source software framework called the Musculoskeletal Atlas Project (MAP) for creating musculoskeletal models.  The software is built with a Python plug-in architecture, to enable quick and easy development from the community. The client-side application (MAP Client) facilitates dicom and motion capture integration, registration tools, and meshing capabilities. The MAP database stores meshes from a larger population of medical imaging data, known as the Melbourne Femur Collection, which consists of 320 full body CT scans. 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More specifically, I have been working a) on a protocol for the automated and reliable setup of molecular dynamics (MD) simulations, b) on rapid ways of analyzing the vast amount of data that is generated in the course of such simulations, c) on applying both (a) and (b) towards the analysis and the redesign of a prenyltransferase (in collaboration with Prof. Chaitan Khosla at Stanford), and d) on the high-throughput screening and evaluation of computationally designed enzymes, binders, and structural proteins (in collaboration with Prof. David Baker at UW).","has_downloads":false,"keywords":"protein engineering,non-natural reactions,active site design,biomolecular catalysis","ontologies":"Molecular_Dynamics,Molecular_Interaction,Computational_Model,Protein_Interaction_Modeling,Molecular_Model","projMembers":"Gert Kiss","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":false},{"group_id":"845","unix_group_name":"cellpressure","modified":"1362430555","downloads":"72","group_name":"Osmotic pressure, bacterial growth, and bacterial division.","logo_file":"cellpressure","short_description":"To study the effect of osmotic forces on bacteria, we apply osmotic shock to a variety of organisms and monitor the effect on cell growth and division.","long_description":"This project aims to test the hypothesis that bacteria exploit osmotic pressure as a physical driving force for growth and division.","has_downloads":true,"keywords":"cell mechanics","ontologies":"Cell_Model","projMembers":"Enrique  Rojas","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":true},{"group_id":"846","unix_group_name":"livecellnfkb","modified":"1406819769","downloads":"799","group_name":"Live Cell NF-κB","logo_file":"","short_description":"This project provides data and visualization tools to explore single-cell NF-κB dynamics","long_description":"This project provides data and visualization tools to explore single-cell NF-κB dynamics.  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The contents are:\n\n1) OpenMM and TINKER input files for six water boxes of progressively increasing size (216 through 288,000 molecules) and DHFR in explicit water from the Joint AMBER-CHARMM benchmark.  \n\n2) Script for automatically running the benchmark across different methods (i.e. force field and run parameters), systems, and platforms.\n\n3) Output data from running the benchmark on a high performance compute node (CPU: 2x Intel Xeon E5-2643 3.30GHz, GPU: NVidia GeForce GTX Titan Black).\n\n4) Instruction manual.\n\nThe simulations are NVT (298.15 K), 1 and 2 fs time steps (2 fs time step uses MTS algorithm), PME electrostatics with real space cutoff 7.0 Angstrom, vdW cutoff 9.0 Angstrom.  Simulation speed is given in ns/day.","has_downloads":true,"keywords":"AMOEBA,OpenMM","ontologies":"","projMembers":"Lee-Ping Wang","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"854","unix_group_name":"children","modified":"1602063186","downloads":"78","group_name":"A generic Juvenile Model based on 5 MRI Data Sets","logo_file":"children","short_description":"Musculo-skeletal model for the lower limbs of children based on MRI-data","long_description":"The model generated for this project is a generic model for children. 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It has a muscular structure from young humans and not, as in most template models a muscle architecture that is based on the examination of cadavers of aged population.\nThe muscle parameters such as optimal muscle fiber length and tendon slack length have been set in a way that the model has similar optimal joint angles as the template models provides with the OpenSim installation.","has_downloads":true,"keywords":"image-based geometric modeling,Children,muscle architecture parameters,lower limbs","ontologies":"Aggregate_Human_Data,Computational_Model","projMembers":"Reinhard Hainisch","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"855","unix_group_name":"breeder","modified":"1613692800","downloads":"5","group_name":"Breeder : genetic algorithm based protein-protein interface optimizer","logo_file":"","short_description":"This algorithm will be used to optimize proteins which will be used as therapy or diagnosis.","long_description":"This algorithm submits many MMB equilibration and FoldX energy evaluation jobs to a cluster and finds sequences with improved binding energy.","has_downloads":true,"keywords":"knowledge based potential,internal coordinate mechanics,biologic","ontologies":"Structural_Model,Antibody_Production","projMembers":"Samuel Flores,Olle Nordesjö,Yavor Kovachev,Daniel  Dourado,joel as,Andrei Rajkovic","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"856","unix_group_name":"posturefeedback","modified":"1457549243","downloads":"0","group_name":"Sensory Components for Simulating Postural Feedback Control in OpenSim","logo_file":"posturefeedback","short_description":"This project proposes to develop new model components in OpenSim that create physiologically based sensory signals during dynamic simulations. 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This was done by using tools in SimBody and OpenSim to create a dynamic optimization routine. This project can be used as an example to create other dynamic optimizations.","has_downloads":true,"keywords":"musculoskeletal simulation,Optimization","ontologies":"Neuromuscular_Model","projMembers":"Carmichael Ong,Matthew Titchenal","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"862","unix_group_name":"calfshortening","modified":"1370532955","downloads":"36","group_name":"How Robust is Human Gait to Calf Muscle Shortening?","logo_file":"","short_description":"Provides the models and simulation results for testing the sensitivity of gait to progressive calf muscle shortening.","long_description":"This is a sensitivity study examining the robustness of human gait to progressive contracture of the gastrocnemius and soleus muscles.  Study results can be used to understand what kinds and degrees of contracture likely lead to equinus, or tip toe walking, which is a characteristic feature of gait in Cerebral Palsy patients.\n\nhttp://www.youtube.com/watch?v=dSl9W6m16-4","has_downloads":true,"keywords":"musculoskeletal biomechanics,Gait analysis,OpenSim,Cerebral palsy","ontologies":"Neuromuscular_Model","projMembers":"Thomas Uchida,Katrina Wisdom","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"863","unix_group_name":"fatigablemuscle","modified":"1380233837","downloads":"454","group_name":"Developing a fatigable muscle model","logo_file":"","short_description":"Provides the code to implement a fatigable muscle model.","long_description":"This project is about developing a muscle model in OpenSim that demonstrates properties of muscle fatigue. This muscle model will also take into account fiber composition and some considerations for orderly recruitment of muscle fibers.  This model is still a work in progress and was developed as part of 4-week project for a class on the modeling and simulation of human movement.  A more detailed description and documentation of the model can be found on the OpenSim support page at http://simtk-confluence.stanford.edu:8080/display/OpenSim/Design+of+a+Fatigable+Muscle","has_downloads":true,"keywords":"fatigue,muscle modeling,muscle","ontologies":"Dynamic_Model,Neuromuscular_Model","projMembers":"Apoorva Rajagopal,Jennifer Yong","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"864","unix_group_name":"fatigablerunner","modified":"1370579547","downloads":"78","group_name":"The Fatigable Runner","logo_file":"","short_description":"Provide a platform to test different running styles to examine the effects of fatigue","long_description":"There has been an ongoing debate for a long time as to which running form is the optimal for performance for endurance, long distance running. Numerous personal accounts for improvement in performance has been reported by everyday athletes all the way to world champion triathletes describing a number of modified components to their running form that have helped in making them run faster and more efficiently. But none of which has really been well documented scientifically. Though this project is only a tiny step toward analyzing differences in running forms with preliminary data, it provides a platform for future biomechanists to examine the key differences in running forms of different individuals.\n\nThis is an extremely difficult question to answer since there is almost no way to experimentally test the differences between running forms. This is because it is impossible to have adequate controls. Every runner is different in height, weight, fitness level, muscular strength, etc. Every runner also runs differently.  It’s not possible for one runner to replicate many different styles in an experiment as every runner has his/her own natural style. However, if different running motion kinematics can be replicated with one model of runner, the differences may be well observed.\n\nUtilizing opensim software, simulations can be performed using the same runner and replicating different running styles. Computed muscle controls also allows muscle activations to be calculated and compared. An additional feature is the adding custom muscle model that fatigues to mimic an endurance running event. Since there has been no model with fatigable muscle implemented before, there are a lot of questions that can only be answered through simulation with fatigable muscles. Differences in running form may be analyzed without considering muscle fatigue, but inducing muscle fatigue may provide more insight.","has_downloads":true,"keywords":"running,fatigue","ontologies":"","projMembers":"Aaron Wayne,Yu Hsiao","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":false},{"group_id":"865","unix_group_name":"flippin-felines","modified":"1381786134","downloads":"189","group_name":"Flippin' Felines: Controlling a Cat Model to Land on Its Feet","logo_file":"flippin-felines","short_description":"(1) Controlling cat models to land on their feet (2) Provide a framework for learning and teaching OpenSim, including an instructional tutorial","long_description":"Cats are known for an uncanny ability to land on their feet. This project explores this phenomenon, known as the \"cat-righting reflex\". It was completed for ME/BIOE 485: Modeling and Simulation of Human Movement, a course at Stanford University.\n\nFrom a research perspective, we aim to answer two questions: (1) When falling upside down, what control strategies does a cat use to flip itself over and land on its feet? (2) What is the minimum model of a cat that will flip in a biologically realistic manner? For example, can the cat flip without being able to twist its spine? We begin to answer these questions using dynamic optimization and 'step-wise' model creation. The modeling and optimization source code is available for download.\n\nIn addition to these basic research questions, we believe that the cat-flipping problem provides an ideal framework for learning and teaching OpenSim. For this reason, we have compiled simplified versions of our modeling and optimization code. While this code is also available for download here, it is laid out in the form of an instructional tutorial on our <a href=\"http://simtk-confluence.stanford.edu:8080/display/OpenSim/Flippin%27+Felines%3A+Controlling+a+Cat+Model+to+Land+on+its+Feet\">Confluence webpage</a>, which also contains a more detailed description of the project and our results.\n\nHere is a short video overview of the project:\n\n<object width=\"560\" height=\"315\"><param name=\"movie\" value=\"//www.youtube.com/v/u7wtqWyLdKA?hl=en_US&version=3&rel=0\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\"//www.youtube.com/v/u7wtqWyLdKA?hl=en_US&version=3&rel=0\" type=\"application/x-shockwave-flash\" width=\"560\" height=\"315\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>","has_downloads":true,"keywords":"dynamic optimization,cat,modeling,reflex","ontologies":"Controllers,Multibody_Dynamics","projMembers":"Christopher Dembia,Sean Sketch","trove_cats":[{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":false},{"group_id":"866","unix_group_name":"exosuit","modified":"1381786639","downloads":"6939","group_name":"Modeling, Evaluation, and Control Optimization of Exosuit with OpenSim","logo_file":"exosuit","short_description":"- Gives an example of how to use OpenSim as an analysis tool of wearable device\n- Provide example simulation models that is easy to use and modify","long_description":"This project tackles the challenges of developing wearable device using OpenSim simulation. Simulation can help developing wearable device as it can give an intuition on how wearable devices interact with human and how muscle activations change when a subject wear the device. We can also find the key features that one should account for when designing wearable device in order to make it efficient.\n\nIn this project, I focused on specific wearable device, called Harvard Exosuit. BioDesign group at Harvard University is developing this suit, and the main idea is to create a soft and deformable under-suit which can assists loaded walking.\n\nMy accomplishments through this project are\n\n- Evaluate the effectiveness of wearing active actuators on metabolic cost reduction.\n- Explain how Exosuit can help loaded gait\n- Verify the impact of changes of design parameters\n- Find optimal control inputs for active actuators \n\nHere is a video clip for project overview:\n\n<object width=\"560\" height=\"315\"><param name=\"movie\" value=\"//www.youtube.com/v/AChL_93DUCw?hl=en_US&version=3&rel=0\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\"//www.youtube.com/v/AChL_93DUCw?hl=en_US&version=3&rel=0\" type=\"application/x-shockwave-flash\" width=\"560\" height=\"315\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>\n\nIf you want more details of this project, please visit the project confluence webpage: <a href=\"http://simtk-confluence.stanford.edu:8080/display/OpenSim/Modeling%2C+Evaluation%2C+and+Control+Optimization+of+Exosuit+with+OpenSim\"> Confluence page</a>","has_downloads":true,"keywords":"Loaded gait,Exosuit,External actuation,Metabolic cost,Wearable device","ontologies":"","projMembers":"Jaehyun Bae","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"867","unix_group_name":"iaa_controller","modified":"1370591572","downloads":"439","group_name":"Induced Accelerations-based controller for balance","logo_file":"iaa_controller","short_description":"We attempt to use an Induced Accelerations Analysis of a biomechanical model to control position and maintain balance.","long_description":"We attempt to use an Induced Accelerations Analysis of a biomechanical model to control position and maintain balance.","has_downloads":true,"keywords":"balance control","ontologies":"Controllers","projMembers":"Mishel Johns,Christopher Ploch","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":false},{"group_id":"868","unix_group_name":"nmlmuscle","modified":"1537400506","downloads":"30","group_name":"Physiologically inspired muscle models","logo_file":"","short_description":"Development and validation of new muscle models for use in musculoskeletal modelling","long_description":"The purpose of this study is to implement current physiological findings about mechanical properties of whole muscles into the OpenSim platform, and to validate the performance of existing and enhanced muscle models. Recent testing and validation of the muscle models with experimental work has shown that muscle models with independent fast- and slow-contracting elements can predict muscle force better and more accurately than previous Hill-type models with only a single contractile element. We have developed a plug-in muscle model to achieve this property in OpenSim, supported by an OpenSim pilot project grant. Recent testing shows that the new plug-in module generates the expected changes in contractile performance when the proportion of fast- to slow- activation is changed. We are currently collecting experimental data to test the validity of the constant-thickness assumption of the existing muscle model, and propose to incorporate more structurally relevant properties if they improve the accuracy of the muscle model.\n\n\nPublication overview\nWakeling, J.M., Lee S.S.M., Arnold A.S., de Boef Miara, M., & Biewener, A.A.. A muscle's force depends on the recruitment patterns of its fibres. Ann. Biomed. Eng. 40, 1708-1720, (2012).\nWakeling, J.M. & Randhawa, A. 1D, 2D and 3D structural models for Hill-type muscle models. Computer Methods in Biomechanics and Biomedical Engineering (11th Int. Symposium). Salt Lake City, UT, (2013).\nLee, S.S.M., deBoef Miara, M, Arnold, A.S., Biewener, A.A., Wakeling, J.M. “Hill-type muscle model with slow and fast fiber contractile elements”. Annual Meeting of the American Society of Biomechanics, Omaha, NB, (2013).\nLee, S.S.M., deBoef Miara, M, Arnold, A.S., Biewener, A.A., Wakeling, J.M. “A two-element Hill-type model to predict muscle forces”. Society of Comparative and Integrative Biology Annual Meeting, San Francisco, CA, (2013).\nLee, S.S.M., de Boef Miara, M., Arnold, A.S., Biewener, A.A., Wakeling, J.M. Accuracy of gastrocenmius forces in walking and running goats predicted by one element and two-element Hill-type models. J. Biomech. In press.","has_downloads":true,"keywords":"Forward Dynamic Simulation,muscle modeling,Musculoskeletal Model","ontologies":"Neuromuscular_Model","projMembers":"Allison Arnold,Sabrina Lee,Taylor Dick,Adrian Lai,david lu,James  Wakeling","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"871","unix_group_name":"sweetlead","modified":"1383955436","downloads":"1369","group_name":"SWEETLEAD: A cheminformatics database of medicines, drugs, and herbal isolates.","logo_file":"sweetlead","short_description":"Individual chemical databases sometimes contain incorrect structural information about drugs.  We seek to address this problem by providing accurate structures for use in drug discovery efforts and cheminformatics analysis.","long_description":"The SWEETLEAD database has been created to provide an exhaustive and highly curated resource for chemical structures of the world's approved medicines, illegal drugs, and isolates from traditional medicinal herbs.  This database has been built using a consensus generating scheme pulling data from several public chemical databases (such as PubChem, ChemSpider, PharmGKB, etc.), as detailed in the publication.","has_downloads":true,"keywords":"repurposing,cheminformatics,computer-aided drug discovery","ontologies":"Delimited_Table,Database","projMembers":"Vijay Pande,Paul Novick","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"872","unix_group_name":"ncsu_exo_hop","modified":"1374129125","downloads":"0","group_name":"Musculoskeletal Modelling of Hopping in Elastic Ankle Exoskeletons","logo_file":"ncsu_exo_hop","short_description":"Understand how ankle exoskeletons affect ankle muscle mechanics and energy consumption","long_description":"Assistive exoskeletons have the potential to aid locomotor recovery and restore walking function in neuromuscularly and musculoskeletally impaired individuals (e.g. post-stroke or spinal cord injury). They also may be used to augment locomotor performance in healthy individuals by reducing the metabolic cost of locomotion or reducing skeletal loading. Whilst these devices have been shown to be effective in replacing joint moments and powers, little is known about how they influence the underlying muscle function. I have collected experimental data on humans examining how ankle exoskeletons affect Soleus muscle function. The purpose of this proposed work is to: (1) Develop an OpenSim-based model of hopping with spring-loaded ankle exoskeletons; (2) Verify that model with the experimental data; and (3) Use the model to examine the mechanics of other muscle groups within the leg.","has_downloads":false,"keywords":"Metabolic cost,exoskeleton,Muscle Function","ontologies":"Neuromuscular_Model","projMembers":"Dominic Farris","trove_cats":[{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"873","unix_group_name":"rhm3dwalk","modified":"1374866748","downloads":"0","group_name":"3D model for simulating the mechanics and energetics of human walking","logo_file":"","short_description":"Provide a code for customizing, performing, and optimizing forward dynamics simulations of human locomotion.","long_description":"This project provides a Fortran code for simulating the mechanics and energetics of human walking in three dimensions.  The model has 23 degrees of freedom (pelvis, trunk, thighs, shanks, feet, toes) and 40 Hill-based muscle model actuators.  The muscle excitation control scheme is customizable.  Implementations of several popular muscle energetics models are provided.  Experimental joint motion and GRF data are provided for performing tracking simulations.  The model can also perform predictive simulations (no tracking) through customization of the cost function.","has_downloads":false,"keywords":"Hill model,Forward dynamics,3D,Musculoskeletal model","ontologies":"","projMembers":"Ross Miller","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"879","unix_group_name":"spine_mechanic","modified":"1375899330","downloads":"0","group_name":"Spine Movement","logo_file":"","short_description":"visualize 3D movement of spine","long_description":"Generate Euler Angles and joint forces of vertebral bodies","has_downloads":false,"keywords":"Lumbar Spine","ontologies":"","projMembers":"Linda McGrady","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"882","unix_group_name":"flexfbatfba","modified":"1387680600","downloads":"73","group_name":"Flexible (flexFBA) and time-linked (tFBA) Flux Balance Analysis methods","logo_file":"","short_description":"Reproduce publication results for methods flexible and time-linked Flux Balance Analysis (flexFBA and tFBA). Additionally a minimal flexFBA example using Matlab Cobra Toolbox formatted models.","long_description":"(provided for computational biologists to reproduce publication results, and a small utility written for example use with the Cobra toolbox) The associated publication describes two complimentary methods that remove the inherent long-time assumptions of the biomass reaction used in FBA. Implementing a flexible objective flexFBA, enables a metabolic network to produce biological process reactants independently from one another. This flexibility is in contrast to the rigid proportion held by the traditional biomass reaction of FBA.  Also, time-linked simulation (tFBA) can represent transitions between metabolic steady states by returning cell process byproducts at subsequent time-steps.","has_downloads":true,"keywords":"flux-balance analysis","ontologies":"","projMembers":"Elsa Birch","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"886","unix_group_name":"prob_tool","modified":"1417553036","downloads":"232","group_name":"Probabilistic Tool for Considering Patient Populations & Model Uncertainty","logo_file":"prob_tool","short_description":"Will provide a probabilistic tool to assess model parameter uncertainty and intersubject variability.","long_description":"The goal of this project is to develop a generalized, probabilistic plugin for OpenSim and to demonstrate subject-specific and population-based applications of this tool. The tool will implement two probabilistic methods (Monte Carlo and advanced mean value) and provide a user-friendly interface to create analyses and visualize results. The probabilistic tool will quantify 5 to 95% confidence bounds for output measures and sensitivity factors, which are used to identify the most important input parameters that contribute to output variability. A subject-specific model will be used to account for measurement errors associated with motion capture and input parameter uncertainties. The code is currently written in Matlab but future releases and additions will explore other applications.","has_downloads":true,"keywords":"uncertainty,population,Variability,hip,gait,probabilistic","ontologies":"","projMembers":"Bradley Davidson,Kevin Shelburne,Peter Laz,Casey Myers","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"888","unix_group_name":"kneemodel","modified":"1472675369","downloads":"1073","group_name":"Discrete Element Knee","logo_file":"kneemodel","short_description":"The purpose of this project is to provide an open source, discrete element knee model.","long_description":"This project implements a discrete element knee model in OpenSim. Specifically, the model is a six degree-of-freedom tibiofemoral and one degree-of-freedom patellofemoral joint.  It includes eighteen ligament bundles and tibiofemoral contact and was validated against cadaveric data.\n\nPublications:\n\nSchmitz, A., Piovesan, D. (2015) Development of an Open-Source, Discrete Element Knee Model. IEEE Transactions on Biomedical Engineering Special Issue on Modeling. In press.\n\nSchmitz, A. (2015) Development of an Open-Source, Discrete Element Knee Model. (poster presentation the 39th Annual Meeting of the American Society of Biomechanics). Columbus, OH: ASB.","has_downloads":true,"keywords":"OpenSim,computational model,discrete element model,knee,open-source","ontologies":"","projMembers":"Anne Schmitz","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"891","unix_group_name":"mcbs","modified":"1561990725","downloads":"73","group_name":"Multidimensional Cubic B-Spline","logo_file":"mcbs","short_description":"Provide a software for fast estimation of lengths and three-dimensional moment arms for musculotendon actuators.","long_description":"This project presents a C++ implementation of multidimensional cubic B-spline for the fast estimation of lengths and three-dimensional moment arms for musculotendon actuators.\nAt first, an OpenSim musculoskeletal model is used to establish the length of each musculotendon actuator for different generalized coordinates (joint angles).  Then,   multidimensional spline function is computed to fit the previous data. Muscle moment arms are obtained by differentiating the musculotendon length spline function with respect to the generalized coordinate of interest.","has_downloads":true,"keywords":"Muscle-tendon length,muscle moment arms,musculoskeletal simulation","ontologies":"Modeling_and_Simulation,Neuromuscular_Model","projMembers":"Monica Reggiani,Massimo Sartori","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"892","unix_group_name":"strideinpatient","modified":"1506462524","downloads":"10","group_name":"Electronic Medical Record Data-Mining","logo_file":"","short_description":"Code and scripts to support mining EMR data from Stanford STRIDE","long_description":"EMR data-mining code such as association rules for order recommendations and outcome predictions and order set evaluation","has_downloads":true,"keywords":"EMR","ontologies":"","projMembers":"Jonathan Chen","trove_cats":[{"id":"400","fullname":"Data Sets"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"896","unix_group_name":"drugfeature","modified":"1504823903","downloads":"67","group_name":"Identifying druggable targets by protein microenvironments matching","logo_file":"","short_description":"The druggability of a target protein is its potential to be modulated by small, drug-like molecules. Druggability is an important criterion in the target selection phase of drug discovery.  However, an effective standard for evaluating target druggability","long_description":"DrugFEATURE is a computational method that evaluates target druggability by assessing the protein microenvironments in potential small molecule binding sites.","has_downloads":true,"keywords":"drug discovery","ontologies":"","projMembers":"Russ Altman,Tianyun Liu","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"897","unix_group_name":"showmotion","modified":"1380646348","downloads":"0","group_name":"S-HOW Motion\nMovement Characterization based on wearable technology","logo_file":"showmotion","short_description":"1) Provide a quick reference for researchers regarding complex motions and scenarios\n\n2) Enhance research, going from the laboratory settings data to the on-the-field data available","long_description":"Wearable technology allows to capture data on the field, allowing new kinematics capture scenarios: interacting with the subject (for example by mobilization), running outside, going upstairs/downstairs, working at the desk, pitching on the field, walking along tens of meters.\n\nThis project will provide little by little new sets of motion data, initially mainly kinematics, which can be useful in research for several reasons:\n\n1) explore kinematics of complex movements\n\n2) explore new simulation opportunities\n\n3) concentrate your time (not your attention of course) more on simulation and data interpretation rather than capturing kinematics\n\n4) enhance your available data sets for running Opensim simulation\n\n5) discuss and share ideas around new motions to be captured, new scenarios where you need simulation\n\nData will be mainly provided applying the measurement protocols called ISEO and OUTWALK published on Medical & Biological Engineering & Computing in 2008 and 2010.\n\n[1]A. G. Cutti, A. Giovanardi, L. Rocchi, A. Davalli, and R. Sacchetti, “Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors,” Med Biol Eng Comput, vol. 46, no. 2, pp. 169–178, Feb. 2008.\n\n\n[2]A. Cutti, A. Ferrari, P. Garofalo, M. Raggi, A. Cappello, and A. Ferrari, “‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors,” Med Biol Eng Comput, Nov. 2009.","has_downloads":false,"keywords":"Gait analysis,gait,Musculoskeletal model,motion capture,Motion Analysis,human motion characterization,walking,kinematics,Trunk,Running,shoulder","ontologies":"Data_Visualization,Individual_Human_Data,Knowledge_Extraction,Data_Resource,Graph_Analysis,Data_Exploration","projMembers":"PIETRO GAROFALO","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"898","unix_group_name":"dmpsynergies","modified":"1381737788","downloads":"1466","group_name":"Interaction Between Biology and Robotics","logo_file":"dmpsynergies","short_description":"Provides a musculoskeletal arm model with eleven muscles and an easy to use Matlab interface to run forward dynamics simulations.","long_description":"In this project we study a salient feature of human motor skill learning that is the ability to exploit similarities across related tasks. We test this concept in forward dynamics simulations of multiple reaching movements of a musculoskeletal arm model with eleven muscles. The eleven muscle activation patterns are generated by a combination of parametrized muscle synergies, where these synergies encode a shared prior among multiple reaching directions.","has_downloads":true,"keywords":"dynamic movement primitives,muscle synergies,motor control,reinforcement learning,musculoskeletal model","ontologies":"","projMembers":"Elmar Rückert","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":false},{"group_id":"899","unix_group_name":"arm_swing","modified":"1401822375","downloads":"922","group_name":"Analysis of arm swing during human walking","logo_file":"arm_swing","short_description":"Provides a simplified total body model for simulation of musculoskeletal movements.","long_description":"This project provides a simplified version of the UpperLowerBodySimple model from the ULB-project, which was adjusted with the purpose to decrease the run time of the simulations. \nThe adjusted model was used to determine arm swing kinematics (with and without muscle excitations) during human walking, with arm movements not exceeding 70 degrees of anteflexion or abduction. However, the adjusted_ULB model can be used for modeling and simulating kinematics and kinetics of all neuromusculoskeletal systems.\nFor an example of an arm swing simulation without muscle excitation we refer to the video below.\n\n<object width=\"420\" height=\"315\"><param name=\"movie\" value=\"//www.youtube.com/v/2C_SCeQS4ks?hl=en_US&version=3\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\"//www.youtube.com/v/2C_SCeQS4ks?hl=en_US&version=3\" type=\"application/x-shockwave-flash\" width=\"420\" height=\"315\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>","has_downloads":true,"keywords":"muscle-driven simulation,musculoskeletal biomechanics,musculoskeletal model,musculoskeletal simulation","ontologies":"","projMembers":"Ilse Jonkers,Jaap van Dieen,Marije Goudriaan,Sjoerd Bruijn","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"900","unix_group_name":"chimphindlimb","modified":"1669739988","downloads":"191","group_name":"A 3-D Musculoskeletal Model of the Chimpanzee Pelvis and Hind Limb","logo_file":"chimphindlimb","short_description":"This project provides a musculoskeletal model for estimating the force- and moment-generating capacity of the major pelvis and hind limb muscles in the chimpanzee. Results from related studies employing the chimpanzee model are also curated here.","long_description":"The following video includes a description of the chimpanzee musculoskeletal model. The detailed description of the development and validation of the model begins at around 22:00 minutes.\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/y_jCNqW4MDI&quot; frameborder=&quot;0&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThe primary resource available on this project page is a three dimensional musculoskeletal model of the chimpanzee pelvis and hind limb. The bone elements were created by scanning the skeleton of a chimpanzee obtained on loan from the America Museum of Natural History. Muscle attachment sites and pathways were based on published muscle maps, as well as dissections performed by the authors. Muscle architectural parameters were based on dissections reported in the literature. See the associated publication for more detail. The development of this model and related research were supported by grants from the U.S. National Science Foundation (awards BCS 0935327 and 0935321).","has_downloads":true,"keywords":"muscle force prediction,lower limbs,musculoskeletal model,muscle moment arms","ontologies":"Neuromuscular_Model","projMembers":"Brian Umberger,Russell Johnson,Leng-Feng Lee,Matthew ONeill","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public 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for a jumping Luxo Lamp, inspired by Witkin and Kass (1988).","has_downloads":false,"keywords":"optimal control,computational biomechanics,Human Movement","ontologies":"Computational_Model,Modeling_and_Simulation,Controllers,Optimizer","projMembers":"Karianne Bergen","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"906","unix_group_name":"sv_thirdparty","modified":"1693835839","downloads":"0","group_name":"SimVascular: Third-party open source software.","logo_file":"","short_description":"Third party open source software needed by the new SimVascular project.","long_description":"Third party open source software needed by internal developers of the new SimVascular 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The data is available for download and can be used freely by anyone.","has_downloads":true,"keywords":"Molecular Dynamics Trajectories,Publication data link","ontologies":"","projMembers":"Kai Kohlhoff","trove_cats":[{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"909","unix_group_name":"frag-feature","modified":"1703168966","downloads":"185","group_name":"FragFEATURE:  Knowledge-based Fragment Binding Prediction","logo_file":"frag-feature","short_description":"Provide a knowledge-based fragment binding predictor as well as the underlying knowledge base.","long_description":"This project is a data-driven approach to predicting small molecule fragments that will bind a protein structure of interest. There are two parts to the project: (1) the knowledge base contains all known protein-fragment interactions from the Protein Data Bank (2) the method uses the knowledge base to make fragment predictions given an input structure.  Each fragment prediction yielded by the method is accompanied by all supporting pieces of information from the knowledge base to facilitate user interpretation of the results.","has_downloads":true,"keywords":"protein-ligand binding,computer-aided drug discovery,structural bioinformatics,protein microenvironments,protein 3d structure,drug discovery","ontologies":"Database,Molecular_Interaction,Protein_Interaction_Modeling,Data_Mining_and_Inference","projMembers":"Russ Altman,Joy Ku,Grace Tang,Alp TARTICI","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"910","unix_group_name":"networkpainter","modified":"1432580229","downloads":"0","group_name":"NetworkPainter: Biological pathway animation","logo_file":"networkpainter","short_description":"Provides a user-friendly interface to draw intracellular pathway diagrams and animate them with high-dimensional experimental data.","long_description":"NetworkPainter is a web-based program for drawing and painting signaling network diagrams with high-dimensional cytometry data. Two versions of NetworkPainter are available. The <a href=\"http://covert.stanford.edu/networkpainter\">NetworkPainter stand-alone version</a> is capable of visualizing any uploaded cytometry data. NetworkPainter is also available through the <a href=\"http://www.cytobank.org/networkpainter.html\">Cytobank</a> flow cytometry repository. This version is capable of analyzing flow and mass cytometry data stored in Cytobank.","has_downloads":false,"keywords":"pathway,animation,cytometry,diagram,visualization,systems biology","ontologies":"","projMembers":"Jonathan Karr,Markus Covert","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"912","unix_group_name":"lamb_headneck","modified":"1390350895","downloads":"33","group_name":"Head kinematics of an animal model during abusive head trauma","logo_file":"lamb_headneck","short_description":"Provides model to simulate head kinematics of experimental model during abusive head trauma","long_description":"The lamb was used as an in vivo experimental analogue of abusive head trauma (AHT). An OpenSim computational model of the lamb was developed and used to interpret biomechanical data from shaking experiments. Sagittal plane acceleration components of the animal’s head during shaking were used to provide in vivo validation of the model. Results demonstrated that peak accelerations occurred when the head impacted the torso and produced acceleration magnitudes exceeding 200 m∙s-2. The computational model demonstrated good agreement with the experimental measurements and was able to reproduce the extreme accelerations that occur during impact. 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The infant model was combined with an upper body adult model body (Holzbaur et al., 2005; Steele et al., 2013) to provide realistic shaking boundary constraints.  Details of the upper body adult model can be found at https://simtk.org/home/arm2013. 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MIST runs over the cloud!","long_description":"The MIcro Simulation Tool is free software that allows the user to :\n* Define state transition models with multiple processes\n* Generate populations to match given statistics using Evolutionary Computation\n* Run Simulations in High Performance Computing environment - including the cloud\n* Analyze and Report the results\n\nBelow are some videos that describe MIST capabilities:\n\nThis video shows the basic ideas behind MIST\n&lt;iframe width=&quot;640&quot; height=&quot;360&quot; src=&quot;https://www.youtube.com/embed/AD896WakR94&quot; frameborder=&quot;0&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video shows some population generation capabilities using Evolutionary Computation:\n&lt;iframe width=&quot;640&quot; height=&quot;360&quot; src=&quot;https://www.youtube.com/embed/PPpmUq8ueiY&quot; frameborder=&quot;0&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video will show how MIST runs over the cloud:\n&lt;iframe width=&quot;640&quot; height=&quot;360&quot; src=&quot;https://www.youtube.com/embed/wpfw8POx-wI&quot; frameborder=&quot;0&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video will show how MIST modeled COVID19 in &lt;a href=&quot;https://devpost.com/software/improved-disease-modeling-tools-for-populations&quot;&gt;Pandemic Response Hackathon&lt;/a&gt;\n&lt;iframe width=&quot;640&quot; height=&quot;360&quot; src=&quot;https://www.youtube.com/embed/0ElKy0Ysz3I&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThe Reference Model is one examples of use of MIST. 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For later versions with enhanced capabilities, please contact the developer.\n","has_downloads":true,"keywords":"Python,cloud,GUI,high-performance,micro simulation","ontologies":"Computational_Model,Compiler,Monte_Carlo_Simulation","projMembers":"Jacob Barhak","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"958","unix_group_name":"mobilizeplans","modified":"1711751954","downloads":"0","group_name":"Mobilize Center Planning","logo_file":"","short_description":"Enables coordination, collaboration, and planning for the Mobilize Center","long_description":"Enables coordination, collaboration, and planning for the Mobilize Center","has_downloads":false,"keywords":"Wearable device,wearable sensors,big data,mobility,biomechanics","ontologies":"","projMembers":"Scott Delp,Jennifer Hicks,Joy Ku,Diane Bush,Jacqueline Tran","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"959","unix_group_name":"spine_ribcage","modified":"1625001959","downloads":"2505","group_name":"Thoracolumbar spine and rib cage model in OpenSim","logo_file":"spine_ribcage","short_description":"The aim of this project is to provide detailed and well-validated musculoskeletal models of the thoracolumbar spine and rib cage, particularly for investigators with an interest studying musculoskeletal loading in the trunk. ","long_description":"These models include a fully articulated thoracolumbar spine (T1 through L5), with 3 rotational degrees-of-freedom at each inter-vertebral joint, and ribcage (24 individual ribs and a sternum). They also include a lumped head and neck body, and upper extremities. Major lumbar spine and abdominal muscle groups were incorporated from the previously developed OpenSim lumbar spine model (https://simtk.org/projects/lumbarspine), and muscles attaching to the upper and mid-thorax were adapted from the neck musculoskeletal model (https://simtk.org/projects/neck_mechanics) and upper extremity model (https://simtk.org/projects/up-ext-model).  Additional muscle groups were added, including the external and internal inter-costals, the thoracic multifidus, the lower portion of the trapezius, the serratus anterior, and the transversus abdominis. Muscle measurements from in vivo computed tomography scans of the trunk were used to adjust the cross-sectional area (CSA) and position of the trunk muscles in the model to match a community-based sample of 125 men (generic male models) or 125 women (generic female models). The most recent releases include generic male and female models trunk models (v1.1; OpenSim 3.3 compatible), and generic male and female full body models (v2.0; OpenSim 4.x compatible) that incorporate lower extremities from the Gait2354 model (https://simtk-confluence.stanford.edu/display/OpenSim/Gait+2392+and+2354+Models). In total the trunk models have 93 degrees-of-freedom, and 552 musculotendon actuators; full body models have 111 degrees of freedom and 620 musculotendon actuators. \nThe models are validated for estimations of spinal loading and trunk muscle tension against previously collected in-vivo measurement of intradiscal pressure, vertebral compression from telemeterized implants and trunk muscle EMG. The models were created and validated in OpenSim and may be used in Static Optimization analyses; we have not verified use in Computed Muscle Control. For more information, see our paper describing model development and validation: https://www.ncbi.nlm.nih.gov/pubmed/25901907\n\nAlso see our webinar: http://opensim.stanford.edu/support/event_details.html?id=143\n\nIn a related project, these models have been adapted to create full body models for children and adolescents: https://simtk.org/projects/spine-children\n\nWe have released a set of subject-specific models created for 250 individuals (125 men, 125 women, ages 41 - 90) from the Framingham Heart Study:  https://dataverse.harvard.edu/dataverse/SpineModeling\n\nWe are currently using these models to study the use of an exosuit and variations in spine loading in the population, including effects of spine curvature and variability in loading in different activities, as well as how this relates to the risk of vertebral fractures in older adults. See our publications list for recent papers.\n","has_downloads":true,"keywords":"Spine,rib cage,thoracic,thorax,ventilation","ontologies":"Neuromuscular_Model","projMembers":"Katelyn Burkhart,Hossein Mokhtarzadeh,Dennis Anderson,Brett Allaire,Alexander Bruno,Mary Bouxsein,Jacob J. 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The structures of the knee were segmented from magnetic resonance images and an iterative closest point algorithm established nodal correspondence between a fine mesh for each member of the training set and a template mesh.  Each of the structures was described in their local coordinate system and a 4x4 transformation was used to describe relative alignment between the structures in the as-scanned position.  The statistical model utilized the nodal coordinates for the knee structures and the transformation matrices in a principal component analysis to capture the shape and alignment variability.  \n\nThe statistical model describes intersubject anatomic variability in the shape and alignment of the knee structures and provides the ability to automatedly generate the geometry for a joint-level finite element analysis for members of the training set or virtual subjects derived from the statistical model, thus facilitating population-based evaluations. \n\nAs part of the project, we also prepared Biomechanics Education Modules that are targeted at middle and high school students (or even undergraduates).  Please visit https://simtk.org/home/biomech_ed/ for more information.\n\nThis work was supported by the National Science Foundation, General and Age Related Disabilities Engineering under CBET Grant: 1034251.\n&quot;Population-based evaluation of knee mechanics considering inter-subject and surgical alignment variability&quot;\nInvestigators: P. Laz, P. Rullkoetter, D. Dennis, R. Kim","has_downloads":true,"keywords":"intersubject variability,knee anatomy,statistical shape model","ontologies":"Finite_Element_Model,Mesh_Model","projMembers":"Kevin Shelburne,Lowell Smoger,Peter Laz,Clare Fitzpatrick,Clare Fitzpatrick","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"961","unix_group_name":"biomech_ed","modified":"1491507678","downloads":"191","group_name":"Biomechanics Education Modules","logo_file":"","short_description":"Provides educational modules on biomechanics principles targeted at middle and high school and undergraduate students","long_description":"This project contains biomechanics modules on “Understanding Forces on the Body” and “Imaging, Materials, and Failure”.  The modules include powerpoint slides and exercises to support the lessons. Exercises illustrate moment arms via a knee experiment and strength of materials via an imaging-based ACL tear example.\n\nThis project was supported by the National Science Foundation through:\n\nPopulation-based evaluation of knee mechanics considering inter-subject and surgical alignment variability\nInvestigators: P. Laz, P. Rullkoetter, D. Dennis, R. Kim\nGeneral and Age Related Disabilities Engineering, CBET-1034251.\n\nMRI: Acquisition of a biplane fluoroscopy system for dynamic imaging of in-vivo human motion.\nInvestigators: P. Rullkoetter, B. Davidson, C. Fitzpatrick, P. Laz, K. Shelburne\nDivision of Computer and Network Systems CNS-1229148.\n\nFor more information on the related statistical shape modeling work, please visit https://simtk.org/home/ssm_knee/","has_downloads":true,"keywords":"biomechanics education,outreach","ontologies":"Education","projMembers":"Kevin Shelburne,Lowell Smoger,Peter Laz","trove_cats":[{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"962","unix_group_name":"popmodwkgrpimag","modified":"1538709629","downloads":"0","group_name":"Population Modeling Workgroup","logo_file":"popmodwkgrpimag","short_description":"Provide a web portal and communication methods such as a mailing list for population modelers.","long_description":"This is the web portal of the Population Modeling Working Group associated with the Interagency Modeling and Analysis Group (IMAG).\nFor additional information see:\nhttp://www.imagwiki.nibib.nih.gov/content/population-modeling-working-group\n\nVisit our discussions at: https://simtk.org/pipermail/popmodwkgrpimag-news/\n\nThe working group produced several review papers mapping the field:\n\n1) Population Modeling Working Group, Population Modeling by Examples (WIP) - SpringSim 2015 , April 12 - 15, Alexandria, VA, USA . Paper available &lt;a href=&quot;http://dl.acm.org/citation.cfm?id=2887741&quot;&gt; Here   &lt;/a&gt; and  &lt;a href=&quot;https://simtk.org/docman/view.php/962/1892/PopModExample_Submit_2015_03_04.doc&quot;&gt;  Here &lt;/a&gt;  Presentation available  &lt;a href=&quot;https://simtk.org/docman/view.php/962/1897/SpringSim2015PopMod_Upload_2015_04_10.pptx&quot;&gt; Here &lt;/a&gt;\n\n2) Population Modeling Working Group, Population Modeling by Examples II - SummerSim 2016 , July 24 - 27, Montreal, CA. Paper available &lt;a href=&quot;https://simtk.org/docman/view.php/962/1963/SummerSim_2016_PopMod_Submit_2016_05_15_Robert_Smith.pdf&quot;&gt; Here &lt;/a&gt; and  &lt;a href=&quot;https://doi.org/10.22360/SummerSim.2016.SCSC.060&quot;&gt; Here &lt;/a&gt;  Presentation available &lt;a href=&quot;https://simtk.org/docman/view.php/962/1988/PopulationModellingByExamplesII_SummerSim_2016.pdf&quot;&gt; Here &lt;/a&gt;\n\n3) Population Modeling Working Group, Population Modeling by Examples III - SummerSim 2017 , July 9 - 12, Bellevue, WA, USA. Paper available &lt;a href=&quot;https://doi.org/10.22360/SummerSim.2017.SCSC.013&quot;&gt; Here &lt;/a&gt; and &lt;a href=&quot;https://simtk.org/docman/view.php/962/4654/PopulationModelingByExamples3_Submit_2017_06_04.docx&quot;&gt; Here &lt;/a&gt;  Presentation is available &lt;a href=&quot;https://simtk.org/docman/view.php/962/4657/SummerSim2017PopMod3_Upload_2017_07_09.pptx&quot;&gt; Here &lt;/a&gt;\n\nTo view the map of population modeling and its classifications, please follow this &lt;a href=&quot;https://docs.google.com/spreadsheets/d/1Ljl6l7hyYbb7wmyoFcrJl38593ufqt65agwHT_EIFEo/edit?usp=sharing&quot;&gt; link&lt;/a&gt;:\n","has_downloads":false,"keywords":"workgroup,population","ontologies":"Web_Posting,Mailing_List","projMembers":"Gary An,Madhav Marathe,Sergey Nuzhdin,Jacob Barhak","trove_cats":[{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"963","unix_group_name":"scmt","modified":"1442848896","downloads":"237","group_name":"Surrogate Contact Modeling Toolbox","logo_file":"scmt","short_description":"Provides the tools to create, test, and use surrogate contact models.","long_description":"This opensource toolbox provides researchers with the capabilities to construct and use surrogate contact models. 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To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. 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As the population ages and life expectancy increases, this trend is not likely to change. Myocardial infarction (MI)-induced adverse left ventricular (LV) remodeling is responsible for nearly 70% of heart failure cases. The adverse remodeling process involves an extension of the border zone (BZ) adjacent to an MI, which is normally perfused but shows myofiber contractile dysfunction. To improve patient-specific modeling of cardiac mechanics, we sought to create a finite element model of the human LV with BZ and MI morphologies integrated directly from delayed-enhancement magnetic resonance (DE-MR) images. Instead of separating the LV into discrete regions (e.g., the MI, BZ, and remote regions) with each having a homogeneous myocardial material property, we assumed a functional relation between the DE-MR image pixel intensity and myocardial stiffness and contractility--we considered a linear variation of material properties as a function of DE-MR image pixel intensity, which is known to improve the accuracy of the model''''s response. The finite element model was then calibrated using measurements obtained from the same patient--namely, 3D strain measurements-using complementary spatial modulation of magnetization magnetic resonance (CSPAMM-MR) images. This led to an average circumferential strain error of 8.9% across all American Heart Association (AHA) segments. We demonstrate the utility of our method for quantifying smooth regional variations in myocardial contractility using cardiac DE-MR and CSPAMM-MR images acquired from a 78-yr-old woman who experienced an MI approximately 1 yr prior. We found a remote myocardial diastolic stiffness of C(0) = 0.102 kPa, and a remote myocardial contractility of T(max) = 146.9 kPa, which are both in the range of previously published normal human values. Moreover, we found a normalized pixel intensity range of 30% for the BZ, which is consistent with the literature. Based on these regional myocardial material properties, we used our finite element model to compute patient-specific diastolic and systolic LV myofiber stress distributions, which cannot be measured directly. One of the main driving forces for adverse LV remodeling is assumed to be an abnormally high level of ventricular wall stress, and many existing and new treatments for heart failure fundamentally attempt to normalize LV wall stress. Thus, our noninvasive method for estimating smooth regional variations in myocardial contractility should be valuable for optimizing new surgical or medical strategies to limit the chronic evolution from infarction to heart failure.","has_downloads":true,"keywords":"Myocardial infarct,patient-specific modeling,Tagged MRI,border zone,computational modeling","ontologies":"","projMembers":"Martin Genet,Lik Chuan Lee,Julius Guccione,gabriel Acevedo-Bolton","trove_cats":[{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1010","unix_group_name":"hand_muscle","modified":"1435240807","downloads":"1482","group_name":"Human hand muscle attachments for an OpenSim upper-extremity model","logo_file":"hand_muscle","short_description":"Provide the code base for creating,simulation, and visualizing intrinsic and extrinsic hand muscles of musculoskeletal upper-extremity model","long_description":"The study described how to determine muscle attachment points for the index, middle, ring and little fingers in an OpenSim upper-extremity model. 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This project presents an optimization based technique to adjust muscle parameters of models linearly scaled from a generic one and to obtain reasonable estimation of optimal fiber lengths and tendon slack lengths in models generated from medical images.\n\n========================================================================\n&lt;ul&gt;\n&lt;li&gt; &lt;b&gt;IMPORTANT:&lt;/b&gt; We have disable the Forum tab on SimTK because rarely monitored. 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This is important for finite element studies of these tissues, as the fiber direction must be specified in the constitutive model. This project contains code, models, and data that can be used to reproduce the results of our publication on this technique. The supplied instructional videos will enable researchers to easily and efficiently apply this method to a variety of other tissues. The software used in the fiber tractography process and demonstrated in this project is Matlab, Autodesk Inventor (free for educators), and Autodesk Simulation CFD (free for educators). Full demonstrations and process instructions can be found in the 7 videos posted at https://vimeo.com/album/3414604:\n\nContents:\n\nChapter 1: Introduction (2:35)\nThis video introduces the CFD fiber tractography software pipeline\n\n<!-- This version of the embed code is no longer supported. Learn more: https://vimeo.com/s/tnm --> <object width=\"500\" height=\"281\"><param name=\"allowfullscreen\" value=\"true\" /><param name=\"allowscriptaccess\" value=\"always\" /><param name=\"movie\" value=\"https://vimeo.com/moogaloop.swf?clip_id=129107314&force_embed=vimeo.com&fullscreen=1\" /><embed src=\"https://vimeo.com/moogaloop.swf?clip_id=129107314&force_embed=vimeo.com&fullscreen=1\" type=\"application/x-shockwave-flash\" allowfullscreen=\"true\" allowscriptaccess=\"always\" width=\"500\" height=\"281\"></embed></object>\n\nChapter 2: Supplementary materials code, models and data (20:21)\nThis video shows the shared models, code, and data posted online at simtk.org/m3lab_cfd4fea.\n\nChapter 3: Finite element simulations (5:38)\nThis video shows finite element simulations using the fiber mapping process.\n\nChapter 4: Iliacus example walkthrough (21:38)\nThis video shows the step-by-step process for fiber mapping the iliacus muscle (a hip flexor).\n\nChapter 5: Bflh example 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For several proteins, their various states can be distinguished from each other on the basis of their minimum energy 3D structures. For many other proteins, like GPCRs, PDZ domains, nuclear transcription factors, heat shock proteins, T-cell receptors and viral attachment proteins, their states can be distinguished categorically from each other only when their finite-temperature conformational ensembles are considered alongside their minimum-energy structures. We are developing tools/methods for:\n(A1) Direct comparison of conformational ensembles - The traditional approach to compare two or more conformational ensembles is to compare their respective summary statistics. This approach is, however, prone to artifactual bias, as data is compared after dimensionality reduction. The proper way to compare ensembles is to compare them directly with each other and prior to any dimensionality reduction. g_ensemble_comp is a tool we have developed that does just that and reports the difference between ensembles in terms of a true metric defined by the zeroth law of thermodynamics. \n(A2) Prediction of allosteric signaling networks - method under development.\n\n(B) LIPID MEMBRANES: The surface area of a lipid bilayer is related fundamentally to many other observables, such as thermal phase transitions and domain formation in mixed lipid bilayers. We have developed g_tessellate_area to compute the 3D surface area of a bilayer using Delunay tessellation.","has_downloads":true,"keywords":"computational modeling,Molecular Dynamics,Protein Dynamics,allosteric communication,Molecular Modeling,allostery,signal transduction","ontologies":"Regulatory_Signaling_Network_Reconstruction,Molecular_Dynamics,Modeling_and_Simulation,Protein-Protein_Interaction","projMembers":"Ahnaf Siddiqui,Sameer Varma","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular 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Agent-based models lend themselves to model from an individual's perspective, and we extend that with a framework which utilizes a cell's perspective in an off-lattice environment. We aim to help increase the understanding of biological phenomena through our Delaunay and Voronoi off-lattice agent-based, discrete event framework. We focus on biological cells and expand on existing cell- and agent-centered methods by offering a new perspective in an off-lattice environment.","has_downloads":true,"keywords":"Morphogenesis,Biomimetic,Cell Behavior,Mechanistic Explanations,Modeling,Off-lattice","ontologies":"","projMembers":"C. 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Despite the fact that there is a user friendly OpenSim interface for Matlab, it lacks the ability to extend new functionalities based on the Java API (e.g. custom controller). Inspired by the relative project “Dynamic Simulation of Movement Based on OpenSim and MATLAB®/Simulink®”, where the user can easily interface OpenSim with Simulink, the proposed framework moves one step further by providing new capabilities to link custom written C++ OpenSim extensions to Matlab and to harvest both the powerful OpenSim C++ API and Matlab functionalities. The implementation is based on Matlab mex interface, which is further extended to support more complex functionalities based on the project mexplus. The latter is a C++ Matlab mex development kit that contains a couple of C++ classes and macros to make mex development easy in Matlab.\n\nAn example project is provided in the download section with instructions on how-to use.","has_downloads":true,"keywords":"matlab,neuromusculoskeletal modelling,OpenSim C++ API,opensim c++ api,neuromusculoskeletal simulation,simulink","ontologies":"","projMembers":"Dimitar Stanev,rosa Pamies","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"320","fullname":"Miscellaneous"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"1043","unix_group_name":"aredsimulation","modified":"1449704987","downloads":"754","group_name":"Simulation of ARED Squat Exercise on the International Space Station","logo_file":"aredsimulation","short_description":"Provide the OpenSim model used in the journal article along with sample inputs so that others can simulate ARED squat exercise on the International Space Station.","long_description":"This project provides an OpenSim model of an astronaut performing ARED squat exercise on the International Space Station (ISS). Along with the model, we provide input files for performing a sample forward dynamic simulation and a Matlab program for plotting the predicted ground reactions and muscle moments. A README file gives step-by-step instructions. We also provide animation files showing simulated motions for ARED squat exercise performed on Earth and on the ISS.","has_downloads":true,"keywords":"Biomechanics,Muscle loading,Space exploration,Microgravity,Movement prediction,Dynamics simulation","ontologies":"Neuromuscular_Model,Numerical_Model","projMembers":"Brandon Kim,B.J. Fregly,Christopher Fregly","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1044","unix_group_name":"il4_redox","modified":"1458333980","downloads":"0","group_name":"Dynamic Redox Regulation of IL-4 Signaling","logo_file":"il4_redox","short_description":"This paper provides a systems level integration of the molecular events subject to reversible oxidation during IL-4 signal transduction. Code for simulation provided.","long_description":"Incomplete reduction of oxygen during respiration results in the formation of highly reactive molecules known as reactive oxygen species (ROS) that react indiscriminately with cellular components and adversely affect cellular function. For a long time ROS were thought solely to be undesirable byproducts of respiration. Indeed, high levels of ROS are associated with a number of diseases. Despite these facts, antioxidants, agents that neutralize ROS, have not shown any clinical benefits when used as oral supplements. This paradox is partially explained by discoveries over the last two decades demonstrating that ROS are not always detrimental and may be essential for controlling physiological processes like cell signaling. However, the mechanisms by which ROS react with biomolecules are not well understood. In this work we combined biological experiments with novel computational methods to identify the most important mechanisms of ROS-mediated regulation in the IL-4 signaling pathway of the immune system. We developed a detailed computer model of the IL-4 pathway and its regulation by ROS dependent and independent methods. Our work enhances the understanding of principles underlying regulation of cell signaling by ROS and has potential implications in advancing therapeutic methods targeting ROS and their adverse effects.","has_downloads":false,"keywords":"cytokine,thiol oxidation,T cell,cell signaling","ontologies":"Cell_Model,Network_Interaction_Model","projMembers":"Melissa Kemp","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"1046","unix_group_name":"directcolloc","modified":"1695406566","downloads":"993","group_name":"Optimal Control of Musculoskeletal Movement Using OpenSim &amp; MATLAB","logo_file":"directcolloc","short_description":"Presents an approach for generating optimal control simulations using OpenSim &amp; MATLAB","long_description":"This project presents an approach for generating optimal control simulations of human movement using OpenSim and MATLAB based on the direct collocation approach. Our implementation is described in the paper &quot;Generating Optimal Control Simulations of Musculoskeletal Movement Using OpenSim and MATLAB&quot; which is available on the Publications page. Models, results and a complete working example are provided on the Downloads page. This project was supported by grants from the U.S. National Science Foundation (awards BCS 0935327 and IIS 1526986) and the National Center for Simulation in Rehabilitation Research.\n\nNote: The code shared with this project was developed with OpenSim 3.x and is not compatible with OpenSim 4.x. The code can still be used with OpenSim 3.x, or it may be helpful just to see how it is laid out. People currently interested in using the direct collocation approach with OpenSim are encouraged to consider using OpenSim Moco:\nhttps://opensim-org.github.io/opensim-moco-site ","has_downloads":true,"keywords":"predictive simulation,musculoskeletal model,optimization","ontologies":"Neuromuscular_Model,Modeling_and_Simulation,Optimizer","projMembers":"Brian Umberger,Leng-Feng Lee","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"1051","unix_group_name":"c3d2opensim_btk","modified":"1447371144","downloads":"766","group_name":"C3D Extraction Toolbox GUI","logo_file":"","short_description":"Provide an easy to use Matlab GUI in which to extract c3d files into an OpenSim compatible format","long_description":"This project utilises Matlab and the Biomechanical ToolKit (btk) to provide an easy to use GUI that extracts motion capture and force plate data from a C3D file and outputs OpenSim compatible files.\n\nThis toolbox combines two extraction toolboxes created by Tim Dorn and Glen Lichtwark. For an in-depth description of some of the functions used in this toolbox and to download unmodified versions of these toolboxes, please follow the links below,\n\nTim’s toolbox: https://simtk.org/home/c3dtoolbox/\nGlen’s toolbox: https://simtk.org/home/matlab_tools.\n\nMain features include:\nEasy to use graphical user interface to extract c3d files to OpenSim compatible files\nWorks for any laboratory setup (marker set and/or force plates)\nForce stitching function that stitches together two force plate strikes\nAutomatically creates OpenSim tool setup files \n\nNote that this toolbox requires the Biomechanical ToolKit (btk) available at https://code.google.com/p/b-tk/. 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Details on the modeling assumptions, simulations to predict moment arms, and experimental moment arm study are described in the cited publication. \n\nPlease respect the time and effort of the original developers by reading the accompanying publications and citing the appropriate references when benefiting from or building upon this work. \n\nPlease refer to the Downloads page for our release of (1) PRC models based on limited data, (2) SE4CF models based on limited data, and (3) experimental moment arm data of the nonimpaired and surgically salvaged wrist.  All currently available models were developed in SIMM. 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In practice, the skills and experiences of model developers will reflect upon their modeling choices; and as a result, discrepancies will exist. The proposed activity will document the magnitude and potential sources of such discrepancies through comparisons of model components and simulation results. This project will examine and critique the current state of model development and simulation reproducibility in joint and tissue mechanics. This will translate into reliable models of the knee joint for simulation-based discoveries and in silico design and evaluation of medical devices and interventions. The required exchange of data, model components, and simulation results among the teams and with the public will also impact developers and users of such resources. Specifications, to facilitate data and model exchange and to develop data and modeling standards, and guidance, to inform modeling and simulation workflows, will likely emerge as by-products of the research activity. 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Particularly for the analysis of the pushup mechanics.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ryan Reynolds","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1080","unix_group_name":"teststex2005","modified":"1462483520","downloads":"0","group_name":"OpenSim Test","logo_file":"","short_description":"Testing how this software works","long_description":"Testing how this software works","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Stefano Dalla Gasperina","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1081","unix_group_name":"emgdrivenmodel","modified":"1498154572","downloads":"440","group_name":"Lower Extremity EMG-driven Modeling with Automated Adjustment of Geometry","logo_file":"emgdrivenmodel","short_description":"This project provides Matlab code for developing EMG-driven models of walking using scaled OpenSim lower extremity musculoskeletal models.","long_description":"This project provides Matlab code for developing EMG-driven models of walking using scaled OpenSim lower extremity musculoskeletal models. The Matlab code adjusts not only traditional Hill-type muscle-tendon model parameters values (optimal muscle fiber length, tendon slack length) but also non-traditional musculoskeletal model parameter values (EMG scale factors, coefficients defining surrogate representations of muscle-tendon lengths, velocities, and moment arms). Along with the Matlab code, we provide sample walking data (marker, ground reaction, and EMG data) collected from a hemiparetic male subject and a sample scaled OpenSim musculoskeletal model of the subject. A README file provides instructions for how to perform the EMG-driven model calibration process using the provided Matlab code, walking data, and scaled OpenSim model.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"B.J. Fregly,Andrew Meyer","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"1082","unix_group_name":"assistloadwalk","modified":"1639074894","downloads":"2506","group_name":"Simulated Assistive Devices for Loaded Walking","logo_file":"assistloadwalk","short_description":"This project contains experimental data of male subjects walking while carrying heavy load, and OpenSim simulations of these subjects walking with hypothetical ideal assistive devices.","long_description":"This project contains experimental data and muscle-actuated tracking simulations of male subjects walking while carrying heavy load, and OpenSim simulations of these subjects wearing hypothetical ideal assistive devices. We collected motion capture data of 7 subjects walking in 4 different conditions: walking (a) without load at a freely selected speed, (b) without load at 80% of the freely selected speed, (c) while carrying 38 kg on the torso at a new freely selected speed, and (d) while carrying 38 kg at the same speed as in (a).\n\nBased on the simulations of loaded walking (condition (c) above), we created new simulations to predict the effect of ideal assistive devices on the metabolic cost of walking. We examined 7 massless devices that each provided unrestricted torque at one degree of freedom and in one direction: hip abduction, hip flexion, hip extension, knee flexion, knee extension, ankle plantarflexion, and ankle dorsiflexion. We estimated the optimal device torques, and the devices' effect on metabolic cost and muscle activity.\n\nDembia CL, Silder A, Uchida TK, Hicks JL, Delp SL (2017) Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PLoS ONE 12(7): e0180320. https://doi.org/10.1371/journal.pone.0180320","has_downloads":true,"keywords":"metabolic cost,assistive devices,loaded walking,exoskeleton,neuromuscular simulation","ontologies":"","projMembers":"Christopher Dembia,Amy Silder,Thomas Uchida,Jennifer Hicks,Scott Delp,Jon Stingel","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"1085","unix_group_name":"htmd","modified":"1463664220","downloads":"0","group_name":"HTMD - High Throughput Molecular Dynamics","logo_file":"htmd","short_description":"HTMD is a molecular-specific programmable environment to prepare, handle, simulate, visualize, and analyze molecular systems.\nHTMD is based on Python, so that scientists can easily extend it to their needs. With HTMD, it is possible to do very complex protocols in just a few lines.","long_description":"In a single script, it is possible to plan an entire computational experiment, from manipulating PDBs, building, executing and analyzing simulations, computing Markov state models, kinetic rates, affinities and pathways.\n\nSee more information on &lt;a href=&quot;https://www.htmd.org/&quot;&gt;https://www.htmd.org&lt;/a&gt;.\nHTMD Forum: &lt;a href=&quot;https://forum.htmd.org/&quot;&gt;https://forum.htmd.org&lt;/a&gt;\n\nWe are also on Github: &lt;a href=&quot;https://github.com/Acellera/htmd&quot;&gt;https://github.com/Acellera/htmd&lt;/a&gt;\nReport issues on: &lt;a href=&quot;https://github.com/Acellera/htmd/issues&quot;&gt;https://github.com/Acellera/htmd/issues&lt;/a&gt;","has_downloads":false,"keywords":"drug discovery","ontologies":"","projMembers":"Acellera Acellera,João M. Damas,MJ Harvey,Stefan Doerr,Gianni De Fabritiis","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"1089","unix_group_name":"rtosim","modified":"1472770909","downloads":"2097","group_name":"Real-time OpenSim extension","logo_file":"","short_description":"The real-time OpenSim extension (RTOSIM) is a set of libraries in C++ that wrap OpenSim APIs to enable the real-time computation of inverse kinematics and inverse dynamics.","long_description":"RTOSIM is a set of efficient and extensible C++ libraries to connect OpenSim with different devices. RTOSIM can use data provided by motion capture systems to solve OpenSim inverse kinematics and inverse dynamics on a frame-by-frame basis. Multiple threads operate concurrently to remove idle times due to communications with input and output devices, and the data flow is automatically managed by RTOSIM in order to preserve data integrity and avoid race conditions.\n\nThe inverse kinematics throughput is also enhanced by the use of multiple threads. From our tests, full-body inverse kinematics using the gait2392 can be solved up to 2000fps using 10+ cores. \n\nRTOSIM source code is available on GitHub (see Downloads section).","has_downloads":true,"keywords":"C++,opensim,inverse dynamics,inverse kinematics,real-time","ontologies":"Computational_Model,Source_Code","projMembers":"Monica Reggiani,Claudio Pizzolato","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1090","unix_group_name":"afferents","modified":"1465971120","downloads":"76","group_name":"Afferent information during the simulation","logo_file":"","short_description":"This project aims to provide muscle classes that incorporate afferent feedback from muscle spindles and Golgi tendon organs. This feedback is calculated during the simulation, so it can potentially be used to provide afferent information to a controller.","long_description":"This project aims to provide muscle classes that incorporate afferent feedback from muscle spindles and Golgi tendon organs. This feedback is calculated during the simulation, so it can potentially be used to provide afferent information to a controller.\n\nThe current implementation is based on a Millard2012EquilibriumMuscle. This class is extended by the addition of objects implementing a model of the muscle spindle, and a model of the GTO.  In addition to the C++ code for the muscle, spindle, and GTO classes, there are two custom Controller classes, an Analysis class, and a version of the TugOfWar model from the tutorials, which can be used for testing.\n\nThe spindle afferent model comes from: Mileusnic et al 2006 &quot;Mathematical models of proprioceptors. I. Control and transduction in the muscle spindle&quot; J Neurophysiol 96:1772-1788.\nThe GTO model is the one used in: Lin, Crago 2002 &quot;Neural and mechanical contributions to the stretch reflex: A model synthesis&quot;  Ann Biomed Eng 30:54-67.","has_downloads":true,"keywords":"sensory information,afferents,muscle, spindle, Golgi tendon organ","ontologies":"Dynamic_Model","projMembers":"Sergio Verduzco-Flores","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":true,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1091","unix_group_name":"amputee-gait","modified":"1467220618","downloads":"0","group_name":"Amputee gait transtibial","logo_file":"","short_description":"We study the effects of transtibial prosthetic alignment during  the gait","long_description":"We study the effects of transtibial prosthetic alignment during  the gait","has_downloads":false,"keywords":"","ontologies":"","projMembers":"ESPERANZA CAMARGO","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1092","unix_group_name":"model-test1","modified":"1464488674","downloads":"0","group_name":"Test Modelling","logo_file":"","short_description":"Referring to inverse dynamics, changing muscle forces and velocitys, accelerations to see the effects. How is velocity of a motion related to muscle force? What muscle forces are needed to produce a specific velocity of a segment or multiple segments for ","long_description":"Referring to inverse dynamics, changing muscle forces and velocitys, accelerations to see the effects. How is velocity of a motion related to muscle force? What muscle forces are needed to produce a specific velocity of a segment or multiple segments for a specific movement.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kevin Long","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1093","unix_group_name":"synwalkingpred","modified":"1482184535","downloads":"481","group_name":"Muscle Synergies Facilitate Prediction of Subject-specific Walking Motions","logo_file":"synwalkingpred","short_description":"This project provides Matlab code and sample experimental walking data for generating dynamically consistent full-body walking predictions for a high functioning stroke subject. The predictions are generated using a patient-specific full-body OpenSim/Matlab neur","long_description":"This project provides Matlab code and experimental walking data for generating dynamically consistent full-body walking predictions for a high functioning stroke subject. The predictions are generated using a patient-specific full-body OpenSim/Matlab neuromusculoskeletal model and the direct collocation optimal control software GPOPS-II, which functions within the Matlab programming environment. The neuromusculoskeletal model is calibrated to the subject's walking data collected at his self-selected speed of 0.5 m/s to produce a patient-specific lower-body kinematic model, patient-specific foot-ground contact models, patient-specific EMG-driven muscle models, and patient-specific neural control models based on the subject's muscle synergy structure. The calibrated model is used to predict how the subject will walk at 0.5 and 0.8 m/s using either joint torque controls (5 per leg), muscle activation controls (35 per leg), or muscle synergy controls (5 per leg) applied to the lower body joints. For now, this project is a placeholder pending acceptance of the associated journal manuscript by Frontiers in Bioengineering and Biotechnology.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"B.J. Fregly,Andrew Meyer","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1094","unix_group_name":"todtest5","modified":"1486970460","downloads":"0","group_name":"Test Project #5","logo_file":"","short_description":"This is a test private test project by Tod Hing.","long_description":"This is a test private test project by Tod Hing.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Tod Hing","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1096","unix_group_name":"optcntrlmuscle","modified":"1593604527","downloads":"618","group_name":"Direct Collocation Optimal Control for Solving the Muscle Redundancy Problem","logo_file":"optcntrlmuscle","short_description":"This project provides Matlab code for solving the muscle redundancy problem using direct collocation. ","long_description":"Estimation of muscle forces during motion involves solving an indeterminate problem (more unknown muscle forces than joint moment constraints), frequently via optimization methods. When the dynamics of muscle activation and contraction are modeled for consistency with muscle physiology, the resulting optimization problem is dynamic and challenging to solve. This project proposes two computationally efficient formulations for solving these dynamic optimization problems using direct collocation optimal control methods. Both formulations rely on implicit representation of the contraction dynamics and use either muscle length or tendon force as state variable.\n\nThis project provides Matlab code for solving the muscle redundancy problem based on both formulations using direct collocation. This project also provides OpenSim models and necessary input data for solving the muscle redundancy problem in the leg during walking and running trials with different models.\n\nFrom v1.1, an implicit formulation of the muscle activation dynamics is available.\nFrom v2.1, an open-source version of the code is available.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"B.J. Fregly,Friedl De Groote,Antoine Falisse,Tom Van Wouwe,Maarten Afschrift","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1102","unix_group_name":"strswrcon","modified":"1467041172","downloads":"0","group_name":"StR SwR model","logo_file":"","short_description":"Novacheck (1998) provides an excellent summary of run mechanics.  Of interest is his phases of force absorption and deceleration and his out of phase stance reversal and swing reversal phases within the absorption and deceleration gait cycle. My current w","long_description":"Novacheck (1998) provides an excellent summary of run mechanics.  Of interest is his phases of force absorption and deceleration and his out of phase stance reversal and swing reversal phases within the absorption and deceleration gait cycle. My current work has developed 2D kinematic measurement norms that I would like to see how they correspond from a model-based approach.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Rob Stanley","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1104","unix_group_name":"asp-hiv-1","modified":"1467327417","downloads":"0","group_name":"HIV-1 Antisense protein","logo_file":"","short_description":"The existence of an HIV-1 protein translated from an antisense transcript was suggested over 25 years ago. However, the Antisense Protein (ASP) gene has still not been completely accepted by the HIV-1 research community. In past years, several studies hav","long_description":"The existence of an HIV-1 protein translated from an antisense transcript was suggested over 25 years ago. However, the Antisense Protein (ASP) gene has still not been completely accepted by the HIV-1 research community. In past years, several studies have highlighted the existence of HIV-1 antisense transcripts.   More recently, we and others have convincingly demonstrated that this transcript produces a protein with a unique distribution and a rapid turnover, when expressed in mammalian cells. The aim of this project is to find out the structure of ASP, which suggest that ASP needs to be considered as a viral gene, playing an important role in HIV-1 replication and persistence. In light of these recent reports, we believe that ASP needs to be added to the schematic representation of the HIV-1 proviral DNA and requires further investigation, as it could represent a new potential target for anti-retroviral therapies and vaccine strategies.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Zhenlong Liu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1105","unix_group_name":"tcell-aging","modified":"1482347018","downloads":"0","group_name":"T cell calcium dynamics regulated by age-induced oxidation","logo_file":"tcell-aging","short_description":"This project provides modeling files associated with the 2016 PLoS One publication \"Calcium dynamics of ex vivo long-term cultured CD8+ T cells are regulated by changes in redox metabolism\" by Rivet, Kniss-James et al.","long_description":"T cells reach a state of replicative senescence characterized by a decreased ability to proliferate and respond to foreign antigens. Calcium release associated with TCR engagement is widely used as a surrogate measure of T cell response. Using an ex vivo culture model that partially replicates features of organismal aging, we observe that while the amplitude of Ca2+ signaling does not change with time in culture, older T cells exhibit faster Ca2+ rise and a faster decay. Gene expression analysis of Ca2+ channels and pumps expressed in T cells by RT-qPCR identified overexpression of the plasma membrane CRAC channel subunit ORAI1 and PMCA in older T cells. To test whether overexpression of the plasma membrane Ca2+ channel is sufficient to explain the kinetic information, we adapted a previously published computational model by Maurya and Subramaniam to include additional details on the store-operated calcium entry (SOCE) process to recapitulate Ca2+ dynamics after T cell receptor stimulation. Simulations demonstrated that upregulation of ORAI1 and PMCA channels is not sufficient to explain the observed alterations in Ca2+ signaling. Instead, modeling analysis identified kinetic parameters associated with the IP3R and STIM1 channels as potential causes for alterations in Ca2+ dynamics associated with the long term ex vivo culturing protocol. Due to these proteins having known cysteine residues susceptible to oxidation, we subsequently investigated and observed transcriptional remodeling of metabolic enzymes, a shift to more oxidized redox couples, and post-translational thiol oxidation of STIM1. The model-directed findings from this study highlight changes in the cellular redox environment that may ultimately lead to altered T cell calcium dynamics during immunosenescence or organismal aging.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Melissa Kemp","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"421","fullname":"Cell"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1110","unix_group_name":"finger","modified":"1513706821","downloads":"179","group_name":"Index finger model","logo_file":"finger","short_description":"This is a musculoskeletal model of the index finger, which includes intrinsic muscles and a plugin to estimate muscle activations using static optimisation and experimentally collected EMG. The aim of the model and solution method  is to improve estimates of muscle co-contraction.","long_description":"This musculoskeletal model of the index finger with four degrees of freedom and seven muscles (divided into 14 component through the extensor mechanism). \n\nWe have included a plugin to apply constraints based on experimentally collected EMG to static optimization solution methods. \n\nThe goal of these tools is to improve co-contraction estimates and provide a means to assess tendon tension distribution through the finger.  ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Alex MacIntosh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1115","unix_group_name":"osimdatabase","modified":"1469131209","downloads":"0","group_name":"OpenSim Model and Data Databse","logo_file":"osimdatabase","short_description":"","long_description":"The purpose of this project is to encourage different researchers to upload their models, recorded data and simulation setup or any other related data, so that everything will be easy to find and centralized on github.\n\nhttps://github.com/mitkof6/OpenSimDatabase","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dimitar Stanev","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1118","unix_group_name":"ankmodelatfl","modified":"1469487779","downloads":"0","group_name":"Modeling effective methods to prevent ankle injuries with OpenSim","logo_file":"","short_description":"Using OpenSim models to help prevent ankle injuries\nDeveloped by Sathvik Koneru \nSaratoga High School","long_description":"The anterior talofibular ligament(ATFL) is the most commonly injured part of the ankle. Thus by adding support and forces acting on the ATFL, testing the angle of ankle inversion is possible; according to previous research a subtalar angle greater than 25 degrees can cause injury. By using a simulation based program such as OpenSim,  I should accurately be able to find an effective apparatus to prevent ankle injuries based on placing forces at certain point on the ankle to help prevent injury and keeping the subtalar angle under 25 degrees from a drop landing.\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sathvik Koneru","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1121","unix_group_name":"parallelrobot","modified":"1489091318","downloads":"0","group_name":"Parallel robots simulation","logo_file":"","short_description":"Parallel robots are used in rehabilitation, because they have advantages over serial robots. OpenSim let users add constraints. In parallel robotics field,  geometric constraints are very important for analysis. ","long_description":"Parallel robots are used in rehabilitation, because they have advantages over serial robots. OpenSim let users  add constraints. In parallel robotics field, geometric constraints are very important for analysis. The mechanical interaction between human body and robots is a growing research theme. This approach can be used as a tool. First,capturing information about human body, then, applying the toolchain analysis in OpenSim, scaling, kinematics and dynamics. Finally integrate the models and simulate the impact in forces and movements of robot in the musculoskeletal model.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Julio Hernando Vargas Riaño","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1124","unix_group_name":"emgrower","modified":"1470073325","downloads":"0","group_name":"Biomechanics of Ergometric Rowing","logo_file":"","short_description":"Determining the joint contact forces of the knee during rowing in OpenSim using an EMG driven model.","long_description":"Determining the joint contact forces of the knee during rowing in OpenSim using an EMG driven model.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Gabriel Del Castillo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1125","unix_group_name":"mhealthconnect","modified":"1507738885","downloads":"0","group_name":"mHealth Connect","logo_file":"","short_description":"mHealth Connect is a group that aims to improve the use of physical activity wearables and apps for clinical purposes.","long_description":"mHealth Connect is a group that aims to improve the use of physical activity wearables and apps for clinical purposes. 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neurophysiology. More specifically, the mot","long_description":"This project provides more detailed information on the research article entitled &quot;Mechanical effort predicts the selection of ankle over hip strategies in non-stepping postural responses&quot; published in journal of neurophysiology. More specifically, the motion capture data of perturbed standing and software to predict the measured postural response is shared. This information is on the one hand usefull to reproduce the experimental and simulation results. On the other hand, the software can be used as a basis for more detailed predictive simulations of postural control.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Maarten Afschrift","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1148","unix_group_name":"ngshd","modified":"1473637350","downloads":"0","group_name":"Next Generation Smart Health Device","logo_file":"","short_description":"Description: Next Generation Smart Heath Devices consist of the following functions:\n1. Personalized Wrist Band Data Centre for Healthy Lifestyle (Smart Watch)\n2. Pre-clinical monitoring of movement disorders/abnormalities\n3. Secure Personllaized Perfo","long_description":"Description: Next Generation Smart Heath Devices consist of the following functions:\n1. Personalized Wrist Band Data Centre for Healthy Lifestyle (Smart Watch)\n2. Pre-clinical monitoring of movement disorders/abnormalities\n3. Secure Personllaized Performance Analysis Data Center\n4. Personalized Recovery Progress Analysis and Classification\n5. Secure Sports/Military Personnel Performance Enhancement\n\nThe underlying architecture built to develop smart health system consists of; intelligent knowledge base system, big data analytics and health informatics. These are the key ingredients included in the secure personalized healthcare tools for your healthy lifestyle.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Joko Triloka","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1151","unix_group_name":"biartprosthesis","modified":"1646696107","downloads":"8861","group_name":"Transtibial (below-knee) amputee OpenSim model and Biarticular Prosthesis","logo_file":"biartprosthesis","short_description":"This project has two main aims: \n1) To develop and validate a transtibial amputee model with representative mass, inertial, and muscular properties\n2) To use this model to analyze collected gait data from transtibial amputees walking on a Biarticular Prosthesis prototype ","long_description":"Full details on both the amputee model and biarticular prosthesis can be found in Andrea Willson's Master's thesis (citation below). Components of this work have been submitted to the ASME Journal of Medical Devices, and additional manuscripts are in preparation (As of 10/31/2018).\n\nWillson, A., &amp; Aubin, Patrick M. (2017). A quasi-passive biarticular prosthesis and novel musculoskeletal model for transtibial amputees. Seattle: University of Washington.\n\nhttps://digital.lib.washington.edu/researchworks/handle/1773/40644\n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Patrick  Aubin,Anthony Anderson,Andrea Willson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1155","unix_group_name":"models-2-sta","modified":"1474481297","downloads":"1011","group_name":"Musculoskeletal model sensitivity to soft tissue artefacts","logo_file":"models-2-sta","short_description":"This study quantifies the sensitivity to the soft tissue artefacts of three open-source musculoskeletal models implemented in OpenSim.","long_description":"Musculoskeletal models estimates can be heavily affected by the soft tissue artefact (STA) when input positional data are obtained using stereophotogrammetry. In this study, we assessed the sensitivity to the STA of three open-source musculoskeletal models and relevant tools, implemented in OpenSim. To this purpose, a baseline dataset of marker trajectories was created using point kinematics for each model from experimental data of one healthy volunteer’s gait. Five hundred STA realizations were then statistically generated using a marker-dependent model of the pelvis and lower limb artefact and added to the baseline data. The STA's impact on the musculoskeletal model estimates of an inverse dynamics pipeline (joint angles, joint moments, and muscle and joint contact forces) was finally quantified using a Monte Carlo analysis.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Claudia Mazzà,Giuliano Lamberto,Saulo Martelli","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"1160","unix_group_name":"datatracking","modified":"1583925971","downloads":"860","group_name":"Data-tracking optimization using collocation","logo_file":"datatracking","short_description":"This project contains a package that can be used to perform data-tracking optimization using collocation method. The codes and models are available. The tracking results for one subject during walking and running are also provided.","long_description":"PLEASE CITE THIS PAPER:\nLin, Y.-C. and Pandy, M. G. (2017). THREE-DIMENSIONAL DATA-TRACKING DYNAMIC OPTIMIZATION SIMULATIONS OF HUMAN LOCOMOTION GENERATED BY DIRECT COLLOCATION, Journal of Biomechanics, 59, 1-8. 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The purpose of this study is to investigate if the different inflow cannula angle and position affects blood flow pattern inside LV ","long_description":"Introduction\nThere has been reports on suboptimal LVAD inflow cannula angle and position can cause LVAD pump dysfunction. The purpose of this study is to investigate if the different inflow cannula angle and position affects blood flow pattern inside LV which can potentially contribute to LVAD dysfunction.\nMethod\nWe will compare blood flow pattern inside LV under different inflow cannula angles to mitral valve. Also we will be utilizing deidentified imaging data to characterize the flow pattern difference between patients who developed LVAD dysfunction and patients who didn't develop LVAD dysfunction.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Toshinobu Kazui","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1177","unix_group_name":"alginate","modified":"1511885180","downloads":"14","group_name":"Effect of Algisyl Injection on Porcine Heart Failure Models ","logo_file":"alginate","short_description":"Using experimental pig studies as validation, we simulate and optimize the injection of Algisyl into left ventricles in order to treat heart failure.","long_description":"Heart failure (HF) is a worldwide epidemic that contributes considerably to the overall cost of health care in developed nations. The number of people afflicted with this complex disease is increasing at an alarming pace—a trend that is likely to continue for many years to come. Over 1 million Americans suffer a myocardial infarction (MI) each year and many experience post-MI left ventricular (LV) remodeling, which manifests as progressive changes in LV structure and function. Post-MI LV remodeling is responsible for nearly 70% of all HF cases. Reduction of LV wall stress is considered a cornerstone in the treatment of HF. There are currently no reliable means to directly measure wall stresses in the intact LV. Thus, we rely on FE models, using LS-DYNA, to predict these stresses, knowing that the predictions cannot be validated directly, albeit we can validate deformations. To make our work more accessible to other researchers we are willing to help convert our models to freely available FE software like Continuity (http://www.continuity.ucsd.edu/) and FEBio (https://febio.org/).\n\nA novel promising therapy for HF using intramyocardial injections of alginate to de-stress the heart based on a “micro-LVAD” (LV assist device) mechanism of action was designed computationally, validated pre-clinically, and then validated clinically in the AUGMENT-HF international prospective multi-center trial. The overall goal of our proposed research is to optimize a therapy for HF that involves percutaneous injection of an alginate hydrogel (Algisyl) in the failing myocardium.\n\nWe developed a novel percutaneous large animal (swine) model of ischemic HF. By preconditioning coronary arteries using balloon inflation prior to placing embolism coils two weeks apart, we reduced swine mortality to 10% and generated a realistic model of ischemic cardiomyopathy in large coronary arteries similar to those in patients. Treatment of HF with Algisyl, even without coronary artery bypass grafting (CABG), resulted in sustained improvement of LV contractile function with reduced LV volume. Our method for automatically optimizing intramyocardial injections for treating HF strongly suggests LV contractile function will be further improved if stiffer implants are placed in chronically infarcted LV regions. Additionally, our method for simulating the progression of HF8 strongly suggests that delivering Algisyl in the borderzone of acutely infarcted LV regions can prevent HF progression. \n\nOur studies support the exciting concept that the injection of inert material into the LV free-wall (with or without CABG) is an effective strategy for inducing LV reverse remodeling that improves LV function and results in decreased myofiber stress. Moreover, if this therapy can be delivered percutaneously rather than via the currently used open-heart procedure, this therapy may become revolutionary for HF treatment.  A minimally invasive procedure would be in the best interest of this patient population (i.e., one that cannot tolerate general anesthesia and surgery) and it would be significantly more cost effective than surgery. \n\nWe have developed a novel suction-based catheter device that accurately and precisely delivers the material into the LV subendocardium and prevents any potential embolization of the Algisyl in the ventricle. Our catheter device latches onto the endocardium using a suction cup to ensure injections at the needed sites. This innovation makes it possible to inject material endocardially into the heart wall percutaneously (including the interventricular septum) through a femoral artery access. Furthermore, this approach allows us to test a pre-emptive or preventative strategy for treating acute MI so that ischemic HF does not develop.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"kevin sack,Julius Guccione,gabriel Acevedo-Bolton","trove_cats":[{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1178","unix_group_name":"openeyesim","modified":"1505916552","downloads":"0","group_name":"OpenEyeSim","logo_file":"","short_description":"WE ARE CURRENTLY REFINING OUR MODEL AND BUILDING UP A USER-FRIENDLY INTERFACE. IF YOU WANT TO GET IT NOW WRITE US AN EMAIL: priamikov@fias.uni-frankfurt.de\n\nBiomechanical model of human eye muscles.","long_description":"Biomechanical simulations of eye movements can help us to better understand the underlying problems of visual perception and motor control. Here we introduce OpenEyeSim, a detailed three-dimensional biomechanical model of the human extra-ocular eye muscles including a visualization of a virtual environment. The main purpose of OpenEyeSim is to serve as a platform for developing models of the joint learning of visual representations and eye muscle control in the perception-action loop. The architecture and parameters of OpenEyeSim are based on measurements of the human eye and it allows to simulate realistic types of eye movements.\n\nThis model can be used as stand alone in the OpenSim simulator, which is interesting for the people who are investigating the motor control part. Additionally to the model comes an extension to the OpenSim, which includes fast offscreen rendering + mex library for the matlab. We're also working on the interface for python.\n\nCorresponding paper: http://jov.arvojournals.org/article.aspx?articleid=2594736\n\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Alex Priamikov","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1180","unix_group_name":"scone","modified":"1702572580","downloads":"3565","group_name":"SCONE: Open Source Software for Predictive Simulation","logo_file":"scone","short_description":"&lt;img src=&quot;https://scone.software/scone_window.png&quot; alt=&quot;SCONE&quot;&gt;SCONE is free and open-source software for &lt;b&gt;predictive simulation&lt;/b&gt; of human and animal motion.\n\nWith SCONE, you can define and optimize &lt;b&gt;neuromuscular controllers&lt;/b&gt; to optimally perform a specific task, according to &lt;b&gt;high-level objectives&lt;/b&gt; such as walking speed and energy efficiency.\n\nPlease visit the &lt;a href=&quot;https://scone.software&quot;&gt;SCONE homepage&lt;/a&gt; for more information, or check &lt;a href=&quot;https://scone.software/doku.php?id=changes&quot;&gt;what's new&lt;/a&gt; in the latest version.","long_description":"If SCONE is helpful for your research, please cite the following paper:\n\nGeijtenbeek, T (2019). SCONE: Open Source Software for Predictive Simulation of Biological Motion. Journal of Open Source Software, 4(38), 1421, https://doi.org/10.21105/joss.01421","has_downloads":true,"keywords":"optimization,neuromusculoskeletal simulation,software tools,motion prediction,neuromuscular control,optimal control,predictive simulation,OpenSim","ontologies":"","projMembers":"Christopher Dembia,Frans C.T. van der Helm,Mojtaba Mirakhorlo,Luca Modenese,Niels Waterval,Ruben Boon,Romain Valette,Rahul Ramesh,Carmichael Ong,Sachin Umans,Thomas Geijtenbeek,Saher Jabeen,Eline van der Kruk,Michiel van Lohuijzen,Kirsten Veerkamp,Arend Schwab,Umit Sheombarsing","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular 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prediction.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Samuel Flores,Andrei Rajkovic","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1182","unix_group_name":"pressure","modified":"1480405523","downloads":"0","group_name":"KneeLoad and MRI","logo_file":"","short_description":"Knee contact pressures calculated using an elastic foundation contact model during gait will be uploaded here and made available.","long_description":"Knee contact pressures calculated using an elastic foundation contact model during gait will be uploaded here and made available.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sam Van Rossom","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1183","unix_group_name":"bilateral-stj","modified":"1478188535","downloads":"0","group_name":"Bilateral scapulothoracic joint","logo_file":"bilateral-stj","short_description":"The aim of this project which partly occured during the virtual OpenSim workshop (october 2016), was to create the left side of the upper limb, taking into account the model released by Seth et al in 2016, named Scapulothoracic joint (ellipsoid joint).\n\n","long_description":"The aim of this project which partly occured during the virtual OpenSim workshop (october 2016), was to create the left side of the upper limb, taking into account the model released by Seth et al in 2016, named Scapulothoracic joint (ellipsoid joint).\n\nOpenSim users who are considering bilateral activities (a fortiori asymmetrical activities) may be interested by this model.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Maxime  Bourgain,Samuel Hybois","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1184","unix_group_name":"master-thesis","modified":"1477938212","downloads":"0","group_name":"AnkleProsthesis","logo_file":"","short_description":"Master thesis about a ankle prosthesis in the frontal plane","long_description":"Master thesis about a ankle prosthesis in the frontal plane","has_downloads":false,"keywords":"","ontologies":"","projMembers":"emile moreau","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1187","unix_group_name":"falesprob5","modified":"1478473769","downloads":"0","group_name":"517 Gait Homework","logo_file":"","short_description":"The purpose of this modeling problem is to develop muscle force-angle curves for the rectus\nfemoris, vastus lateralis, and vastus medialis using OpenSim.","long_description":"The purpose of this modeling problem is to develop muscle force-angle curves for the rectus\nfemoris, vastus lateralis, and vastus medialis using OpenSim.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Colten Fales","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1192","unix_group_name":"weeder","modified":"1479150250","downloads":"0","group_name":"Power weeder simulation","logo_file":"","short_description":"The power weeder is being operated by a single person for weeding in the paddy field. The movement of human in the paddy field has to be simulated to reduce the drudgery","long_description":"The power weeder is being operated by a single person for weeding in the paddy field. The movement of human in the paddy field has to be simulated to reduce the drudgery","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Surendrakumar Allimuthu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1193","unix_group_name":"meeg467project","modified":"1479150460","downloads":"0","group_name":"MEEG467","logo_file":"","short_description":"Dynamic Walking Challenge: Go the Distance!","long_description":"Dynamic Walking Challenge: Go the Distance!","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Danielle Gerstman","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1196","unix_group_name":"dva","modified":"1479338201","downloads":"0","group_name":"velocity measurement in DVA","logo_file":"","short_description":"velocity measurement in DVA with MRI with contrast","long_description":"velocity measurement in DVA with MRI with contrast","has_downloads":false,"keywords":"","ontologies":"","projMembers":"carina vallejo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1198","unix_group_name":"safe-delivery","modified":"1479904076","downloads":"0","group_name":"Investigating the effects of pelvic floor muscles during pregnancy","logo_file":"","short_description":"Developing  a risk predictive Model  about how the pelvic floor muscles change during pregnancy and how they stretch during the delivery in order to identify and discover knowledge about these muscles to avoid damage during delivery. Which damage increase the risk of urinary incontinence or pelvic organ prolapse later in life.","long_description":"Developing  a risk predictive Model  about how the pelvic floor muscles change during pregnancy and how they stretch during the delivery in order to identify and discover knowledge about these muscles to avoid damage during delivery. Which damage increases the risk of urinary incontinence or pelvic organ prolapse later in life.","has_downloads":false,"keywords":"Pelvis","ontologies":"Dynamic_Model","projMembers":"Bisaso Samuel","trove_cats":[{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1199","unix_group_name":"1pka","modified":"1479929179","downloads":"0","group_name":"Molecular Dynamics Simulations of Sicarboxilic Acids in Aqueous Solutions","logo_file":"","short_description":"Changes in the protonation and deprotonation\nof dicarboxylic  acid residues in proteins play a key role in many\nbiological processes and pathways. I want to obtain \nof the free-energy profile for the protonation−deprotonation\nreaction of the  dicarb","long_description":"Changes in the protonation and deprotonation\nof dicarboxylic  acid residues in proteins play a key role in many\nbiological processes and pathways. I want to obtain \nof the free-energy profile for the protonation−deprotonation\nreaction of the  dicarboxylic acids in aqueous solutions\nusing ab initio Car−Parrinello molecular dynamics simulations\ncoupled with metadynamics sampling and also with clasical MD.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"miroslava  nedyalkova","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1200","unix_group_name":"va-dxamoi","modified":"1602251267","downloads":"46","group_name":"VA - Density-Weighted Moment of Inertia from DXA scans (VA-DXAMOI)","logo_file":"","short_description":"This project contains the code used to determine the density-weighted moment of inertia (MOI) from a DXA scan. We expect the MOI will correlate better with fracture strength and fracture risk than Bone Mineral Density of Bone Mineral Content. VA-DXAMOI is specifically developed for the distal femur.\n\nPlease read the description or README for important information regarding the code package.","long_description":"     VA-DXAMOI is a Matlab-based software package used to calculate the density-weighted moment of inertia from a DXA scan of cross-sections perpendicular to the longitudinal axis of the femur. MATLAB is required to use the software. VA-DXAMOI was developed to process raw data acquired from a Hologic QDR-1000W pencil-beam scanner. At this point in time, the software will not work with data acquired using scanners made by GE/Lunar, Norland or others manufacturers, nor with data acquired using Hologic fan beam scanners. The software may work with data acquired using a Hologic QDR-2000 pencil-beam scanner, but we have not confirmed that ourselves. Also note that VA-DXAMOI was validated using scans of the distal half of human cadaver femurs scanned in a water bath to simulate the presence of soft tissue. The software should work for other human and animal long bones, although validation studies for other bones have not yet been conducted. While the software has only been validated from in vitro scans, we believe it will also work for in vivo scanning. Note that if scanning the distal femur in vivo, the section to be analyzed must not include any portion of the patella. The software should work equally well for cross-sections from just proximal to the patella up to the lesser trochanter.\n\n     1.\tWhile every attempt has been made to eliminate errors in programming and logic, we do not guarantee the accuracy of this code. Any errors brought to our attention will be corrected when future versions of VA-DXAMOI are developed.\n\n     2.\tWhile we support distribution of this software, we can not be responsible for the integrity of the source code. The most current version of the software can be obtained at https://simtk.org/projects/va-dxamoi\n\n     3.\tIf you modify the source code in any way, please clearly indicate (with comments in the code and in the documentation) exactly what you have changed. If you believe your addition may help other researchers, please let us know about it. If appropriate, we will include it (with proper credit) in future versions of VA-DXAMOI.\n\n     4.\tIf you publish or present any results obtained with the help of VA-DXAMOI, we ask that you acknowledge its use. We ask that you reference the following article:\n\nBaker AM, Wagner DW, Kiratli BJ, Beaupre GS: Pixel-Based DXA-Derived Structural Properties Strongly Correlate with pQCT Measures at the One-Third Distal Femur Site. Annals of Biomedical Engineering, In Press.\n\n     5.\tFinally, if you have any suggestions or comments about VA-DXAMOI, please let us know.\n\nVA-DXAMOI Support Group\nMusculoskeletal Research Laboratory (MC 153)\nVA Palo Alto Health Care System\n3801 Miranda Avenue\nPalo Alto, CA 94304-1200","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Gary Beaupre,Alexander Baker","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1204","unix_group_name":"tc-ak-inhib","modified":"1480536223","downloads":"0","group_name":"Inhibitors of Arginine Kinase in Trypanosoma cruzi","logo_file":"","short_description":"Searching for molecules capable of inhibiting activity of Arginine Kinase in Trypanosoma cruzi, the causative agent of Chagas Disease.","long_description":"Searching for molecules capable of inhibiting activity of Arginine Kinase in Trypanosoma cruzi, the causative agent of Chagas Disease.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Edward Valera","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1212","unix_group_name":"cp-child-gait","modified":"1654892418","downloads":"289","group_name":"Gait data children with spastic CP and typically developing children","logo_file":"","short_description":"This data contains total body plug in gait markerset during walking at a preferred walking speed in children with cerebral palsy (hemiplegia n=5, 9.00 ± 2.28; diplegia n=4, age 10.50 ±1.66) and typically developing children (n=5, age 8.40 ± 1.50).","long_description":"This data contains total body plug in gait markerset during walking at a preferred walking speed in children with cerebral palsy (hemiplegia n=5, 9.00 ± 2.28; diplegia n=4, age 10.50 ±1.66) and typically developing children (n=5, age 8.40 ± 1.50).\nThey were ambulant (without walking aids), were diagnosed with the predominantly spastic type of CP, and had sufficient cooperation to follow verbal instructions. They did not receive lower limb surgery or did not undergo Botulinum Toxin treatment within the past 6 months.\nGround reaction forces were measured using two force plates (AMTI, Watertown, MA) embedded in the 10m walkway.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Pieter Meyns","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1219","unix_group_name":"rems","modified":"1562590246","downloads":"2357","group_name":"Real-Time EMG-informed Modeling Tools","logo_file":"","short_description":"This project aims to develop a real-time computational framework for modeling and simulating human musculoskeletal function using muscle electromyography (EMG), foot-ground reaction forces (GRF) and joint position from motion capture systems.","long_description":"This project aims to develop a real-time computational framework for modeling and simulating human musculoskeletal function using muscle electromyography (EMG), foot-ground reaction forces (GRF) and three-dimensional marker trajectories from motion capture systems. The framework will combine the Calibrated EMG-Informed Neuromusculoskeletal Modelling Toolbox (CEINMS) (https://simtk.org/projects/ceinms), the Multidimensional Cubic B-Spline method (https://simtk.org/projects/mcbs) and the inverse kinematics/dynamics functionalities of the OpenSim software (https://simtk.org/projects/opensim).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Guillaume Durandau,Massimo Sartori","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1220","unix_group_name":"md3t0t","modified":"1483568579","downloads":"0","group_name":"3t0t","logo_file":"","short_description":"MD Simulation of 3t0t","long_description":"MD Simulation of 3t0t","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Cagla Ergun","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1221","unix_group_name":"props","modified":"1509401545","downloads":"14","group_name":"PROPS: PRobabilistic Pathway Score","logo_file":"","short_description":"This R package calculates PRObabilistic Pathway Scores (PROPS), which are pathway-based features, from gene-based data.","long_description":"For more details, please refer to the following paper:\n\nLichy Han, Mateusz Maciejewski, Christoph Brockel, William Gordon, Scott B. Snapper, Joshua R. Korzenik, Lovisa Afzelius, Russ B. Altman. A PRObabilistic Pathway Score (PROPS) for Classification with Applications to Inflammatory Bowel Disease.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Lichy H","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1229","unix_group_name":"todtest200","modified":"1484616596","downloads":"0","group_name":"todhingtest","logo_file":"","short_description":"This is a test project","long_description":"This is a test project","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Tod Hing","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1232","unix_group_name":"elbowtransfer","modified":"1491953116","downloads":"8","group_name":"Elbow reconstruction biomechanics in tetraplegia","logo_file":"elbowtransfer","short_description":"Through this project, we share and summarize experimental joint moments, voluntary activation, and corticomotor excitability data from individuals with tetraplegia and tendon transfer, as well as models and simulations to study the upper extremity following elbow reconstruction.","long_description":"Using neural stimulation techniques, we have quantified voluntary activation and corticomotor excitability of surgically transferred muscle in individuals with quadriplegia resulting from SCI. We are using the Simtk forum to share the data we collected. Our publications should be referenced for detailed information regarding our objectives, subjects, methods, and results. Here, we provide a summary of our work. \n\nWe found that maximum voluntary activation (percentage of the motorneuron pool that can be voluntarily recruited during maximum effort) is more complete in individuals with quadriplegia who have undergone biceps-to-triceps transfer relative to those who have undergone posterior deltoid-to-triceps transfer. Both the biceps-to-triceps and the posterior deltoid-to-triceps transfers are surgical procedures that enable active elbow extension via reassignment of a nonparalyzed donor muscle (biceps or deltoid) to the insertion of the paralyzed triceps. The greater voluntary activation after biceps transfer we found in functionally relevant postures augmented the greater force generating capacity of the biceps muscle, leading to increased post-surgery elbow extension strength relative to when the posterior deltoid is transferred. In another study, using  transcranial magnetic stimulation to non-invasively stimulate the motor cortex, we found that posture-dependent excitability of the corticomotor pathway to the biceps is altered following SCI and surgical transfer relative to the nonimpaired biceps. Thus, if initial training of the transferred biceps is facilitated in postures that correspond to greatest corticomotor excitability, training postures cannot be based on the excitability of the nonimpaired biceps. ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Carrie Peterson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1233","unix_group_name":"tricfd","modified":"1490693581","downloads":"0","group_name":"CFD analysis of Arterial flow in Thromboembolism","logo_file":"tricfd","short_description":"Drawing inputs from Bio-medical Imaging datasets (Both of CT-Angio/ MRI/ IVUS)*\n* Computed Tomography Angiography;\n* Magnetic Resonance Imaging;\n* Intra - Vascular Ultra Sound (of Athero-sclerotic Plaques).\n\n as well as to Compute the Hemodynamic parameters and thus derive an Integrated Model upon CFD simulation of Thromboembolism in Human Heart (Coronary/ Carotid artery); whereby we visualize and Quantify the arterial flow in Laminar/ Turbulent/ Eddy regimes across a broad range allowed by ranges permissible by Multi-Variate Analysis of these \"52\" CFD numbers, coupled with Hemodynamic parameters deviating from Normal values- attempting to link both Theoretical Number Theory and Applied Numerical Methods in this Pursuit in the Cardio-Vascular Context, thereof.","long_description":"To evaluate &quot;52&quot; dimensionless CFD numbers (akin to 'deck' of French-Playing cards):- \n# Reynolds number,\n# Sherwood number,\n# Schimdt number,\n# Rayleigh number,\n# Weber number,\n# Capillary number,\n# Bond number,\n# Froude number,\n# Nusselt number,\n# Peclet number (for Mass diffusivity),\n# Peclet number (for Heat diffusivity),\n# Prandtl number,\n# Grashof number, and\n# Brinkman number,\n# Cavitation number,\n# Stanton number,\n# [Mass -Transfer] Stanton number,\n# Eckert number,\n# Knudsen number,\n# Graetz number,\n# Lewis number,\n# Mach number,\n# Poiseuille number,\n# Rossby number,\n# Strouhal number; and\n# Taylor number,\n# Archimedes number,\n# Arrhenius number,\n# Bingham number,\n# Biot number,\n# [Mass-Transfer] Biot number,\n# Blake number,\n# Bondenstein number,\n# Cauchy number,\n# Coefficient of Frication (dimensionless number),\n# Condensation number,\n# Dean number,\n# Drag-coefficient (dimensionless number),\n# Elasticity number,\n# Etovos number,\n# Euler number,\n# Fourier number,\n# [Mass-Transfer] Fourier number,\n# Friction factor (dimensionless number),\n# Galileo number,\n# Colburn &quot;j&quot; (Heat) factor,\n# Colburn &quot;j&quot; (Mass) factor,\n# Hodgson number,\n# Jakob number,\n# Ohnesorge number,\n# Pipeline parameter (dimensionless number),\n# Power number [possibly of 3D-printed Thrombotic human heart].\n\nIdeally, we would very much like to Extend this &quot;Wolfram Mathematica-11 Demonstration&quot; under the simplistic consideration of a Single &quot;Spherical Thromb&quot;, merely beyond the Re= Reynolds number - to ALL of the &quot;52&quot; CFD-'deck' numbers immediately post-Plaque Fissure around the instance of &quot;Thrombotic-Thrombolytic Equilibrium&quot; involved in Coronary Arterial flow.\n\nDEMO:\n- Mikhail Dimitrov Mikhailov\n&quot;Flow around a Sphere at Finite Reynolds Number by Galerkin Method&quot;\nhttp://demonstrations.wolfram.com/FlowAroundASphereAtFiniteReynoldsNumberByGalerkinMethod/\nWolfram Demonstrations Project\nPublished: January 2, 2013\n\nREFERENCES:\n[0] Coronary Plaque Disruption\nErling Falk, Prediman K. Shah, Valentin Fuster\nhttps://doi.org/10.1161/01.CIR.92.3.657\nCirculation. 1995;92:657-671\nOriginally published August 1, 1995.\n\n[1] Lagrangian wall shear stress structures and near-wall transport in high-Schmidt-number aneurysmal flows.\nAmirhossein Arzani (a1), Alberto M. Gambaruto (a2), Guoning Chen (a3) and Shawn C. Shadden (a1) \n(a1) Mechanical Engineering, University of California Berkeley, Berkeley, CA 94720, USA\n(a2) Mechanical Engineering, University of Bristol, University Walk, Bristol BS8 1TR, UK\n(a3) Computer Science, University of Houston, Houston, TX 77204, USA\nhttps://doi.org/10.1017/jfm.2016.6\n\n[2] A reduced-dimensional model for near-wall transport in cardiovascular flows.\nKirk B. Hansen* , Shawn C. Shadden*\n*Department of Mechanical Engineering, University of California, Berkeley, CA, USA.\nPMID: 26298313 PMCID: PMC4764478 DOI: 10.1007/s10237-015-0719-4\n\n^WIKI:\nhttps://en.wikipedia.org/wiki/Dimensionless_numbers_in_fluid_mechanics\n\n$OPEN ACCESS IMAGING DATASETS:\nhttps://grand-challenge.org/\n\n@OUR LAB HOMEPAGE:\nhttp://www.triindia.org/\n\n%RESOURCES:\nhttp://www.cfd.life/\nhttps://cfd.direct/\n\n+CERTIFICATIONS:\nhttps://onlinecourses.nptel.ac.in/noc17_ee01/preview\nhttps://onlinecourses.nptel.ac.in/noc17_ch01/preview\n\n~Inspiration: &quot;CAF&quot; (Cellular Automaton Fluids: Wolfram, 1986).\nhttp://www.stephenwolfram.com/publications/cellular-automata-complexity/pdfs/cellular-automaton-fluids-theory.pdf","has_downloads":false,"keywords":"cfd, hemodynamics, thrombosis","ontologies":"","projMembers":"Praharshit Sharma","trove_cats":[{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"1234","unix_group_name":"ims-iom","modified":"1485194403","downloads":"0","group_name":"3D model of lower leg fascia","logo_file":"","short_description":"Aim of the project is to build a 3 dimensional model of the bone-fascia system of the lower leg.\nThe first \"layer\" in this model should be graphics of the bones and joints , second layer graphics of the connection between fascia and bone (intermuscular s","long_description":"Aim of the project is to build a 3 dimensional model of the bone-fascia system of the lower leg.\nThe first &quot;layer&quot; in this model should be graphics of the bones and joints , second layer graphics of the connection between fascia and bone (intermuscular septa and interosseous membrane , third layer graphics of the deep fascia o the leg, fourth layer graphics of  the superficial fascia.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Christoph Rossmy","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1239","unix_group_name":"emfasa","modified":"1486930991","downloads":"3884","group_name":"EMfasa","logo_file":"","short_description":"EMfasa is a package to automatically model protein structures into density maps obtained from cryo-EM or X-ray crystallography.  It performs separate modeling steps from building a backbone model, fragment fitting,  fragment assembly, and sequence assignment. ","long_description":"EMfasa is a package to automatically model protein structures into density maps obtained from cryo-EM or X-ray crystallography.  It performs separate modeling steps from building a backbone model, fragment fitting,  fragment assembly, and sequence assignment. ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Gunnar Schroeder","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1244","unix_group_name":"aascnn","modified":"1502475050","downloads":"2517","group_name":"3D Deep CNN for Amino Acid Environment Similarity Analysis","logo_file":"","short_description":"","long_description":"Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purposed but not optimized for the specific problem of interest. \nIn this project, we develop a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure.  To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Wen Torng","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1245","unix_group_name":"htsnp-pca","modified":"1708779621","downloads":"13","group_name":"htSNP finder - PCA Based Methods","logo_file":"htsnp-pca","short_description":"Methods for selecting haplotype tagging SNPs (htSNPs) using Principal Components Analysis (PCA).","long_description":"The immense volume and rapid growth of human genomic data, especially single nucleotide polymorphisms (SNPs), present special challenges for both biomedical researchers and automatic algorithms. One such challenge is to select an optimal subset of SNPs, commonly referred as &quot;haplotype tagging SNPs&quot; (htSNPs), to capture most of the haplotype diversity of each haplotype block or gene-specific region. This information-reduction process facilitates cost-effective genotyping and, subsequently, genotype-phenotype association studies. It also has implications for assessing the risk of identifying research subjects on the basis of SNP information deposited in public domain databases. We have investigated methods for selecting htSNPs by use of principal components analysis (PCA). These methods first identify eigenSNPs and then map them to actual SNPs. We evaluated two mapping strategies, greedy discard and varimax rotation, by assessing the ability of the selected htSNPs to reconstruct genotypes of non-htSNPs. We also compared these methods with two other htSNP finders, one of which is PCA based. We applied these methods to three experimental data sets and found that the PCA-based methods tend to select the smallest set of htSNPs to achieve a 90% reconstruction precision.","has_downloads":true,"keywords":"htSNP,PCA","ontologies":"","projMembers":"Russ Altman,Zhen Lin","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"1246","unix_group_name":"htsnp-fsfs","modified":"1488421976","downloads":"2","group_name":"htSNP finder - feature selection","logo_file":"","short_description":"A major challenge for genome-wide disease association studies is the high cost of genotyping large number of single nucleotide polymorphisms (SNP). The correlations between SNPs, however, make it possible to select a parsimonious set of informative SNPs, ","long_description":"A major challenge for genome-wide disease association studies is the high cost of genotyping large number of single nucleotide polymorphisms (SNP). The correlations between SNPs, however, make it possible to select a parsimonious set of informative SNPs, known as “tagging” SNPs, able to capture most variation in a population. Considerable research interest has recently focused on the development of methods for finding such SNPs. In this paper we present an efficient method for finding tagging SNPs. The method does not involve computation-intensive search for SNP subsets but discards redundant SNPs using a feature selection algorithm. In contrast to most existing methods, the method presented here does not limit itself to using only correlations between SNPs in local groups.  By using correlations that occur across different chromosomal regions, the method can reduce the number of globally redundant SNPs. Experimental results show that the number of tagging SNPs selected by our method is smaller than by using block-based approaches.","has_downloads":true,"keywords":"htSNP,Machine Learning","ontologies":"","projMembers":"Russ Altman,Zhen Lin,Phuong Tu","trove_cats":[{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1251","unix_group_name":"softpro","modified":"1489358393","downloads":"0","group_name":"Genetic predisposition to soft tissue sarcomas","logo_file":"","short_description":"We intend to use the genotype data of 400 patients with soft tissue sarcomas and 600 control subjects to identify loci of predisposition to soft tissue sarcomas. Genotype data regard clock related genes.","long_description":"We intend to use the genotype data of 400 patients with soft tissue sarcomas and 600 control subjects to identify loci of predisposition to soft tissue sarcomas. Genotype data regard clock related genes.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"simone mocellin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1255","unix_group_name":"pataky","modified":"1489771715","downloads":"0","group_name":"Load applied to ACL by non-planar motion of knee joint during bicycle movement","logo_file":"","short_description":"The knee joint during bicycle movement is non-planar motion.\nSince the cause of ACL damage is non-planar motion of the knee joint, consider how much load is applied to the ACL during bicycle exercise.\nIt is based on the rotation angle data of the knee j","long_description":"The knee joint during bicycle movement is non-planar motion.\nSince the cause of ACL damage is non-planar motion of the knee joint, consider how much load is applied to the ACL during bicycle exercise.\nIt is based on the rotation angle data of the knee joint during the bicycle movement obtained from the experimental result.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Nagaya Toshinari","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1258","unix_group_name":"abm-dmd","modified":"1566924053","downloads":"0","group_name":"Agent-based model of Duchenne muscular dystrophy","logo_file":"","short_description":"Agent-based model of inflammatory cells, satellite cells, and fibroblasts to study disease mechanisms in Duchenne muscular dystrophy.","long_description":"This study developed an agent-based model (ABM) of skeletal muscle damage and regeneration in order to investigate the cellular mechanisms of disease in Duchenne muscular dystrophy (DMD). Then model was used to probe the role of these mechanisms first in isolation, and then collectively in computational models of the mdx mouse, the most commonly used mouse model of DMD. The project includes both model output and source code for the ABM.\n\nThe model was built in Repast, a Java based modeling platform.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kelley Virgilio","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1260","unix_group_name":"circadian-gaba","modified":"1491672529","downloads":"0","group_name":"Multiscale Modeling of the Mammalian Circadian Clock: The Role of GABA Signaling","logo_file":"","short_description":"The goal of this project is to develop a multiscale model of the mammalian circadian clock  and to integrate this model with targeted experiments and novel computational tools to gain improved understanding of clock connectivity, synchronization and entrainment properties.","long_description":"The synchronization and entrainment of coupled biological oscillators is an emerging research area in complex network systems. The mammalian circadian clock located in the suprachiasmatic nucleus (SCN) of the hypothalamus consists of approximately 20,000 pacemaker neurons that are coupled together to produce a robust overall rhythm that drives other bodily functions such as sleep patterns. The SCN represents an ideal model system for studying biological network design and behavior due to accumulating data on individual SCN neurons and their interactions. Experimental studies have shown that SCN intercellular communication is primarily mediated by two neurotransmitters: vasoactive intestinal peptide (VIP) and gamma-aminobutyric acid (GABA). While VIP is well established as an essential synchronizing agent, the role of GABA with respect to its inhibitory/excitatory, day/night, synchronizing and entrainment effects remains controversial. Improved understanding of neurotransmitter mediated intercellular signaling in the SCN will have important clinical implications for prevention and treatment of circadian rhythm disruptions, including mood and sleep disorders and metabolic diseases.\n\nThe goal of this project is to develop a multiscale model of the SCN and to integrate this model with targeted experiments and novel computational tools to gain improved understanding of SCN connectivity, synchronization and entrainment properties. The research focuses on GABA signaling because its role in the SCN is prominent, not well understood, and recent advances by the three participating investigators will enable a complete and careful dissection of the role of this common neurotransmitter with synapse-level resolution across large arrays of circadian neurons. The multiscale model will establish a link between core clock genes and ion channels at the individual cell level and network synchronization and entrainment behavior at the SCN tissue level through cell-to-cell connectivity. Targeted experiments will be performed to inform the construction and validate the predictions of the network model. General computational techniques for model reduction and efficient simulation of heterogeneous cellular networks will be developed to facilitate analysis of model behavior over a wide range of environmental conditions. The research has the potential to be highly transformative by both advancing the multiscale modeling of coupled oscillators/complex networks and by fundamentally changing our understanding of GABA signaling in circadian timekeeping and potentially in other brain regions.","has_downloads":false,"keywords":"circadian, computational neuroscience, network model","ontologies":"","projMembers":"Thomas Bertalan,Natthapong Sueviriyapan,Michael Henson,Erik Herzog,Yannis Kevrekidis","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"1261","unix_group_name":"thresholddetect","modified":"1490717439","downloads":"0","group_name":"Predicting metabolic thresholds from heart rate variability","logo_file":"","short_description":"This project provides the heart rate, ventilatory, and metabolic data necessary to implement and test an algorithm designed to automatically predict the first and second ventilatory thresholds from heart rate variability.","long_description":"This project provides the heart rate, ventilatory, and metabolic data necessary to implement and test an algorithm designed to automatically predict the first and second ventilatory thresholds from heart rate variability.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Amy Silder","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1262","unix_group_name":"snap","modified":"1507738886","downloads":"0","group_name":"SNAP: Stanford Network Analysis Project","logo_file":"","short_description":"Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks","long_description":"Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. SNAP is optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation. It has been used in a variety of biological studies, including the study of genes-drugs association, the examination of C. elegans’ neuronal network, and the analysis of the impact of social networks from a smartphone app on physical activity levels. To learn more or to download, visit http://snap.stanford.edu.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Joy Ku","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1263","unix_group_name":"snorkel","modified":"1551803063","downloads":"39228","group_name":"Snorkel","logo_file":"","short_description":"Snorkel is an open-source system that generates training data for information extraction systems, also known as predictive systems.","long_description":"Snorkel is an open-source system that introduces a new approach for rapidly creating, modeling, and managing data for training predictive systems. It is currently focused on accelerating the development of structured or &quot;dark&quot; data extraction applications for domains in which large labeled training sets are not available or easy to obtain. Examples include biomedical literature and clinical notes. Initial results show that Snorkel with its use of weakly labeled, noisy training data can achieve the same performance as fully supervised learning approaches with “gold standard” labeled training data.\n\nSnorkel has applicability in many domains. Example biomedical domains where Snorkel is being used include the microbiome, joint replacements, and cancer. To learn more or to download, visit http://snorkel.stanford.edu.\n\nTo view recordings, slides, and other materials from the July 2017 workshop, click Downloads.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Joy Ku,Jacqueline Tran","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1264","unix_group_name":"extracredit","modified":"1490823885","downloads":"0","group_name":"Musculoskeletal Modeling Extra Credit","logo_file":"","short_description":"Tutorial One: Musculoskeletal Modeling for ME/BME4501 Extra Credit","long_description":"Tutorial One: Musculoskeletal Modeling for ME/BME4501 Extra Credit","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Olivia Steen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1267","unix_group_name":"thoracic-spine","modified":"1491113062","downloads":"687","group_name":"Articulated Kinematic Model of the Thoracic and Lumbar Spine","logo_file":"thoracic-spine","short_description":"Development of an articulated lumbar and thoracic spine model\nsuitable for kinematic analysis. The model uses the lumbar model by Christophy et al\nas a base, but has added custom joints to the thoracic spine segments. Christophy’s model has been modif","long_description":"Development of an articulated lumbar and thoracic spine model\nsuitable for kinematic analysis. The model uses the lumbar model by Christophy et al\nas a base, but has added custom joints to the thoracic spine segments. Christophy’s model has been modified in the following way:\n\n- Separated the thoracic vertebrae in the torso as separate bodies and added custom\ninter-vertebral joints.\n- Applied ROM constraints for each inter-vertebral joint using a meta-analysis of\ndata from the literature.\n- Positioned the joints using the Instantaneous Axis of Rotation (IAR) from the\nliterature.\n- Replaced the pelvis weld joint to the ground with a custom joint that allows\ntranslation in 3D space.\n- Removed bodies that are not required for our study (e.g. Rib Cage).\n- Added model markers on key bony landmarks such as the pelvis and spinous\nprocesses for scaling and IK purposes.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Gino Coates","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1268","unix_group_name":"amberfb15","modified":"1505983373","downloads":"3","group_name":"AMBER-FB15 data repository","logo_file":"","short_description":"Quantum chemistry and ForceBalance data files used in the optimization of the AMBER-FB15 protein force field.","long_description":"Quantum chemistry and ForceBalance data files used in the optimization of the AMBER-FB15 protein force field.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Lee-Ping Wang","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1275","unix_group_name":"kdb","modified":"1525819550","downloads":"127","group_name":"Computational Analysis of Kinase Selectivity using Structural Knowledge","logo_file":"kdb","short_description":"Kinases play a significant role in diverse signaling pathways and up-regulations of kinases activity has been implicated in multitude of diseases such as cancers. Consequently, understanding kinase selectivity and target-mediated toxicity remains a top priority for kinase inhibitor design. ","long_description":"Here, we present a knowledge-based approach to profile kinase selectivity based on the similarity between drug binding microenvironments. To allow large-scale kinase site similarity profiling, we have created a kinome structure database consisting of 5000 inhibitor-binding pockets from 187 unique human kinase crystal structures. 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enzyme.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Victor Bolanos-Garcia","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1283","unix_group_name":"rra-opt-gui","modified":"1551734354","downloads":"330","group_name":"RRA Optimization MATLAB GUI","logo_file":"","short_description":"A MATLAB based graphical user interface (GUI) has been developed that uses numerical optimization to determine the optimal task weights for use with the Residual Reduction Algorithm (RRA) in OpenSim.","long_description":"This is a MATLAB based graphical user interface (GUI) that implements the Particle Swarm Optimization (PSO) and Simplex Simulated Annealing (SIMPSA) numerical optimization algorithms that can be used to determine optimal task weights during residual reduction algorithm (RRA) in OpenSim. The GUI allows the user to easily select the necessary OpenSim files for use in RRA and will determine the optimal task weights needed to reduce the residual forces and moments applied to musculoskeletal model during the simulated motion.\n\nThe GUI is compatible with MATLAB versions 2013 and higher.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Michael Samaan,Stacie Ringleb,Joshua Weinhandl,Sebastian Bawab","trove_cats":[{"id":"1001","fullname":"OpenSim"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1284","unix_group_name":"trex2005","modified":"1569049023","downloads":"65","group_name":"Musculoskeletal model of the hindlimb of Tyrannosaurus rex","logo_file":"trex2005","short_description":"This project consists of the SIMM (+OpenSim) model files for the MOR555 T. rex specimen (now at Smithsonian) used in the paper cited below.","long_description":"This project consists of the SIMM model files for the MOR555 T. rex specimen (now at Smithsonian) used in the following paper:\nHutchinson, J.R., Anderson, F.C., Blemker, S., Delp, S.L. 2005. Analysis of hindlimb muscle moment arms in Tyrannosaurus rex using a three-dimensional musculoskeletal computer model. Paleobiology 31:676-701. doi: 10.1666/04044.1\n\nIt includes these files:\n1) A &quot;bones&quot; folder with .ASC format polygonal meshes at low resolution, as described in the above paper. Additional left leg files were generated as well as the third toe.\n2) .jnt and .msl files (&quot;trex J42&quot; and &quot;trex M42&quot;) that are used by proprietary SIMM software (version 7.0) to run the model; these can be opened as text files in typical text applications.\n3) A snapshot of the SIMM model.\n\nNote that the SIMM model has undergone minor changes from the 2005 paper's model, but these are fairly trivial (except as noted above).\n\nMore info: http://simtk-confluence.stanford.edu:8080/display/OpenSim/Tyrannosaurus+Rex+Model\n\nAlso available at: https://figshare.com/articles/_/4982492\n\nOpenSim model has now been added (2019).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"John Hutchinson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1286","unix_group_name":"therefmodel","modified":"1699741029","downloads":"0","group_name":"The Reference Model for Disease Progression","logo_file":"therefmodel","short_description":"The Reference Model describes disease complications in a population. \nIt is: \n•\tAn ensemble model assembling multiple other models\n•\tA league of disease models that compete and cooperate\n•\tA validation model\n•\tA medical knowledge accumulator\n","long_description":"The Reference Model is now:\n•\t&lt;a href=&quot;http://dx.doi.org/10.7759/cureus.9455&quot; target=&quot;_blank&quot;&gt;  COVID-19 model for US states and territories &lt;/a&gt;\n•\t&lt;a href=&quot;https://jacob-barhak.github.io/InteractivePoster_MSM_IMAG_2019.html&quot; target=&quot;_blank&quot;&gt;  The Most Validated Cardiovascular (CVD) Diabetes Model known  &lt;/a&gt;\n•\t&lt;a href=&quot;https://patents.google.com/patent/US20140297241A1/en&quot; target=&quot;_blank&quot;&gt;  United States Patent 9,858,390&lt;/a&gt;  \n•\t &lt;a href=&quot;https://patents.google.com/patent/US20170286627A1/en&quot; target=&quot;_blank&quot;&gt;  United States Patent Number 10,923,234&lt;/a&gt;  \n\n\nThe Reference Model can now:\n•\tAttempt to explain COVID-19  for US states\n•\tDetermine CVD models that significantly behave better on several diabetic populations\n•\tDeduce that CVD probability halves every 5 years due to medicine improving -  according to information from the last 3 decades\n•\tCalculate life tables for diabetics\n•\tInterface with ClinicalTrials.Gov\n•\tInclude human interpretation in the model\n•\tCreate an interactive map of our &lt;a href=&quot;https://jacob-barhak.github.io/InteractivePoster_MSM_IMAG_2019.html&quot; target=&quot;_blank&quot;&gt; &lt;b&gt; cumulative computational knowledge gap&lt;/b&gt;\n\n\n&lt;a href=&quot;http://dx.doi.org/10.7759/cureus.9455&quot; target=&quot;_blank&quot;&gt; &lt;b&gt; COVID-19 MODEL&lt;/b&gt;  &lt;/a&gt;  \n\nThe interactive plot below shows our cumulative knowledge gap by showing the error in the vertical axis for US states and territories listed on the horizontal axis. Circles at the bottom have a better fit between observed COVID-19 results and model results. Results are for normalized population of size 10,000 individuals. Hover over the circles to see additional details about each state. The slider determines the model optimization iteration. User can explore the map by changing size and color attributes. \n\n\n&lt;iframe width=&quot;1000&quot; height=&quot;400&quot; src=&quot;https://jacob-barhak.netlify.app/thereferencemodel/results_covid19_2020_06_27/populationplot&quot; frameborder=&quot;0&quot; &gt; &lt;/iframe&gt;\n\n&lt;a href=&quot;https://simtk.org/projects/mist&quot; target=&quot;_blank&quot;&gt; &lt;b&gt; TECHNOLOGY  &lt;/b&gt; &lt;/a&gt; \n\nThe Reference Model is a good way to cross reference information to find out pieces of information and assumptions that fit together, and allow competition against accumulated known data to guide our perception. High Performance Computing is a key to those capabilities and it provided using capabilities of the  &lt;a href=&quot;https://simtk.org/projects/mist&quot; target=&quot;_blank&quot;&gt; MIcro Simulation Tool (MIST)   &lt;/a&gt; .\n \nMIST also provides advance population generation techniques using Evolutionary computation. The Reference Model uses publicly available data such as clinical trial publications. This allows it to access more information since it allows accessing data that otherwise will be restricted from sharing. The Reference Model has an interface that allows it to read information from &lt;a href=&quot;https://clinicaltrials.gov/&quot; target=&quot;_blank&quot; &gt; ClinicalTrials.Gov&lt;/a&gt; while maintaining tractability and reproducibility.\n\n&lt;b&gt; &lt;a href=&quot;https://simtk.org/plugins/simtk_news/index.php?group_id=1286&quot; target=&quot;_blank&quot;&gt; PUBLICATIONS:  &lt;/a&gt; &lt;/b&gt;\nThe Reference Model was created in 2012 and evolved since then. You can find key developments and publications by year in the  &lt;a href=&quot;https://simtk.org/plugins/simtk_news/index.php?group_id=1286&quot; target=&quot;_blank&quot;&gt; news section   &lt;/a&gt;.\n\nHere are some videos describing the Model:\n\nThis video gives a brief introduction\n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/s9L-qFF84Ew&quot; title=&quot;The Reference Model for Disease Progression: Explaining COVID-19&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video will shows recent results of explaining  COVID-19 using USA data:\n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/1M645o5gWrc&quot; title=&quot;YouTube video player&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video will show a breakthrough of becoming the first multiscale ensemble model for COVID-19:\n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/-z8N40TdKDk?start=1860&quot; title=&quot;YouTube video player&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video explains the model in a larger context as presented in AnacondaCon 2019: \n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/fQIYMf5wKGE&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video explains how human interpretation can be used as  presented in the Multiscale Viral Pandemics working group webinar: \n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/aTB8-XEZheU&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis video summarizes a decade of work as presented in PyTexas 2017: \n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/Pj_N4izLmsI?list=PL0MRiRrXAvRiwQUUwTTh5g8rhbQyYlubo&quot; frameborder=&quot;0&quot; gesture=&quot;media&quot; allow=&quot;encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;\n       \nThis describes the evolution of the model up to 2016 presented in PyTexas:\n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/htGRRjia-QQ&quot; frameborder=&quot;0&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nThis describes the work presented in PyData in 2014: \n&lt;iframe width=&quot;800&quot; height=&quot;450&quot; src=&quot;https://www.youtube.com/embed/vyvxiljc5vA&quot; frameborder=&quot;0&quot; allowfullscreen&gt;&lt;/iframe&gt;","has_downloads":false,"keywords":"Ensemble Model , Diabetes, Heart Disease, Economic Model","ontologies":"","projMembers":"Jacob Barhak","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"401","fullname":"Web Site"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":false},{"group_id":"1287","unix_group_name":"velociraptor","modified":"1569048625","downloads":"0","group_name":"Velociraptor hindlimb musculoskeletal model","logo_file":"velociraptor","short_description":"This folder consists of the SIMM(+OpenSim) model files for the IGM985/986 Velociraptor mongoliensis specimen (housed at AMNH at the time) used in the book chapter cited below.","long_description":"This folder consists of the SIMM model files for the IGM985/986 Velociraptor mongoliensis specimen (housed at AMNH at the time) used in the following book chapter:\nHutchinson, J.R., Miller, C.E., Fritsch, G., Hildebrandt, T. 2008. The anatomical foundation for multidisciplinary studies of animal limb function: examples from dinosaur and elephant limb imaging studies.\nIn: Frey R., Endo, H. (eds.), Anatomical Imaging: Towards a New Morphology. Berlin: Springer-Verlag, pp. 23-38. https://link.springer.com/book/10.1007%2F978-4-431-76933-0\n\nIt includes these files:\n1) A &quot;bones&quot; folder with .ASC format polygonal meshes at low resolution, as described in the above paper.\n2) .jnt and .msl files (&quot;veloc_J12b&quot; and &quot;veloc_M12b&quot;) that are used by proprietary SIMM software (version 7.0) to run the model; these can be opened as text files in typical text applications.\n3) A snapshot of the SIMM model (.tif image).\n4) A pdf of the chapter that described the model. (NOT covered by the CC-BY license but shared due to limited availability otherwise)\n\nNote that the SIMM model has undergone very minor changes from the 2008 paper's model.\n\nSIMM software is commercial and available at: http://www.musculographics.com/html/products/SIMM.html\nThe model can, in theory, be imported into the free, open source musculoskeletal modelling package OpenSim, but there may be file conversion issues. See http://opensim.stanford.edu/ for more information.\n\nFiles also available at: https://figshare.com/articles/Velociraptor_hindlimb_musculoskeletal_model/4982981\n\nOpenSim model has now been added (2019).","has_downloads":false,"keywords":"","ontologies":"","projMembers":"John Hutchinson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1288","unix_group_name":"ibiosim","modified":"1494438301","downloads":"0","group_name":"iBioSim","logo_file":"ibiosim","short_description":"iBioSim is a computer-aided design (CAD) tool aimed for the modeling, analysis, and design of genetic circuits. While iBioSim primarily targets models of genetic circuits, models representing metabolic networks, cell-signaling pathways, and other biologic","long_description":"iBioSim is a computer-aided design (CAD) tool aimed for the modeling, analysis, and design of genetic circuits. While iBioSim primarily targets models of genetic circuits, models representing metabolic networks, cell-signaling pathways, and other biological and chemical systems can also be analyzed.\n\niBioSim also includes modeling and visualization support for multi-cellular and spatial models as well.\n\nIt is capable of importing and exporting models specified using the Systems Biology Markup Language (SBML). It can import all levels and versions of SBML and is able to export Level 3 Version 1. It supports all core SBML modeling constructs except some types of fast reactions, and also has support for the hierarchical model composition, layout, flux balance constraints, and arrays packages.\n\nIt has also been tested successfully on the stochastic benchmark suite and the curated models in the BioModels database. iBioSim also supports the Synthetic Biology Open Language (SBOL), an emerging standard for information exchange in synthetic biology.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Leandro Watanabe","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1291","unix_group_name":"tem-abm-cfd","modified":"1494954488","downloads":"0","group_name":"A Multiscale Model of Leukocyte Transendothelial Migration During atherogenesis","logo_file":"tem-abm-cfd","short_description":"Available herein is a multiscale model of leukocyte TEM and plaque evolution in the left anterior descending (LAD) coronary artery. The approach integrates cellular behaviors via agent-based modeling (ABM) and hemodynamic effects via computational fluid dynamics (CFD). In this computational framework, the ABM implements the diffusion kinetics of key biological proteins, namely Low Density Lipoprotein (LDL), Tissue Necrosis Factor alpha (TNF-α), Interlukin-10 (IL-10) and Interlukin-1 beta (IL-1β), to predict chemotactic driven leukocyte migration into and within the artery wall. The ABM also considers wall shear stress (WSS) dependent leukocyte TEM and compensatory arterial remodeling obeying Glagov’s phenomenon. Overall this multi-scale and multi-physics approach appropriately captures and integrates the spatiotemporal events occurring at the cellular level in order to predict leukocyte transmigration and plaque evolution. ","long_description":"Atherosclerosis affects millions of people worldwide and is characterized by a plaque , build-up of fatty material, leukocytes, and extracellular matrix inside the artery wall. Over time plaque enhances and blocks blood flow thereby altering the hemodynamics. If the plaque ruptures, the occlusion may cause a life-threating stroke or myocardial infarction. Although it is known that local biochemical and hemodynamics influence leukocyte adhesion and trans-endothelial migration (TEM) into the wall, their effects on the growth rates of plaques are less known.  There-fore, we  have developed a three-dimensional computational approach to appropriately capture and integrate crucial spatiotemporal events, in order to predict leukocytes migration from the blood into the artery wall. The approach integrates cellular behaviors via agent-based modeling (ABM) and hemodynamic effects via computational fluid dynamics (CFD). Collectively, understanding how mechanobiological events are integrated within an artery will help eluci-date emergent behaviors and predict plaque evolution.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Rita Bhui,Heather Hayenga","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1295","unix_group_name":"tls-spinefusion","modified":"1495567952","downloads":"102","group_name":"Spine: ThoracoLumboSacral (TLS) Long Constructs Statistical Model","logo_file":"tls-spinefusion","short_description":"This project analyzes sacropelvic fixation biomechanics and techniques.  Software tools are provided that allow users to easily navigate the results of a statistical model based on complex multivariate, multi-axis, experimental data.","long_description":"The lumbosacral junction is a susceptible transition point between the mobile lumbar spine and the rigid pelvis. Already subject to high rates of pathology, this level can experience up to 67-69% radiographic adjacent segment degeneration following instrumentation with long constructs terminating at L5. Studies have shown that up to 22 % of adolescent scoliosis constructs terminating in the lower lumbar spine required revision within a 15-year follow up period. The sacropelvis is often included in cases of deformity correction including those requiring osteotomy, high grade spondylolistheisis, and “long” thoracolumbar constructs. What constitutes a long posterior construct, and when sacropelvic fixation is required are unclear at this time, and the optimal sacropelvic fixation technique for varying construct lengths has yet to be determined.  \n\nBiomechanical and clinical evidence supports that iliac screws remain the strongest, most definitive method of sacropelvic fixation. However, iliac screws also may increase operative time, blood loss, postoperative sacroiliac (SI) joint pain, and rarely neurovascular injury. Given the associated morbidity, iliac screws are not currently considered “the standard of care” for all long lumbar and thoracolumbar constructs.  Understanding the impact that construct length, construct type, and loading direction have on kinematics of the base of the spine is in important factor in finding optimal solutions for each surgical intervention.  This project aims to elucidate the relationship of all these variables.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Robb Colbrunn","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1301","unix_group_name":"thomasoverbergh","modified":"1597917060","downloads":"0","group_name":"Bi-planar radiography-based skeletal modeling platform used in spinal deformity","logo_file":"thomasoverbergh","short_description":"A software tool to create subject-specific musculoskeletal models based on bi-planar radiographic images: applied in spinal deformity subjects.\n◊◊◊Project in progress◊◊◊\n==> See 'documents' for more images.","long_description":"A software tool to create subject-specific musculoskeletal models based on bi-planar radiographic images: applied in spinal deformity subjects.\n\nFeel free to contact me for more information: Thomas.Overbergh@kuleuven.be\n\nPlease cite the following publication: \n\nOverbergh, T., Severijns, P., Beaucage-Gauvreau, E., Jonkers, I., Moke, L., Scheys, L., 2020. Development and validation of a modeling workflow for the generation of image-based, subject-specific thoracolumbar models of spinal deformity. J. Biomech. 110, 109946.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thomas Overbergh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1302","unix_group_name":"model-high-flex","modified":"1509141696","downloads":"6881","group_name":"A musculoskeletal model for simulations that involve high hip and knee flexion","logo_file":"","short_description":"The goal of this project was to developed a refined musculoskeletal model capable of simulating movements that involve substantial hip and knee flexion (e.g. sprinting and cycling). The model is based on the model published by Rajagopal et al., (2016).\n\nPlease cite the following paper, \"Lai, A.K.M, Arnold, A.S., Wakeling, J.M. (2017) Why are antagonist muscles co-activated in my simulation? A musculoskeletal model for analysing human locomotor task. Annals of Biomedical Engineering. ","long_description":"Existing “off-the-shelf” musculoskeletal models are problematic when simulating movements that involve substantial hip and knee flexion, such as the upstroke of pedalling, because they tend to generate excessive passive fibre force. \n\nThe goal of this project was to develop a refined musculoskeletal model capable of simulating pedalling and fast running, in addition to walking, which predicts the activation patterns of muscles better than existing models. \n\nWe refined the OpenSimTM model published by Rajagopal et al. (2016) by increasing the model’s range of knee flexion, updating the paths of the knee muscles, and modifying the force-generating properties of eleven muscles. \n\nSimulations of pedalling, running and walking based on this model reproduced measured EMG activity better than simulations based on the existing model  even when both models tracked the same subject-specific kinematics. Improvements in the predicted activations were associated with decreases in the net passive moments\n\nThe refined model is suitable for analysing movements with up to 120 degrees of hip flexion and 140 degrees of knee flexion.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Allison Arnold,Adrian Lai,James  Wakeling","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1303","unix_group_name":"vumcpilot","modified":"1497308357","downloads":"0","group_name":"VUMC Pilot","logo_file":"","short_description":"Pilot project with SimTK","long_description":"Pilot project with SimTK","has_downloads":false,"keywords":"","ontologies":"","projMembers":"John Jeffrey Carr","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1305","unix_group_name":"vectorspaces","modified":"1535735514","downloads":"0","group_name":"Vector Space Construction for the Microbiome","logo_file":"","short_description":"Project with the FDA as part of the UCSF-Stanford CERSI to predict drug metabolism by bacteria in the human gut. Prototyped on MetaCyc data using the mol2vec pipeline from Stefano Rensi. Work as part of student's masters thesis in Russ Altman's lab at Sta","long_description":"Project with the FDA as part of the UCSF-Stanford CERSI to predict drug metabolism by bacteria in the human gut. Prototyped on MetaCyc data using the mol2vec pipeline from Stefano Rensi. Work as part of student's masters thesis in Russ Altman's lab at Stanford University.\n\nThe contents are solely the responsibility of the authors and do not necessarily represent the official views of the HHS or FDA.\n\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ambika Acharya,Emily Mallory","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1306","unix_group_name":"wu-shoulder","modified":"1497501526","downloads":"679","group_name":"Wu's musculoskeletal model for evaluating shoulder muscle and joint force","logo_file":"","short_description":"This generic rigid-body upper-limb musculoskeletal model may be employed to estimate the forces in the shoulder muscles given pre-defined upper limb motion and external forces. \n\nThe muscle-tendon paths have been optimised to data derived from one compr","long_description":"This generic rigid-body upper-limb musculoskeletal model may be employed to estimate the forces in the shoulder muscles given pre-defined upper limb motion and external forces. \n\nThe muscle-tendon paths have been optimised to data derived from one comprehensive set of muscle moment arm experiments performed in vitro. Musculotendon parameters have calculated using an optimization routine from sets of isometric and isokinetic tasks performed one generic healthy individual. A customised static optimization function was written in Matlab to add constraints on the glenohumeral joint force direction. This is required to ensure that the calculated muscle forces produce sufficient stabilising glenohumeral joint compression. The optimiser utilizes OpenSim and the C++ API. \n\nThe downloadable project is provided, including relevant files for performing muscle and joint force estimation, as well as setup instructions. An example shoulder motion from one healthy subject during coronal-plane abduction is also provided. \n\nPlease cite the following paper when using the model:\nWu, W., Lee, P. V. S., Bryant, A. L., Galea, M., &amp; Ackland, D. C. (2016). Subject-specific musculoskeletal modeling in the evaluation of shoulder muscle and joint function. Journal of Biomechanics, 49(15), 3626–3634.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Wen Wu,David Ackland","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1307","unix_group_name":"elastomericfoam","modified":"1497470179","downloads":"10","group_name":"Determination of elastomeric foam parameters for simulations of complex loading","logo_file":"","short_description":"The download includes mechanical testing data on elastomeric foams; shear, unconfined compression and volumetric compression, collected during the course of Marc Thomas Petre's master's thesis.","long_description":"Finite element (FE) analysis has shown promise for the evaluation of elastomeric foam personal protection devices. Although appropriate representation of foam materials is necessary in order to obtain realistic simulation results, material definitions used in the literature vary widely and often fail to account for the multi-mode loading experienced by these devices. This project accompanies the publication - Petre, M., Erdemir, A. and Cavanagh, P. R. (2006) Determination of elastomeric foam parameters for simulations of complex loading, Computer Methods in Biomechanics and Biomedical Engineering, 9, 231-242; in order to provide data and relevant material in regards to a library of elastomeric foam material parameters that can be used in FE simulations of complex loading scenarios.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ahmet Erdemir","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1308","unix_group_name":"retina-abm","modified":"1506946464","downloads":"44","group_name":"Developing Retinal Vascular ABM with Pericytes","logo_file":"","short_description":"Objective: Define a role for perivascular cells during developmental retinal angiogenesis in the context of endothelial cell Notch1-DLL4 signaling at the multicellular network level.\nMethods: The retinal vasculature is highly sensitive to growth factor m","long_description":"Objective: Define a role for perivascular cells during developmental retinal angiogenesis in the context of endothelial cell Notch1-DLL4 signaling at the multicellular network level.\nMethods: The retinal vasculature is highly sensitive to growth factor mediated intercellular signaling. Although endothelial cell signaling has been explored in detail, it remains unclear how pericytes function to modulate these signals that lead to a diverse set of vascular network patterns in health and disease. We have developed an agent-based model of retinal angiogenesis that incorporates both endothelial cells and pericytes to investigate the formation of vascular network patterns as a function of pericyte coverage.  We use our model to test the hypothesis that pericytes modulate Notch1-DLL4 signaling in endothelial cell-endothelial cell interactions. \nResults: ABM simulations that include pericytes more accurately predict experimentally observed vascular network morphologies than simulations that lack pericytes, suggesting that pericytes may influence sprouting behaviors through physical blockade of endothelial intercellular connections. \nConclusion: This study supports a role for pericytes as a physical buffer to signal propagation during vascular network formation – a barrier that may be important for generating healthy microvascular network patterns.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Shayn Peirce-Cottler,Joseph Walpole","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1310","unix_group_name":"motion-training","modified":"1497883125","downloads":"0","group_name":"Task-Based Musculoskeletal Control Framework for Real-Time Motion Training","logo_file":"","short_description":"The goal of this project is to create a cyber-human framework that advances both robotics and biomechanics, by deepening our scientific understanding of human motor performance dictated by musculoskeletal physics and neural control, in order to assist cli","long_description":"The goal of this project is to create a cyber-human framework that advances both robotics and biomechanics, by deepening our scientific understanding of human motor performance dictated by musculoskeletal physics and neural control, in order to assist clinicians in quantifying the characteristics of a subject's motion and designing effective motion training treatments. Current technologies do not permit detailed motion reconstruction in real time, which limits their use in clinical settings. This work will combine theory with software, hardware and sensing technology to synthesize human motion with dynamic, actively controlled subject-specific musculoskeletal models and to provide real-time visual feedback to a human subject. The project will deliver open-source algorithms and metrics for quantifying human performance and for understanding the underlying motion characteristics that modify these metrics. The capabilities developed in this project will have a transformative impact on society by enabling real-time human motion synthesis, with potential applications in rehabilitation, physical therapy, human-robot interaction, kinesiology and occupational biomechanics. \n\nProject outcomes will include: (1) computational models of the human musculoskeletal system for task-based control; (2) integrated performance metrics for motion characterization based on a subject's physiological constraints; and (3) control and simulation algorithms to synthesize movement using biomechanical models that accurately match experimental data, compensate for measurement errors, and visualize the model and its motion in real time. To these ends, task-based models of human motion will first be created. Motion capture experiments will be conducted to validate the model and to fine tune subject-specific parameters. The resulting computational platform will then be used to determine long-term performance statistics and metrics to efficiently characterize human motion. In the second phase, robust control and simulation algorithms will be integrated with the computational system to synthesize movement using biomechanical models. The framework will be used to identify feasible modifications to improve subject-specific motion characteristics. Finally, these criteria will be integrated into a feedback mechanism that will visually suggest modified trajectories for optimal motion to the subject.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emel Demircan","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1311","unix_group_name":"roboticact","modified":"1583835585","downloads":"171","group_name":"Robotic Actuators","logo_file":"","short_description":"The goal of this project is to develop a class of DC motor in OpenSim. The actuator model can be used to simulate powered robotic devices working within musculoskeletal systems.","long_description":"To perform comparable simulations of human movements with robotic devices, it may be required to include the dynamics of the robotic actuators, instead of using simplistic, ideal force/torque models. Therefore, in this project, we develop a class of DC motor, that is a popular type actuator used in developing assistive robotic devices (e.g., prostheses, exoskeleton). The motor can be use to simulate powered robotic devices working within musculoskeletal systems.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Vinh Nguyen,Frank Sup","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1312","unix_group_name":"mice","modified":"1509981094","downloads":"0","group_name":"Comparison of muscle fiber excursion in walking between mice and humans","logo_file":"mice","short_description":"Comparison of muscle fiber excursion in walking between mice and humans","long_description":"Comparison of muscle fiber excursion in walking between mice and humans","has_downloads":false,"keywords":"","ontologies":"","projMembers":"James Charles,Silvia Blemker,Xiao Hu,John Hutchinson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1318","unix_group_name":"lyme","modified":"1499468387","downloads":"0","group_name":"Lyme Disease Modeling","logo_file":"","short_description":"This project adopts a system dynamics approach to study the impact of behavioral, demographic and environmental factors on Lyme disease transmission and prevention in built environments by developing a simulation model of Lyme incidence using ecological a","long_description":"This project adopts a system dynamics approach to study the impact of behavioral, demographic and environmental factors on Lyme disease transmission and prevention in built environments by developing a simulation model of Lyme incidence using ecological and social, behavioral and demographic risk factors for transmission, and to determine the most cost-effective prevention strategies.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Nasim Sabounchi,Nasser Sharareh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1319","unix_group_name":"callusanalog","modified":"1516658850","downloads":"4","group_name":"Callus Analog Framework","logo_file":"callusanalog","short_description":"We provide a framework for biomedical researchers to experiment with mechanisms that may drive the fracture healing process. We utilize Java and the MASON simulation toolkit.","long_description":"Utilizing the MASON simulation toolkit, this framework implements four mechanisms which aim to describe the fracture healing process. Our in silico Callus Analogs attempt to simulate the transition of the histologic appearance of a mouse fracture callus from day-7 to day-10 of the healing process. After applying rigorous cycles of the Iterative Refinement Protocol, our mechanisms achieved 73-94% similarity when compared to the prespecified histologic appearance of the fracture callus at day-10. \n\nSimulated healing provides a new perspective on the actual healing process and a new way of thinking about plausible networked fracture healing processes. This work is intended to be applicable to other biomedical fields that use histologic analysis to investigate and explain tissue level phenomena.\n\nResearchers may choose between the four implemented mechanisms through the GUI, and may extend this initial work with customized mechanisms and additional tissue units. Installation instructions are provided in a README file bundled with the source code.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Meir Marmor,Ryan Kennedy,C. Anthony Hunt","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1322","unix_group_name":"muscle-contact","modified":"1508019038","downloads":"0","group_name":"Muscle-Joint Contact Force Model","logo_file":"","short_description":"Objective: Develop Full Body Model with Muscle-Joint Contact Force Analysis","long_description":"Through implementation of contact geometry native to OpenSim, the project aims to model compressive loading on joints resulting from muscle activity via CMC *or* prescribed activation profiles in forward dynamics. Current model development pertains to knee joint compressive loading resulting from IT-Band tension and knee/hip kinematics. Extension of this framework to other key joints or body segments will allow full body analysis of such joint loading mechanisms.","has_downloads":false,"keywords":"contact forces,musculoskeletal,OpenSim,joint load,musculotendon kinematics","ontologies":"Contact_Modeling","projMembers":"Deepak Sathyanarayan","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1323","unix_group_name":"isb2017","modified":"1501085376","downloads":"0","group_name":"ISB2017demo","logo_file":"","short_description":"Created for demo at ISB 2017","long_description":"Created for demo at ISB 2017","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Takeo Matsumoto","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1326","unix_group_name":"osa-sim-drl","modified":"1501265414","downloads":"0","group_name":"Simulating an anthropomimetic robotic arm with OpenSim for applying DeepRL","logo_file":"","short_description":"The Robot Studio is a leading designer of open-source humanoid robots based on the same mechanical principles as the human body. The main goal of this project is to simulate an anthropomimetic robotic arm using OpenSim and to link it to some machine learning libraries like TensorFlow to apply DeepRL.","long_description":"Overview : The Robot Studio is a leading designer of open-source humanoid robots based on the same mechanical principles as the human body. This approach produces a style of robot capable of adapting to its environment, with movements that intuitively differ from the rigid motions of a standard robotic system. We aim to create a robust, free hardware platform that can safely perform a wide range of useful tasks alongside humans in everyday real-life situations.\n \nProblem description : With such complex robots it is not possible to use classical methods like the inverse kinematics to drive the arm in space like it can be done for a stiff industrial robotic arm. In this case there is too much non-linearities, too many degrees of freedom with some redundancy and elasticity in the system.\nOne of the possibilities is to train the robot to learn a specific task using Deep Reinforcement Learning algorithms. But it would be a very tedious long process to do with only one robot prototype because it would need a lot of human assistance and the hardware would probably break before it has learned the task. The way to go around that is to do the training in a simulator, duplicate the experiment a thousands times in parallel on GPU-servers and at the end, transfer the skill to the machine and let it learn from there.\n\nProject : The main goal of this project is to simulate an anthropomimetic robotic arm using OpenSim and to link it to some machine learning libraries like TensorFlow to apply DeepRL.\n\nTo have an idea on how it looks and move, here is a video of the robot arm, driven from a gamepad : https://www.youtube.com/watch?v=m1l0T5p3lN0\n\nAny advice or help on how to achieve that is greatly appreciated!\n\nWebsite : http://www.therobotstudio.com","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Cyril Jourdan","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1327","unix_group_name":"p01","modified":"1501265346","downloads":"0","group_name":"Hopping performance","logo_file":"","short_description":"Metabolic cost of hopping","long_description":"Metabolic cost of hopping","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Zahra Safaeepour","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1328","unix_group_name":"psb-vector-bact","modified":"1506987281","downloads":"0","group_name":"Chemical Reaction Vector Embeddings PSB Supplement","logo_file":"","short_description":"Hosting for supplement table and documentation for \"Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome\" research article for the 2018 Pacific Symposium on Biocomputing. \n\nAuthors: Emily K Mallory (Stanfor","long_description":"Hosting for supplement table and documentation for &quot;Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome&quot; research article for the 2018 Pacific Symposium on Biocomputing. \n\nSupplement files available under Documents.\n\nAuthors: Emily K Mallory (Stanford), Ambika Acharya (Stanford), Stefano E Rensi (Stanford), Roselie A Bright (OHI/FDA), and Russ B Altman (Stanford)\n\nThe contents are solely the responsibility of the authors and do not necessarily represent the official views of the HHS or FDA.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emily Mallory","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1332","unix_group_name":"drop-foot-afos","modified":"1504020931","downloads":"0","group_name":"Drop Foot and AFOs","logo_file":"","short_description":"Drop foot is a condition that can affect individuals who have suffered a stroke, cerebral palsy, or injury to the cerebrum. It occurs when the muscles in the ankle and/or lower leg are weak, causing the foot to drop and drag during gait. The knee on the a","long_description":"Drop foot is a condition that can affect individuals who have suffered a stroke, cerebral palsy, or injury to the cerebrum. It occurs when the muscles in the ankle and/or lower leg are weak, causing the foot to drop and drag during gait. The knee on the affected leg is placed under excess stress, as it will lift the leg higher in an attempt to compensate for the dragging foot. This extra loading in the knee joint puts the sufferer at an increased risk for developing osteoarthritis. Ankle foot orthoses (AFOs) are a common treatment for this condition, and our goal for this project is to use OpenSim to model the effect of stiffness, design, and placement of the AFOs  with the goal of making the devices more effective.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ashley Rice,Jeff Reinbolt","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1333","unix_group_name":"orp3","modified":"1502817567","downloads":"0","group_name":"Orp3","logo_file":"","short_description":"Does Orp3 have Calcium binding sites?","long_description":"Does Orp3 have Calcium binding sites?","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ryan D'Souza","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1336","unix_group_name":"humanfc","modified":"1528335489","downloads":"40","group_name":"Identification of 139 Human Transcriptomic Modules using ICA","logo_file":"","short_description":"We identified 139 transcriptomic profiles (aka Functional Components; FCs) in a data-driven fashion from a large set of microarray data from GEO. We also constructed a human tissue compendium to accompany the work. We have written up an R package that wou","long_description":"We identified 139 transcriptomic profiles (aka Functional Components; FCs) in a data-driven fashion from a large set of microarray data from GEO. We also constructed a human tissue compendium to accompany the work. We have written up an R package that would allow users to project their own data into this FC space, and also search the human tissue compendium for tissues most similar to a query sample.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Weizhuang Zhou","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1343","unix_group_name":"casp12funassess","modified":"1504728144","downloads":"2","group_name":"Biological and Functional Relevance of CASP Predictions","logo_file":"","short_description":"Compared with experimental structures, how useful are predicted models for functional annotation?  We assessed the predicted models’ functional utility by comparing the performances of functional characterization methods on predicted and experimental structures.","long_description":"Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation?  We assessed the predicted models’ functional utility by comparing the performances of functional characterization methods on predicted and experimental structures. We identified 28 sites in 25 protein targets on which to perform functional assessment.  These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that were expected or suggested by experimental authors to have small molecule binding (apo-sites), and ten sites containing motifs, loops, or key residues with important disease-associated mutations.  We evaluated the functional utility of the predictions by comparing their microenvironments to the experimental structures.  Overall structural quality correlates with functional utility.  However, the best-ranked predictions (global) may not have the best functional quality (local).  Our assessment provides the ability to discriminate between predictions with high structural quality.  When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites.  Some servers show consistently high performance for certain types of functional sites.  Finally, many functional sites are associated with protein-protein interaction.  We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we found that the features in the models were mainly determined by the choice of template.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Tianyun Liu,Russ Altman,Wen Torng,Shirbi Ish-Shalom","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1348","unix_group_name":"task-space","modified":"1590739965","downloads":"896","group_name":"Simulation of Constrained Musculoskeletal Systems in Task Space","logo_file":"task-space","short_description":"This work proposes an operational task space formalization of constrained musculoskeletal systems, motivated by its promising results in the field of robotics. ","long_description":"Objective: This work proposes an operational task space formalization of constrained musculoskeletal systems, motivated by its promising results in the field of robotics. \n\nMethods: The change of representation requires different algorithms for solving the inverse and forward dynamics simulation in the task space domain. We propose an extension to the Direct Marker Control and an adaptation of the Computed Muscle Control algorithms for solving the inverse kinematics and muscle redundancy problems respectively. \n\nResults: Experimental evaluation demonstrates that this framework is not only successful in dealing with the inverse dynamics problem, but also provides an intuitive way of studying and designing simulations, facilitating assessment prior to any experimental data collection. \n\nSignificance: The incorporation of constraints in the derivation unveils an important extension of this framework towards addressing systems that use absolute coordinates and topologies that contain closed kinematic chains. Task space projection reveals a more intuitive encoding of the motion planning problem, allows for better correspondence between observed and estimated variables, provides the means to effectively study the role of kinematic redundancy and, most importantly, offers an abstract point of view and control, which can be advantageous towards further integration with high level models of the precommand level.\n\nConclusion: Task-based approaches could be adopted in the design of simulation related to the study of constrained musculoskeletal systems.\n\nThe source code of the project can be found at: https://github.com/mitkof6/opensim-task-space.git\n\nThe new API of task space and constraint projection for OpenSim V4.0 is available at: https://github.com/mitkof6/task-space\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/jfE14iWRZDs&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;","has_downloads":true,"keywords":"task space,musculoskeletal system,inverse dynamics,forward dynamics,constrained mechanics","ontologies":"","projMembers":"Dimitar Stanev,Konstantinos Moustakas","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"1357","unix_group_name":"statopt-contact","modified":"1554805351","downloads":"837","group_name":"OpenSim plugin to estimate contact forces using static optimization","logo_file":"statopt-contact","short_description":"This project provides a plugin for OpenSim to simultaneously estimate (external) contact forces and (internal) muscle forces in an inverse dynamic manner from a prescribed motion trajectory.","long_description":"The plugin implements a custom OpenSim model component to represent a simple point-on-plane contact model. Friction effects are considered according to the static and the dynamic regime of Coulomb’s law. This contact model is evaluated by a special static optimization procedure, that can be run within OpenSim’s Analyze Tool.\n\nWe developed this approach to analyze physical interactions between human beings and technical artifacts such as manufacturing machines and sports equipment. However, it is also particularly useful to estimate ground reaction forces during gait.\n\nBackground information on this approach can be found in the following publication:\n\nD. Krüger and S. Wartzack, “A contact model to simulate human–artifact interaction based on force optimization: implementation and application to the analysis of a training machine,” Computer Methods in Biomechanics and Biomedical Engineering, pp. 1–10, Oct. 2017.\nDOI: 10.1080/10255842.2017.1393804\n\nIf you would like to use our work in your own research please cite that paper!","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Daniel Krueger,Felix Laufer","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1359","unix_group_name":"knee-front-mom","modified":"1507227229","downloads":"464","group_name":"Knee joint model to calculate moments on the frontal plane at contact points","logo_file":"knee-front-mom","short_description":"This project makes available a modified version of the gait2392 model to calculate knee contact forces at the medial and lateral compartment using the equilibrium equations from Winby et al. (2009) J Biomechanics 42, 2294-2300. This approach can be used to calculate knee contact forces in EMG-driven approaches.","long_description":"This project makes available a modified version of the gait2392 model that:\n* allows calculating the moments acting at two contact points identified for the knee joint. \n* allows calculating the muscle moment arms with respect to this contact points (frontal plane)\n* can be used to calculate joint contact forces at the medial and lateral compartments using the equations provided in Winby et al. (2009) J Biomechanics 42, 2294-2300.\n* can be used to calculate knee contact forces in EMG-driven approaches.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Luca Modenese,David John Saxby,Claudio Pizzolato","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1376","unix_group_name":"agovmsc","modified":"1506060759","downloads":"0","group_name":"Motor exploration of the upper limb","logo_file":"","short_description":"Looking at 3D analysis of upper limb motor exploratory movement after stroke","long_description":"Looking at 3D analysis of upper limb motor exploratory movement after stroke","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Amerson Govindsamy","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1379","unix_group_name":"icudnr","modified":"1506463295","downloads":"0","group_name":"Reversals and Limitation in High-Intensity, Life-Sustaining Treatments","logo_file":"","short_description":"Clinical factors associated with ICU patients \"changing their mind\" about life support treatments with DNR orders.","long_description":"Clinical factors associated with ICU patients &quot;changing their mind&quot; about life support treatments with DNR orders.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jonathan Chen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1384","unix_group_name":"wfmusclemodel","modified":"1515108955","downloads":"0","group_name":"Winding Filament Muscle Model","logo_file":"wfmusclemodel","short_description":"The goal of this project is to implement a model of the Winding Filament Hypothesis of muscle contraction (Nishikawa, 2012) for use in OpenSim.","long_description":"The Winding Filament Hypothesis states that - upon calcium influx into the muscle sarcomere - titin binds to and winds upon actin thin filaments. The mechanical behavior of titin could serve as an explanatory mechanism for force enhancement with stretch, force depression with shortening, and dynamic force-velocity properties of active muscle.\nThe Winding Filament Model (WFM) is a two-parameter (resting length and peak isometric force) approximation of the molecular kinematics and kinetics of a muscle sarcomere. The model is comprised of a massless actin &quot;pulley&quot; of radius R, about which a damped contractile element (myosin head), a damped titin spring element, and a series elastic element interact. Current implementation in OpenSim is through the MATLAB scripting interface, with the goal of this project being a freely distributed WFM plug-in utilizing all features currently offered within the OpenSim 3.3 API.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Daniel Rivera","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1396","unix_group_name":"jointtorque","modified":"1508194181","downloads":"0","group_name":"Joint Torque","logo_file":"","short_description":"Assess the workload of workers based on their joint torque","long_description":"Assess the workload of workers based on their joint torque","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yantao Yu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1398","unix_group_name":"virworkshop2017","modified":"1508870312","downloads":"0","group_name":"2017 Fall OpenSim Virtual Workshop","logo_file":"","short_description":"The Question and Answer and Support Forum for the 2017 Fall Virtual Workshop. ","long_description":"The 2017 Fall Virtual Workshop will bring together a group of international scholars and OpenSim experts to help each other advance their research using modeling and simulation. This forum is a place to accelerate OpenSim based projects while also building relationships with a network of researchers and engineers who are using OpenSim for research. ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"jimmy d,Christopher Dembia,Jennifer Hicks,Ajay Seth,Joy Ku,Thomas Uchida","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1404","unix_group_name":"neckdynamics","modified":"1564158570","downloads":"849","group_name":"Musculoskeletal Model of Head and Neck Suitable for Dynamic Simulations","logo_file":"neckdynamics","short_description":"This project provides an improved model of the head and neck, that is suitable for dynamic simulations. This model is the first of its kind to offer realistic moment generating capacity in all directions.","long_description":"Previous models of the head and neck were unable to generate realistic moment generating capacities in all directions. Even scaling individual muscle strengths was not enough to get realistic moment generating capacity in all directions, because most neck muscles apply significant moment in multiple directions. Introducing hyoid muscles to the model begins to address this problem.\n\nPlease cite: Mortensen, J. D., Vasavada, A. N., &amp; Merryweather, A. S. (2018). The inclusion of hyoid muscles improve moment generating capacity and dynamic simulations in musculoskeletal models of the head and neck. PloS one, 13(6), e0199912.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jonathan Mortensen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1405","unix_group_name":"ebc-network","modified":"1543259579","downloads":"892","group_name":"A global network of biomedical relationships derived from text","logo_file":"","short_description":"Code and README for regenerating GNBR. The full network and a complete description of the project are here: https://zenodo.org/record/1495808.","long_description":"Please see https://zenodo.org/record/1495808 for a complete description of this project, as well as the network itself. The SimTK site contains:\n\n- Bash and Python scripts for regenerating GNBR on the Sherlock cluster at Stanford (will need to be adapted if you're working at a different institution)\n- Java code (link to GitHub repo) for dependency parsing, etc. that these scripts depend on\n- Resource files containing flagship paths and themes\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Russ Altman,Bethany Percha","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1415","unix_group_name":"high-hip-flex","modified":"1587391872","downloads":"2041","group_name":"Full Body Model to Perform Deep Squatting and High Hip Flexion Tasks","logo_file":"high-hip-flex","short_description":"Simulation of high flexion tasks such as squatting is hindered through invalid moment length estimation when using generic musculoskeletal models. The purpose of this study was to apply wrapping surfaces at the hip joint to allow it to simulate movements that involve substantial hip flexion (e.g. deep squatting).","long_description":"This customized musculoskeletal is suitable for analysis with up to 138 degrees of hip flexion and 145 degrees of knee flexion and was based on the previously published model by Rajagopal et al. (2016) and Lai et al. (2017).\n\nFour wrapping surfaces were updated:\nGmax1_at_pelvis\nGmax2_at_pelvis\nKnExt_at_fem\nKnExtVL_at_fem\n\nThree wrapping surfaces were implemented:\nPost_at_pelvis\nGmed_at_pelvis\nFlex_at_femhead\n\nTo cite this article: Danilo S. Catelli, Mariska Wesseling, Ilse Jonkers &amp; Mario Lamontagne (2019): A musculoskeletal model customized for squatting task, Computer Methods in Biomechanics and Biomedical Engineering, 22(1):21-24.\nDOI: 10.1080/10255842.2018.1523396\n\nLink to this article: https://doi.org/10.1080/10255842.2018.1523396","has_downloads":true,"keywords":"High flexion,Squat,Muscle wrapping,Knee,Hip","ontologies":"","projMembers":"Ilse Jonkers,Mario Lamontagne,Mariska Wesseling,Danilo Catelli","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1417","unix_group_name":"xsens","modified":"1697082061","downloads":"2150","group_name":"XSens - OpenSim modelling","logo_file":"xsens","short_description":"This project intends to assess the feasibility of using inertia-tracking system (i.e. XSens) to provide input data for modelling in OpenSim. We adapted the Catelli model to the 'T-pose' used for XSens to scale their biomechanical model.","long_description":"This project intends to assess the feasibility of using inertia-tracking system (i.e. XSens) to provide input data for modelling in OpenSim. We adapted the Catelli model to the 'T-pose' used for XSens to scale their biomechanical model. We also created the virtual markers exported at the C3D files from MVN Analyse into OpenSim .xml files. In addition, we developed scripts in order to convert data from XSens in the TRC files required for scaling, based on work from Felipe Alvim and others.\nIn order to convert the data from XSens to OpenSim, the following steps are required:\n1- Export the data from MVN Studio as C3D assigning the first frame as the 'T-pose';\n2- Use the codes provided in this project to convert the C3D files into TRC (static and motion)\n\nThis project also allows you to export data from Kistler Bioware software to .mot files, if you wish to do Inverse Dynamics. For this step, you have to export the data as .txt files and use these files into the program block associated with the Bioware convertion.","has_downloads":true,"keywords":"XSens","ontologies":"","projMembers":"Rodrigo Bini","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1428","unix_group_name":"infarctabm","modified":"1511813981","downloads":"6","group_name":"Agent-Based Model of Myocardial Scar Formation (Rouillard et al. 2012)","logo_file":"","short_description":"Our group developed an agent-based model of myocardial scar healing, implemented in MATLAB. The model tracks migration, proliferation, and collagen remodeling by fibroblasts over 6 weeks of simulated infarct healing, incorporating the influence of regiona","long_description":"Our group developed an agent-based model of myocardial scar healing, implemented in MATLAB. The model tracks migration, proliferation, and collagen remodeling by fibroblasts over 6 weeks of simulated infarct healing, incorporating the influence of regional strains, chemokine gradients, and local matrix orientation on the orientation of the fibroblasts and the collagen fibers they produce. The original model was published in the Journal of Physiology in 2012, and has been used with various minor modifications in several other papers and abstracts from our group since that time. If you use or adapt the model, please cite the original Journal of Physiology article: Rouillard AD, Holmes JW. Mechanical regulation of fibroblast migration and collagen remodeling in healing myocardial infarcts, J Physiol, 590(18):4585-4602, Sep 2012 (http://www.ncbi.nlm.nih.gov/pubmed/22495588).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jeff Holmes","trove_cats":[{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1004","fullname":"IMAG/MSM Consortium"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"},{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1429","unix_group_name":"gabr-md","modified":"1511979029","downloads":"0","group_name":"GabR Molecular Dynamics","logo_file":"","short_description":"GabR from Bacillus subtilis is a transcriptional regulator belonging to the MocR subfamily of the GntR regulators. The structure of the MocR regulators is characterized by the presence of two domains: i) a N-terminal domain, about 60 residue long, possess","long_description":"GabR from Bacillus subtilis is a transcriptional regulator belonging to the MocR subfamily of the GntR regulators. The structure of the MocR regulators is characterized by the presence of two domains: i) a N-terminal domain, about 60 residue long, possessing the winged-Helix-Turn-Helix (wHTH) architecture with DNA recognition and binding capability; ii) a C-terminal domain (about 350 residue) folded as the pyridoxal 5’-phosphate (PLP) dependent aspartate aminotransferase (AAT) with dimerization and effector binding functions. The two domains are linked to each other by a peptide bridge. Although structural and functional characterization of MocRs is proceeding at a fast pace, virtually nothing is know about the molecular changes induced by the effector binding and on how these modifications influence the properties of the regulator. An extensive molecular dynamics simulation on the crystallographic structure of the homodimeric B. subtilis GabR has been undertaken with the aim to envisage the role and the importance of conformational flexibility in the action of GabR. Molecular dynamics has been calculated for the apo (without PLP) and holo (with PLP bound) forms of the GabR. A comparison between the molecular dynamics trajectories calculated for the two GabR forms suggested that one of the wHTH domain detaches from the AAT-like domain in the GabR PLP-bound form. The most evident conformational change in the holo PLP-bound form is represented by the rotation and the subsequent detachment from the subunit surface of one of the wHTH domains. The movement is mediated by a rearrangement of the linker connecting the AAT domain possibly triggered by the presence of the negative charge of the PLP cofactor. This is the second most significant conformational modification. The C-terminal section of the linker docks into the “active site” pocket and establish stabilizing contacts consisting of hydrogen-bonds, salt-bridges and hydrophobic interactions.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Stefano Pascarella","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1431","unix_group_name":"aneurysm","modified":"1512059107","downloads":"0","group_name":"cerebral aneurysm research using CFD","logo_file":"","short_description":"cerebral aneurysm is known to be related with flow dynamics in its initiation, growth and rupture, however, its direct role is still poorly understood. thus, a research using CFD, especially in an attempt to bring it into clinical medicine, is proposed. T","long_description":"cerebral aneurysm is known to be related with flow dynamics in its initiation, growth and rupture, however, its direct role is still poorly understood. thus, a research using CFD, especially in an attempt to bring it into clinical medicine, is proposed. To do so, CFD is to be validated using animal experiment data and clinical data as well. This research brings us a particular flow marker in cerebral aneurysm diagnostics and treatment decision-making process.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"takanobu yagi","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1432","unix_group_name":"lucas01","modified":"1512151836","downloads":"0","group_name":"Fragment-based mixed-solvent molecular dynamics of pVHL:EloC:EloB E3 ligase","logo_file":"","short_description":"In this project we sought to identify novel ligandable pockets in the pVHL:EloC:EloB E3 ubiquitin ligase applying mixed-solvent MD using small fragments as probes. Results extracted from the simulations will be published in a manuscript we are currently w","long_description":"In this project we sought to identify novel ligandable pockets in the pVHL:EloC:EloB E3 ubiquitin ligase applying mixed-solvent MD using small fragments as probes. Results extracted from the simulations will be published in a manuscript we are currently working on.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Xavier Lucas","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1433","unix_group_name":"tendon-repair","modified":"1512154119","downloads":"0","group_name":"Collagen remodeling in tendon injury","logo_file":"","short_description":"This model demonstrates the effect of different treatment regimens on a supraspinatus tendon injury. A complete tear is made in the tendon and fibroblasts migrate to the wound to deposit collagen and remodel the scar that is formed. The red agents are col","long_description":"This model demonstrates the effect of different treatment regimens on a supraspinatus tendon injury. A complete tear is made in the tendon and fibroblasts migrate to the wound to deposit collagen and remodel the scar that is formed. The red agents are collagen fibers with an assigned heading. The blue triangles are fibroblasts that migrate throughout the wound and deposit and degrade collagen. The green color represents chemokine that is produced from the wound space and diffuse outward.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Michaela Rikard","trove_cats":[{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1435","unix_group_name":"norovirusabm","modified":"1512672688","downloads":"0","group_name":"Agent based model of Norovirus transmission on a cruise ship","logo_file":"","short_description":"We've created an agent based model of the transmission of Norovirus on a cruise ship. This agent based model was defined in Netlogo. A cruise ship environment was created with several different compartments - cabin, dining/common area, and pool deck. Agen","long_description":"We've created an agent based model of the transmission of Norovirus on a cruise ship. This agent based model was defined in Netlogo. A cruise ship environment was created with several different compartments - cabin, dining/common area, and pool deck. Agents were divided into workers and passengers, who had different daily schedules of commuting through the common areas. Infection spread was modeled by a probability of spreading the virus to another agent upon contact, represented in Netlogo as sharing the same patch as an infected agent.  Illness was modeled with an incubation period following initial infection, an infectious period where agents can spread the virus, and a symptomatic period where agents confine themselves to their cabin. Total infected agents over the duration of the cruise can be monitored.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Amanda Westman","trove_cats":[{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1436","unix_group_name":"wound-infection","modified":"1512845604","downloads":"0","group_name":"Innate immune response to bacterial infection in a superficial, acute wound","logo_file":"","short_description":"We've created an agent based model in which cells of the innate (non-specific) immune system respond to a bacterial infection in a shallow, acute skin wound. The user can change rates in the model to compare the time for all the bacteria to be removed (ba","long_description":"We've created an agent based model in which cells of the innate (non-specific) immune system respond to a bacterial infection in a shallow, acute skin wound. The user can change rates in the model to compare the time for all the bacteria to be removed (bacterial clearance) in a healthy patient compared to a diabetic patient. Diabetic patients are at an increased risk of long-lasting bacterial infections because they have increased glucose sugar levels in their bloodstream and their immune cells are less effective at fighting infection.\n\nGlucose is replenished from the blood stream at a constant rate. If bacteria have accumulated enough energy, they reproduce. Else the bacteria move towards the highest concentration of glucose, and consume it to gain more energy. A bacteria leaves a trail of chemical as it moves. A neutrophil, the first responder to an infection, then appears at the scene, recruiting other neutrophils to join in the fight. The neutrophils follow the chemical trail from the bacteria. Neutrophils leave a trail of immune chemicals which kill bacteria and gobble the bacteria up by phagocytosis if they get close enough. Later, macrophages appear at the scene which also release immune chemicals and gobble up bacteria. The macrophages are more effective at killing bacteria than neutrophils, but are slower to respond to the infection. Over time, the build-up of chemicals from the bacteria and immune cells damages the tissue.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Cara Broshkevitch","trove_cats":[{"id":"1005","fullname":"IMAG-MSM Public Dissemination & Education Working Group"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1450","unix_group_name":"febiolumbar","modified":"1604082445","downloads":"561","group_name":"FEBio Finite Element Models of the Human Lumbar Spine","logo_file":"febiolumbar","short_description":"Developing open access finite element models of the human lumbar spine.","long_description":"The purpose of this project is to develop open access finite element models of the human lumbar spine using open source software (FEBio Software Suite (https://febio.org/)).  We are developing whole lumbar spine models (L1-5) and models of functional spine units (FSUs).  Models are developed from CT image data segmented in our laboratory (https://mrl.sci.utah.edu/) and from porting models to FEBio that were first developed for commercial codes.  This project is a collaboration between the Departments of Biomedical Engineering and Orthopaedics at the University of Utah, and we work closely with the FEBio development team.\n\nPlease see the following reference for details about the models and cite this reference when using the models for further publications:\n\nFinley SM, Spina NT, Brodke DS, DeDen CA, Ellis BJ:  FEBio finite element models of the human lumbar spine.  Computer Methods in Biomechanics and Biomedical Engineering, 21(6): 444-452, 2018.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ben Ellis,Sean Finley","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1452","unix_group_name":"boneremodelabm","modified":"1516310648","downloads":"46","group_name":"Agent-Based Model of Load-Dependent Remodeling in Trabecular Bone","logo_file":"","short_description":"This model simulates remodeling of the trabecular bone at the cellular level due to changes in loading conditions. ","long_description":"This model simulates remodeling of the trabecular bone at the cellular level due to changes in loading conditions. Fluctuations in bone density carried out by osteocytes, osteoblasts, and osteoclasts within the margins of a transverse cross section of bone are representing visually. Users can assess bone remodeling as a function of age and gender. The different load settings, meant to reflect bed-rest, normal activity, or high activity, can be changed in real time so that a user can see how cell behavior and counts react. ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Adrienne Williams,Alexander Mathew,Benjamin Anton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1460","unix_group_name":"fscnn","modified":"1533418232","downloads":"1797","group_name":"High Precision Functional Site Detection Using 3DCNNs","logo_file":"","short_description":"In this project, we present a general framework that applies 3D convolutional neural networks (3DCNN) to structure-based protein functional site detection. The framework does not rely on engineered features and can extract task-dependent features automati","long_description":"In this project, we present a general framework that applies 3D convolutional neural networks (3DCNN) to structure-based protein functional site detection. The framework does not rely on engineered features and can extract task-dependent features automatically from the raw atom distributions. Our deep 3DCNNs achieve an average prediction recall of 0.955 at the precision threshold of 0.99, outperforming SVM and Naïve Bayes models that employ conventional features. Importantly, the 3DCNN models showed superior performance on challenging cases where 1D sequence motifs are absent but a function is known to exist. Finally, we inspect the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features about protein functional sites.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Wen Torng","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1468","unix_group_name":"bioid","modified":"1519148693","downloads":"0","group_name":"BioID","logo_file":"","short_description":"Frozen Shoulder Simmulation","long_description":"Frozen Shoulder Simmulation","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Temiloluwa Adenyi","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1469","unix_group_name":"hipflexion298","modified":"1519257654","downloads":"0","group_name":"HON kinesiology iliopsoas flexion model","logo_file":"","short_description":"Modeling the simultaneous abduction and flexion of the hip joint and iliofemoral/iliopsoas soft tissue groups during side extension known as \"developee\"","long_description":"Modeling the simultaneous abduction and flexion of the hip joint and iliofemoral/iliopsoas soft tissue groups during side extension known as &quot;developee&quot;","has_downloads":false,"keywords":"","ontologies":"","projMembers":"sabrina earp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1473","unix_group_name":"moving-fingers","modified":"1655137649","downloads":"787","group_name":"Modification of Wrist Model to include all the movements of the fingers.","logo_file":"moving-fingers","short_description":"The ¨Wrist Model¨ reported by Gonzalez in Opensim SimTK is complemented by adding the missing degrees of freedom to the middle, ring and little fingers.","long_description":"The ¨Wrist Model¨ reported by Gonzalez in Opensim SimTK have 10 degrees of freedom, and a total of 23 actuator muscles, with movement in the forearm, wrist, thumb (no flexion) and Index, but as the purpose of our investigation is to evaluate the complete movements of a real hand in simulation, we decided to modify this model and complete the movement of the hand adding the missing degrees of freedom to the thumb, middle, ring and little fingers. This more complete model is used for simulation of the predicted movements of a hand motion classifier under Matlab, for a virtual hand application. The tool ¨Computed Muscle Control¨ (CMC) was used review the sequence of muscle excitations of these movements. Programs such as rhinoceros, paraview and notepad++ were used to modify figures in .vtp format to read opensim models.\n\nIn the Documents section we find the .mot files of the movements that we created to load in the presented model.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jose  Alejandro Amezquita Garcia,Miguel Bravo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1474","unix_group_name":"kuo-head-neck","modified":"1559150415","downloads":"895","group_name":"Role of the Neck during Impact Loading","logo_file":"","short_description":"Building upon the Vasavada 1998, and more recent Mortensen 2018 neck and head model, we are exploring the role of muscles and passive cervical spine (neck) soft tissue in stabilizing the head and neck following impact loads.","long_description":"Building upon the Vasavada 1998, and more recent Mortensen 2018, neck and head model, we are exploring the role of muscles and passive cervical spine (neck) soft tissue in stabilizing the head and neck following impact loads. We have collected human subject head kinematics data undergoing mild external loads with varying load direction (resulting in head extension or head lateral flexion) and varying muscle activity (minimal activation/relaxed and maximal activation/co-contracted). Example videos of the experimental procedures:\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/Hb1vDtzpGEU&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/e9se5LTDcUs&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nWe have developed subject-specific head and neck models and validated them against measured head kinematics using measured impact loads. The head and neck model adds cervical spine ligaments, which utilize a new dynamic ligament plugin model. The dynamic ligament model is similar to the current ligament model available in vanilla OpenSim, but adds velocity dependence based on previous experimental literature. Using simulations of our experimental impacts, we can now predict the torque contributions of the various components of the cervical spine (active muscle, passive muscle, other passive structures). Example videos of OpenSim simulations validated against experimental results:\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/ZljBr-k8j1Q&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/xK0r_BqrDWs&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/6heoO7D3MDY&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/ggFFadRbRgU&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nWe are now obtaining consent to provide data publicly through SimTK. To be included are the baseline model, subject-scaled models, experimental average male and experimental average female models, relevant experimental data (head kinematics and impact loads), and relevant files for running simulations (muscle activations, setup files, external load files, output motion and analysis files).\n\nFor more information, please contact: calvin.kuo@ubc.ca\n\nRelevant Citations:\n1) Kuo C., Fanton M., Wu L., Camarillo D.B. &quot;Spinal constraint modulates head instantaneous center of rotation and dictates head angular motion.&quot; Journal of Biomechanics. 76(25): 220-228. (2018)\n2) Kuo C, Sheffels J, Fanton M, Yu I, Hamalainen R, Camarillo D. &quot;Passive Cervical Spine Ligaments Provide Stability during Head Impacts.&quot; Journal of the Royal Society Interface. https://doi.org/10.1098/rsif.2019.0086 (2019)","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jodie Sheffels,Rosa Hamalainen,Bianca Yu,Calvin Kuo,Michael Fanton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1475","unix_group_name":"hk4510","modified":"1520190133","downloads":"0","group_name":"Knee Joint","logo_file":"","short_description":"Analyzing the effects of hyper mobility on the knee joint","long_description":"Analyzing the effects of hyper mobility on the knee joint","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Hannah Milan","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1476","unix_group_name":"papersensing","modified":"1520447301","downloads":"0","group_name":"Paper based sensors for pyocyanin detection","logo_file":"","short_description":"Paper-based sensors for rapid detection of virulence factor produced by Pseudomonas aeruginosa","long_description":"Paper-based sensors for rapid detection of virulence factor produced by Pseudomonas aeruginosa","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Fatima Alatraktchi","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1477","unix_group_name":"lunge01","modified":"1520453110","downloads":"0","group_name":"Influence of forward lunge variations on lower extremity muscle activiy","logo_file":"","short_description":"The objective of this study is to examine muscle activation during the forward lunge in adult exercisers. This project seeks to determine which combination of trunk and lower leg angles maximizes muscle activation during a forward lunge exercise. Knowing ","long_description":"The objective of this study is to examine muscle activation during the forward lunge in adult exercisers. This project seeks to determine which combination of trunk and lower leg angles maximizes muscle activation during a forward lunge exercise. Knowing this information will help inform practitioners’ decisions on which type of exercise is best for lower extremity strengthening. The central hypothesis is the trunk lean lunge will have greater muscle activity in the gluteus maximus, vastus lateralis, biceps femoris and gluteus medius when the trunk and shank angle are altered. The rationale for this research is to inform future guidelines regarding how to maximize the effects of the forward lunge exercise while minimizing discomfort and risk of injury.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Karen Buckley","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1478","unix_group_name":"weldinghelmet","modified":"1520617837","downloads":"0","group_name":"Welding Helmet Calculations","logo_file":"","short_description":"Biomechanic Calculations for the loads and momentums a welding helmet brings upon the user.","long_description":"Biomechanic Calculations for the loads and momentums a welding helmet brings upon the user.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Josefin Pettersson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1479","unix_group_name":"ahsl-tamu","modified":"1520795221","downloads":"0","group_name":"Modelling Fluid Shift Observed in Astronauts in variable gravity environments","logo_file":"","short_description":"Interested in modeling the fluid shift and redistribution of blood flow observed in astronauts in variable gravity environments.","long_description":"Interested in modeling the fluid shift and redistribution of blood flow observed in astronauts in variable gravity environments.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Bonnie Dunbar","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1485","unix_group_name":"sym-moment-arm","modified":"1590739885","downloads":"70","group_name":"Symbolic Derivation of the Muscle Moment Arm Matrix","logo_file":"sym-moment-arm","short_description":"","long_description":"OpenSim is a framework for modeling and simulation of musculoskeletal systems. The muscle moment arm is an important variable for evaluating the effectiveness of a muscle to actuate a particular joint. Calculating the muscle moment arm requires knowledge of the muscle path and wrapping surfaces. OpenSim is restricted to evaluate the muscle moment arm at an arbitrary configuration, lacking the information for calculating higher order derivatives. This project evaluates the moment arm at different configurations and approximates its terms using multivariate polynomial fitting, thus a symbolic expression is derived. Examples are provided for the gait2392 model (23 DoFs and 92 muscles). Results are calculated and stored in the *.dat* files which can be loaded using python's pickle utility. The *R.dat* file is the symbolic expression (sympy Matrix) of the muscle moment arm matrix. Visual inspection of the polynomial fitting is provided.\n\nhttps://github.com/mitkof6/symbolic_moment_arm","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Dimitar Stanev","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1487","unix_group_name":"leg-exos","modified":"1522172287","downloads":"0","group_name":"Lower Limp Exoskeleton","logo_file":"","short_description":"Recently, stroke patients increase dramatically in Thailand. Ischemic brain disease is one of the most common serious diseases in Thailand. And it is a serious problem for the patient itself and their families. Therefore, this project aims to invent a rei","long_description":"Recently, stroke patients increase dramatically in Thailand. Ischemic brain disease is one of the most common serious diseases in Thailand. And it is a serious problem for the patient itself and their families. Therefore, this project aims to invent a reinforced lower limp exoskeleton for patients with stroke. The purpose is to help patients with stroke to rehabilitate their leg. Also, sometimes patients need to help themselves  as much as possible. There is a case where sometimes caregivers can not take care of the patients at all times. The lower limp exoskeleton would reduce the burden of these caregivers.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Patinya Samanuhut","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1488","unix_group_name":"serm","modified":"1522280888","downloads":"8","group_name":"Pocket Similarity Identifies SERMs as Microtubule Modulators","logo_file":"","short_description":"A computational binding site similarity screen of drug-like ligand pockets from PDB repurposes SERMs as novel microtubule targeting agents.","long_description":"A computational binding site similarity screen of &gt; 14,000 ligand-binding pockets from PDB revealed an unexpected similarity between the estrogen receptor and the beta-tubulin taxane site. Evaluation of nine selective estrogen modulator receptors (SERMs) via in vivo and in vitro assays confirmed taxane site binding, microtubule modulation and cell growth inhibition. Our study demonstrates that SERMs can modulate microtubule assembly and suggests potential drug repurposing for cancer treatment.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ben Lo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1490","unix_group_name":"oaprogression","modified":"1666537362","downloads":"116","group_name":"Modeling and Predicting Osteoarthritis Progression","logo_file":"oaprogression","short_description":"The goal of this study was to model the longitudinal progression of knee osteoarthritis (OA) and build a prognostic tool that uses data collected in one year to predict disease progression over eight years. We carried out functional data clustering with a mixed-effects mixture model to overcome the challenge of missing data in longitudinal studies.","long_description":"To model OA progression, we used eight-year joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire, clustering disease progression trajectories with a mixed-effects mixture model that was designed especially for functional data (trajectories) with missing portions. After clustering subjects based on radiographic and pain progression, we used clinical variables collected within the first year to build least absolute shrinkage and selection operator (LASSO) regression models for predicting the probabilities of belonging to each cluster. \n\nFor more details, please refer to the following article:\n\nHalilaj E, Le Y, Hicks JL, Hastie TJ, Delp SL. Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative. Osteoarthritis Cartilage. 2018;26(12):1643-1650. doi:10.1016/j.joca.2018.08.003\n\nStatistical modeling was based on the following articles:\n\nJames GM, Sugar CA. Clustering for Sparsely Sampled Functional Data. J Am Stat Assoc. 2003;98(462):397-408.\n\nJames GM, Hastie TJ. Functional linear discriminant analysis for irregularly sampled curves. J R Stat Soc Ser B Stat Methodol. 2001;63(3):533-550.\n\nJames GM, Hastie TJ, Sugar CA. Principal component models for sparse functional data. Biometrika. 2000;87(3):587-602.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Jennifer Hicks,Scott Delp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1491","unix_group_name":"oastaging","modified":"1666537117","downloads":"264","group_name":"Automated Staging of OA Using Deep Neural Networks","logo_file":"oastaging","short_description":"We introduce a fully automated, open-source tool for staging knee osteoarthritis severity from X-ray images. ","long_description":"Recent developments in machine learning, specifically in the area of deep learning, are transforming medical image analysis, yet automated analysis of X-ray and Magnetic Resonance Imaging data remains a major bottleneck in OA research. The Osteoarthritis Initiative (OAI) database presents a unique opportunity to transfer advances in deep learning to osteoarthritis research. Toward that goal, we demonstrate the feasibility of deep learning approaches to automate staging of knee OA severity from X-ray data.\n\nYou can run our software using docker software. Follow the instruction on our official github repository https://github.com/stanfordnmbl/kneenet-docker\n\nCite: Thomas, Kevin A., Łukasz Kidziński, Eni Halilaj, Scott L. Fleming, Guhan R. Venkataraman, Edwin HG Oei, Garry E. Gold, and Scott L. Delp. &quot;Automated classification of radiographic knee osteoarthritis severity using deep neural networks.&quot; Radiology. Artificial intelligence 2, no. 2 (2020).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Kevin Thomas,Łukasz Kidziński,Scott Delp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1493","unix_group_name":"dcwithjtcontact","modified":"1522326169","downloads":"67","group_name":"Direct collocation with articular contact","logo_file":"","short_description":"This project provides a package that can be used to solve both data-tracking optimization and predictive optimization problems using collocation in conjunction with surrogate contact. A OpenSim model created based on the 4th Grand Challenge dataset is available for download. Note that two plug-ins, SurrContactForce.dll and LigamentJ.dll are currently unavailable.  We will make these two plug-ins and the source code available once an on-going project is completed.  ","long_description":"This project provides a package that can be used to solve both data-tracking optimization and predictive optimization problems using collocation in conjunction with surrogate contact.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"yi-chung lin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1495","unix_group_name":"umdnrc","modified":"1531508981","downloads":"47","group_name":"UMD Neuromechanics Fullbody Model","logo_file":"","short_description":"A fullbody model from the Neuromechanics Research Core, University of Maryland","long_description":"A fullbody model from the Neuromechanics Research Core, University of Maryland","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ross Miller","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1496","unix_group_name":"bicepangle","modified":"1522948685","downloads":"0","group_name":"BIcep Contraction","logo_file":"","short_description":"This will help calc muscle contraction for the bicep thru the motion of different angles","long_description":"This will help calc muscle contraction for the bicep thru the motion of different angles","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kyle Ondar","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1499","unix_group_name":"simcpspasticity","modified":"1546486583","downloads":"1000","group_name":"Simulating Muscle Spasticity in Children with Cerebral Palsy","logo_file":"","short_description":"This project contains experimental data and code for simulating muscle spasticity during passive stretches and gait in children with cerebral palsy.","long_description":"We developed three models of spasticity based on feedback from muscle states. The first model relied on feedback from muscle length and velocity. The second model relied on feedback from muscle length, velocity and acceleration. The third model relied on feedback from muscle force and its first time derivative (force rate). We first calibrated the models based on experimental data collected during passive stretches in children with cerebral palsy. We then used the calibrated models to predict the spastic response during gait. We showed that only the model based on feedback from muscle force and force rate could explain spastic muscle activity during passive stretches and gait. This suggests that force encoding in muscle spindles in combination with altered feedback gains and thresholds underlie activity of spastic muscles during passive stretches and gait.\n\nThis project contains experimental data and code necessary to reproduce all results presented in the associated publication.  Please find more information in the manual (see folder Manual in simcpspasticity-code).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Friedl De Groote,Ilse Jonkers","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1500","unix_group_name":"knee-exo-pred","modified":"1552596734","downloads":"0","group_name":"Knee Exoskeleton Prediction Framework","logo_file":"","short_description":"There is a need for improved methods to prescribe and evaluate the use of wearable robotic devices for the treatment of movement disorders, such as crouch gait from cerebral palsy (CP). Our first aim is to develop and validate a modeling framework for acc","long_description":"There is a need for improved methods to prescribe and evaluate the use of wearable robotic devices for the treatment of movement disorders, such as crouch gait from cerebral palsy (CP). Our first aim is to develop and validate a modeling framework for accurately predicting changes in swing limb kinematics and muscle activity during robotic assisted walking in children with CP. We will utilize an existing experimental biomechanics dataset from our exoskeleton feasibility study to refine model parameters, which may include optimization schemes, muscle properties, and limb-device interaction mechanics.\n\nSimulation files from our publication (https://doi.org/10.1016/j.jbiomech.2019.02.025) are available for download. ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Zach Lerner","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1501","unix_group_name":"sheepforelimb","modified":"1550853510","downloads":"26","group_name":"Musculoskeletal model of a sheep forelimb","logo_file":"sheepforelimb","short_description":"The objective of this project was the creation of a musculoskeletal model of a sheep forelimb for dynamic simulation and analysis.","long_description":"The objective of this project was the creation of a musculoskeletal model of a sheep forelimb for dynamic simulation and analysis.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Santiago Arroyave-Tobon","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1505","unix_group_name":"batchprocesstrc","modified":"1533346411","downloads":"116","group_name":"Batch processing to create marker (.trc) files using Scilab","logo_file":"batchprocesstrc","short_description":"I provide a script code (Scilab) that creates marker (.trc) files from .csv (Comma Separated Values) files. ","long_description":"* Please prepare .csv (Comma Separated Values) files of marker trajectories (Unit: mm).\n* You can easily conduct batch processing using the script code.\n* You can remove a header including some rows.\n* You can remove some unnecessary columns.\n* You can change directions of axes (x, y, z axes) for OpenSim.\n* If you do not have a software to export marker (.trc) files, the script code may help you.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Takuma Inai","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1506","unix_group_name":"batchprocessmot","modified":"1533346229","downloads":"67","group_name":"Batch processing to create ground reaction force (.mot) files using Scilab","logo_file":"batchprocessmot","short_description":"I provide a script code (Scilab) that creates ground reaction force (.mot) files from .csv (Comma Separated Values) files.\n","long_description":"* Please prepare .csv (Comma Separated Values) files of ground reaction forces (Units: N, m, Nm).\n* You can easily conduct batch processing using the script code.\n* You can remove a header including some rows.\n* You can remove some unnecessary columns.\n* You can change directions of axes (x, y, z axes) for OpenSim.\n* If you do not have a software to export ground reaction force (.mot) files, the script code may help you.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Takuma Inai","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1512","unix_group_name":"rfs-ffs-cad-tsf","modified":"1527118512","downloads":"0","group_name":"Foot strike pattern, cadence, and tibial stress fracture parameters","logo_file":"","short_description":"The purpose of this study was to test the hypothesis that converting to a forefoot striking pattern or increasing cadence without focusing on changing foot strike type would reduce biomechanical parameters associated with tibial stress fracture in recreational runners.","long_description":"Previous work comparing acute adaptations to forefoot striking or altering cadence attempted to independently assess the effects of foot strike and cadence by having subjects run using a forefoot striking pattern without adjusting cadence and changing cadence while maintaining a rearfoot striking pattern. In our study, we aimed to capture how runners naturally adjusted their running pattern while being asked to focus only on changing foot strike or increasing cadence. Our goal was to study how natural adaptations to changing foot strike or increasing cadence affects tibial stress fracture risk during overground running in a single population by analyzing the following set of injury risk parameters: peak absolute free moment, peak hip adduction angle, and average and peak loading rates.\n\n*Data is available in the Documents section.\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Christopher Dembia,Jennifer Yong","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1517","unix_group_name":"bicep-curl","modified":"1526000536","downloads":"0","group_name":"Modeling an arm","logo_file":"","short_description":"This semester I am researching how the anatomical structure of the different joints in a person’s body affect the amount of torque required to use that body part. I started by focusing specifically on the bicep muscle and the force that the muscle must ","long_description":"This semester I am researching how the anatomical structure of the different joints in a person’s body affect the amount of torque required to use that body part. I started by focusing specifically on the bicep muscle and the force that the muscle must exert during a bicep curl. I am trying to model the arm geometrically, and need to know the actual lengths and angles or ratios of some elements of an arm.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Adele Smolansky","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1519","unix_group_name":"ostrichopensim","modified":"1607049886","downloads":"50","group_name":"Musculoskeletal simulation of an ostrich","logo_file":"ostrichopensim","short_description":"OpenSim model (based on Hutchinson et al. 2015 in PeerJ- see also https://simtk.org/projects/ostrich_model/) used for simulations (static and dynamic) of walking and running in the main publication cited here.","long_description":"See Publication that describes the model in full (paper is Open Access).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jeff Rankin,John Hutchinson","trove_cats":[{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1520","unix_group_name":"redundancy","modified":"1590740181","downloads":"33","group_name":"Modeling kinematic and dynamic redundancy","logo_file":"redundancy","short_description":"Methods for modeling, simulation and analysis of redundant musculoskeletal systems.","long_description":"The coordination of the human musculoskeletal system is deeply influenced by its redundant structure, in both kinematic and dynamic terms. Noticing a lack of a relevant, thorough treatment in the literature, we formally address the issue in order to understand and quantify factors affecting the motor coordination. We employed well-established techniques from linear algebra and projection operators to extend the underlying kinematic and dynamic relations by modeling the redundancy effects in null space. We distinguish three types of operational spaces, namely task, joint and muscle space, which are directly associated with the physiological factors of the system. A method for consistently quantifying the redundancy on multiple levels in the entire space of feasible solutions is also presented. We evaluate the proposed muscle space projection on segmental level reflexes and the computation of the feasible muscle forces for arbitrary movements. The former proves to be a convenient representation for interfacing with segmental level models or implementing controllers for tendon driven robots, while the latter enables the identification of force variability and correlations between muscle groups, attributed to the system’s redundancy. Furthermore, the usefulness of the proposed framework is demonstrated in the context of estimating the bounds of the joint reaction loads, where we show that misinterpretation of the results is possible if the null space forces are ignored. This work presents a theoretical analysis of the redundancy problem, facilitating application in a broad range of fields related to motor coordination, as it provides the groundwork for null space characterization. The proposed framework rigorously accounts for the effects of kinematic and dynamic redundancy, incorporating it directly into the underlying equations using the notion of null space projection, leading to a complete description of the system.\n\nhttps://github.com/mitkof6/musculoskeletal-redundancy\n\nhttps://github.com/mitkof6/feasible_muscle_force_analysis","has_downloads":true,"keywords":"musculoskeletal system,redundancy","ontologies":"","projMembers":"Dimitar Stanev,Konstantinos Moustakas","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1521","unix_group_name":"simcp","modified":"1559815154","downloads":"215","group_name":"SimCP: a platform to simulate gait performance after intervention in CP children","logo_file":"","short_description":"The goal of SimCP is to create an OpenSim-based simulation platform to predict gait performance following orthopedic intervention in children with cerebral palsy ","long_description":"The motivations behind this project are (1) the observation that it is very difficult to predict\n\nthe functional outcome of a clinical treatment in children with cerebral palsy (CP), which\n\noften leads to follow-up treatments with an important socio-economic impact; and (2) the\n\nrecent scientific progress in the domain of subject-specific neuro-musculoskeletal modeling\n\nwhich enables the development of computer simulations to predict the functional outcome\n\nfor individual patients. Therefore, this project aims to create SimCP, a simulation platform\n\nthat will enable the clinicians to compare the effect of different treatments and to determine\n\nwhich treatment has the highest potential of improving gait performance before the\n\nintervention.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Ilse Jonkers,Friedl De Groote,lorenzo pitto,Mariska Wesseling,Hoa Hoang,sam van rossom,hans kainz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1524","unix_group_name":"nemo","modified":"1527494679","downloads":"57","group_name":"NEMO","logo_file":"","short_description":"Nested Monte Carlo based RNA inverse folding agent, for applications in RNA design.","long_description":"Nested Monte Carlo based RNA inverse folding agent, for applications in RNA design.","has_downloads":true,"keywords":"Monte Carlo,RNA inverse folding,RNA secondary structure","ontologies":"","projMembers":"Rhiju Das,Fernando Portela","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"307","fullname":"RNA"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":false,"is_application":true,"is_data":false},{"group_id":"1527","unix_group_name":"dnaagncs","modified":"1527790591","downloads":"0","group_name":"dnaagncs","logo_file":"","short_description":"Silver nanoclusters (AgNCs) are <2nm sized fluorescent species which is unstable in solution. However, with scaffolding nucleic acid, AgNCs can be stabilized in solution and can be used for multiple applications. The scaffolding DNA has to be structured a","long_description":"Silver nanoclusters (AgNCs) are &lt;2nm sized fluorescent species which is unstable in solution. However, with scaffolding nucleic acid, AgNCs can be stabilized in solution and can be used for multiple applications. The scaffolding DNA has to be structured and the structure influences the fluorescent properties of AgNCs. We want to investigate the role of individual structure and the mechanism through which such modulation can be engineered.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Pratik Shah","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1528","unix_group_name":"hka-exo-sim","modified":"1528217884","downloads":"596","group_name":"Modeling Bilateral Hip-Knee-Ankle Exoskeleton Assistance","logo_file":"hka-exo-sim","short_description":"Software to model hip-knee-ankle exoskeleton assistance during walking","long_description":"Here is software developed for Stanford's ME 485 course. The files here have been adapted for modeling hip-knee-ankle exoskeleton assistance during walking using ideal torque actuators. It is currently implemented to optimize to reduce activations-squared. Metabolic cost can then be calculated from the results of the optimization. \n\nThis project utilizes the optimal muscle control framework from De Groote et al 2016: https://simtk.org/projects/optcntrlmuscle which in turn utilizes GPOPS-II optimal control software and adigator. Experimental data and some software adapted from https://github.com/nickbianco/soft-exosuit-design. The model was adapted from Ong et al. 2017 (check confluence page below for full references). \n\nMore information on this project can be found at:\nhttps://simtk-confluence.stanford.edu/display/OpenSim/Modeling+Bilateral+Hip-Knee-Ankle+Exoskeleton+Assistance.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Patrick Franks,Julia Butterfield","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1529","unix_group_name":"cyclingmodel","modified":"1528235713","downloads":"0","group_name":"OpenSim Teaching Materials","logo_file":"","short_description":"We created a simplified cycling model and a GUI concept for a teaching application. ","long_description":"The goal of the project is to create an interactive and enjoyable biomechanics teaching tool for high school students. The teaching tool will allow students to examine how muscle excitations and coordinations impact cycling speed. Students will be able to adjust the muscle excitations for the model as a function of the crank angle. The model includes the right leg with the feet attached to a pedal that is connected to a shaft and crank. The student will be walked through  three levels. Level 1 focuses on the vasti and gluteus maximums in the down stroke. Level 2 focuses on the hamstrings in the upstroke. Level 3 focuses on the coordination of all the muscles in a multi-player race. The level structure is designed to give the students early success and to analyze how the different muscles interact with the crank angle during the down stroke and upstroke phases.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1530","unix_group_name":"mcfreduction","modified":"1528222880","downloads":"0","group_name":"Combining Kinematic &amp; Gait Modifications to Reduce Medial Knee Contact Force","logo_file":"","short_description":"Does decreasing the foot progression angle (toe-in gait) reduce medial contact loads in the knee?\n\nMedial compartment osteoarthritis is a leading cause of years claimed by disability worldwide. Joint replacements improve the quality of life for individu","long_description":"Does decreasing the foot progression angle (toe-in gait) reduce medial contact loads in the knee?\n\nMedial compartment osteoarthritis is a leading cause of years claimed by disability worldwide. Joint replacements improve the quality of life for individuals with end-stage osteoarthritis; however, less invasive interventions to prevent or delay surgery are desirable. Increased contact forces in the medial compartment of the knee joint are thought to accelerate the progression of osteoarthritis.\n\nKnee adduction moment (KAM) and total tibiofemoral force (TF) are potential correlates to medial contact force (MCF) in the knee joint (Kutzner et al., 2013; Nagura et al., 2006). Shull et al. (2012) showed experimentally that shifting the foot progression angle inward during walking (i.e. toe-in gait) could reduce the first peak in KAM during stance. This is accomplished by shifting foot's center of pressure laterally and altering the ground reaction force vector. Lowering the KAM tends to shift knee contact forces laterally, thus potentially alleviating load on the medial compartment. DeMers et al. (2014) showed that muscle recruitment optimization could be leveraged to lower the second peak of TF. His study reveals the sensitivity of contact forces in the knee to internal muscle forces, particularly for muscles crossing the knee joint. Winby et al. (2009) further describes that medial contact force is 42% external forces contribution (i..e ground reaction forces) and 58% internal contributes (i.e. muscle activation). Decreasing MCF is the focus of interventions to decelerate progression of medial compartment osteoarthritis.\n\nBy building a lower-body musculoskeletal model that combines Rajagopal’s full-body musculoskeletal model and Lerner’s contact model of the knee joint, we will confirm whether toe-in gait reduces medial contact force in the knee (Rajagopal et al., 2012; Lerner et al., 2015).","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kirsten Seagers","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1531","unix_group_name":"sensitivity","modified":"1528237450","downloads":"30","group_name":"Sensitivity Analysis of Gait Models to Variation in Muscle-tendon Parameters","logo_file":"","short_description":"We modified the muscle-tendon parameters of ankle muscles during walking to determine if the optimal activation patterns of these and surrounding muscles were altered when optimizing activations with fixed kinematics. The medial gastrocnemius, soleus, and","long_description":"We modified the muscle-tendon parameters of ankle muscles during walking to determine if the optimal activation patterns of these and surrounding muscles were altered when optimizing activations with fixed kinematics. The medial gastrocnemius, soleus, and tibialis anterior muscles and optimal force, optimal fiber length, tendon slack length, and pennation angle were the key parts of the analysis, but this script allows for changes in any muscle and at any parameter.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jon Stingel,Katherine Poggensee","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1532","unix_group_name":"4cfscaphoid","modified":"1528470232","downloads":"0","group_name":"Wrist Salvage Procedures: 4CF without Scaphoidectomy","logo_file":"","short_description":"This is a private project for collection of materials needed to complete a 4CF model without scaphoidectomy (and with other variations to the scaphoid)","long_description":"This is a private project for collection of materials needed to complete a 4CF model without scaphoidectomy (and with other variations to the scaphoid)","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Wendy Murray,Emily Walsh,Jennifer Nichols","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1533","unix_group_name":"martela1","modified":"1528996648","downloads":"0","group_name":"DNA 20pb","logo_file":"","short_description":"building the pdb file of DNA fragment","long_description":"building the pdb file of DNA fragment","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Anne Martel","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1536","unix_group_name":"fpsm","modified":"1530376089","downloads":"0","group_name":"FPSM - French Pediatric Shoulder Model to evaluate shoulder joint disorders","logo_file":"fpsm","short_description":"This is the first pediatric shoulder joint model developed at an INSERM lab at LaTIM, France, to evaluate pediatric shoulder joint biomechanics. Pediatric joint models are scarce in OpenSIM and scaling down adult models does not help determine child-specific biomechanics in the areas of shoulder disorders such as Obstetric Brachial Plexus Palsy (OBPP). The goal of this project is to determine the impact of abnormal musculoskeletal structures in OBPP pathology. ","long_description":"We at LaTIM, INSERM unit 1101 in Brest, France, are conducting research on shoulder joint disorders in adults and children. Pediatric joint models are scarce in OpenSIM. Shoulder joint disorders in children are challenging as the anatomy (and biomechanics) varies by age. This project aims to develop pediatric shoulder joint model and to disseminate the modeling and geometry information to the SimTK user community. \n\nThis project started as a main thesis topic of our PhD student Ms. Asma Salhi and is built from scratch as no much modeling data or information is available for pediatric shoulder. While everyone can access and download the model files as and when made available, developers need to contact us in order to contribute to the development efforts. \n\nThis project is currently funded by Region of Brittany, France; IMT Atlantique, Brest, France, Campus France, INSERM, and CHRU de Brest.","has_downloads":false,"keywords":"Musculoskeletal Modeling,shoulder biomechanics,pediatric shoulder,OBPP","ontologies":"","projMembers":"Bhushan Borotikar,Salhi Asma,Valerie Burdin","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1538","unix_group_name":"modifiedlumbar","modified":"1542283313","downloads":"473","group_name":"Lumbar Spine with Passive Elements","logo_file":"modifiedlumbar","short_description":"The objective of this study is to adjust and enhance the existing lumbar spine model with more detailed passive elements to simulate the rotational moment/force produced by intervertebral disc, facet articulations and ligaments.","long_description":"The model files included a modified lumbar model with passive elements and a full body model incorporating this lumbar model. The files also included setting files of external loads (10·Nm moment in three principal axes) for ROM validation.\n\nThe ligaments in L3-S1 were modeled by Ligament elements. The rotational stiffness produced by intervertebral disc or facet articulation was modeled using ExpressionBasedBushingForce element.\n\nThe model was created based on other previously developed OpenSim models (https://simtk.org/projects/fullbodylumbar, https://simtk.org/home/lumbarspine, https://simtk.org/projects/intervertebr_jr, https://simtk.org/projects/ulb_project).","has_downloads":true,"keywords":"lumbar spine","ontologies":"","projMembers":"Wenxin Niu,Kuan Wang","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1543","unix_group_name":"eye","modified":"1590740094","downloads":"322","group_name":"An Open-Source OpenSim Oculomotor Model for Kinematics and Dynamics Simulation","logo_file":"eye","short_description":"","long_description":"Physics-based modeling and dynamic simulation of human eye movements has significant implications for improving our understanding of the oculomotor system and treating various visuomotor disorders. We introduce an open-source biomechanical model of the human eye that can be used for kinematics and dynamics analysis. This model is based on the passive pulley hypothesis, constructed based on the data reported in literature regarding physiological measurements of the human eye and made publicly available. The model is implemented in OpenSim, which is an open-source framework for modeling and simulation of musculoskeletal systems. The model incorporates an eye globe, orbital suspension tissues and six extraocular muscles. The excitation and activation patterns for a variety of targets can be calculated using the proposed closed-loop fixation controller that drives the model to perform saccadic movements in a forward dynamics manner. The controller minimizes the error between the desired saccadic trajectory and the predicted movement. Consequently, this model enables the investigation muscle activation patterns during static fixation and analyze the dynamics of eye movements.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Dimitar Stanev,Konstantinos Moustakas,Konstantinos Filip","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1549","unix_group_name":"shoulderemgmoca","modified":"1531252439","downloads":"94","group_name":"A Comprehensive Assessment of Shoulder Motion","logo_file":"","short_description":"Comprehensive set of synchronized EMG and motion capture data of 20 muscle segments of the shoulder from five normally developed subjects performing five commonplace shoulder motions.","long_description":"Comprehensive set of synchronized EMG and motion capture data of 20 muscle segments of the shoulder from five normally developed subjects performing five commonplace shoulder motions.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"James Richards,Kristen Nicholson,Tyler Richardson,Elizabeth Rapp,Robert Quinton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1551","unix_group_name":"shoulderbiomech","modified":"1531855885","downloads":"0","group_name":"Shoulder Biomechanics","logo_file":"","short_description":"Biomechanical analysis of the shoulder joint.","long_description":"Biomechanical analysis of the shoulder joint.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ali Elder","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1553","unix_group_name":"fes-cycling","modified":"1706883089","downloads":"370","group_name":"Predictive framework for functional electrical stimulation (FES) cycling","logo_file":"","short_description":"Our innovative approach introduces a novel framework and showcases its application in solving predictive models.","long_description":"Enhancing the efficacy of spinal cord injury (SCI) rehabilitation is crucial for a patient’s optimal recovery. While functional electrical stimulation (FES) cycling stands as a standard therapy, achieving notable improvements proves challenging due to the inherent complexities embedded in the dynamics of the movement. Indeed, overcoming the time-consuming nature of cycling becomes imperative, prompting the development of predictive models through optimal control simulation. The current challenge lies in the demand for a specific framework that considers the unique intricacies of SCI FES cycling. In response, our innovative approach introduces a novel framework and showcases its application in solving predictive models. Leveraging open-source tools, including OpenSim and Blender, we built the FES cycling model. Subsequently, we outlined predictive problems within OpenSim Moco. This advancement mitigates the time-consuming constraints of prior methods. This improved avenue for simulating FES cycling for SCI rehabilitation paves the way for practical and time-effective integration of Digital Twins in clinical applications.","has_downloads":true,"keywords":"Dynamic muscoluskeletal control,Neuromuscular control,cycling,stimulation","ontologies":"","projMembers":"Antonio Bo,Felipe Moreira Ramos,Ana  de Sousa","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1556","unix_group_name":"emt-tumor-agm2","modified":"1532636075","downloads":"32","group_name":"Agent-based model of epithelial-mesenchymal cells transition","logo_file":"emt-tumor-agm2","short_description":"Provides a tumor model of epithelial-mesenchymal cells transition.","long_description":"This is an interactive model to demonstrate epithelial-mesenchymal cells transition (EMT), and how this process might be affected by hypoxic environment, EMT inhibitor drug, and apoptosis drug. This model also simulates consequences of the EMT process on angiogenesis and tumor progression.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Tanyaporn (Oom) Pattarabanjird,Shayn Peirce-Cottler","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1557","unix_group_name":"aclbrace","modified":"1532629965","downloads":"0","group_name":"ACL Knee Brace","logo_file":"","short_description":"ACL Knee Brace","long_description":"ACL Knee Brace","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ryan Halvorson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1558","unix_group_name":"mallorythesis","modified":"1533621816","downloads":"0","group_name":"Supplemental Files for Mallory Dissertation","logo_file":"","short_description":"Project contains supplemental files for \"Augmenting Drug Mechanism Prediction with Text Mining\", PhD dissertation by Emily K Mallory at Stanford University.","long_description":"Project contains supplemental files for &quot;Augmenting Drug Mechanism Prediction with Text Mining&quot;, PhD dissertation by Emily K Mallory at Stanford University.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emily Mallory","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1568","unix_group_name":"biceps","modified":"1535052415","downloads":"0","group_name":"Bayesian Inference of Conformational Populations (BICePs)","logo_file":"","short_description":"The BICePs algorithm (Bayesian Inference of Conformational Populations) is a statistically rigorous Bayesian inference method to reconcile theoretical predictions of conformational state populations with sparse and/or noisy experimental measurements and o","long_description":"The BICePs algorithm (Bayesian Inference of Conformational Populations) is a statistically rigorous Bayesian inference method to reconcile theoretical predictions of conformational state populations with sparse and/or noisy experimental measurements and objectively compare different models.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yunhui Ge","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1571","unix_group_name":"cmc-ssm","modified":"1535590703","downloads":"44","group_name":"A statistical shape model of the healthy first carpometacarpal joint","logo_file":"cmc-ssm","short_description":"This project contains instructions, python scripts, and example data for generating statistical shape models (SSM) using the GIAS2 library (available here: https://bitbucket.org/jangle/gias2). \n","long_description":"The workflow has been applied to healthy de-identified data of the first carpometacarpal joint (CMC), a.k.a. the trapeziometacarpal (TMC) joint, but can also be applied to other geometries, bones, or joints of interest.\n\nThe research that produced the data provided in this resource was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Number R01AR059185. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\n\nFor more information, refer to the following files that are located in the root folder of the download package:\n- readme.txt\n- Commands.txt\n- GIAS2py36-requirements.txt","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Marco Schneider","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1572","unix_group_name":"il2waves","modified":"1535571603","downloads":"0","group_name":"Oscillatory IL-2 stimulus for T cell responses","logo_file":"il2waves","short_description":"Model and data files associated with Kippner & Kemp, PLoS One, 2018.","long_description":"Cell response to extracellular ligand is affected not only by ligand availability, but also by pre-existing cell-to-cell variability that enables a range of responses within a cell population. We developed a computational model that incorporates cell heterogeneity in order to investigate Jurkat T cell response to time dependent extracellular IL-2 stimulation. Our model predicted preferred timing of IL-2 oscillatory input for maximizing downstream intracellular STAT5 nuclear translocation. The modeled cytokine exposure was replicated experimentally through the use of a microfluidic platform that enabled the parallelized capture of dynamic single cell response to precisely delivered pulses of IL-2 stimulus. The in vitro results demonstrate that single cell response profiles vary with pulsatile IL-2 input at pre-equilibrium levels. These observations confirmed our model predictions that Jurkat cells have a preferred range of extracellular IL-2 fluctuations, in which downstream response is rapidly initiated. Further investigation into this filtering behavior could increase our understanding of how pre-existing cellular states within immune cell populations enable a systems response within a preferred range of ligand fluctuations, and whether the observed cytokine range corresponds to in vivo conditions.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Melissa Kemp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1574","unix_group_name":"fdasnorkel","modified":"1535737157","downloads":"0","group_name":"Snorkel Project with FDA","logo_file":"","short_description":"Project with the FDA as part of the UCSF-Stanford CERSI to extract chemical reactions and other bacteria-related relationships from text using Snorkel.","long_description":"Project with the FDA as part of the UCSF-Stanford CERSI to extract chemical reactions and other bacteria-related relationships from text using Snorkel.\n\nThe contents are solely the responsibility of the authors and do not necessarily represent the official views of the HHS or FDA.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emily Mallory","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1575","unix_group_name":"hipwrapping","modified":"1535997648","downloads":"28","group_name":"Musculoskseltal models including wrapping surfaces around the hip joint","logo_file":"","short_description":"Generic and subject-specific musculoskeletal models including wrapping surfaces around the hip joint.\n","long_description":"In this project you can find the generic and subject-specific musculoskeletal models including wrapping surfaces around the hip joint as described in:\nWesseling, M., De Groote, F., Bosmans, L., Bartels, W., Meyer, C., Desloovere, K., Jonkers, I., 2016. Subject-specific geometrical detail rather than cost function formulation affects hip loading calculation. Comput. Methods Biomech. Biomed. Engin. 19, 1475–1488.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ilse Jonkers,Mariska Wesseling","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1581","unix_group_name":"pendulumtest","modified":"1542275407","downloads":"65","group_name":"Physiology-based model of the pendulum test","logo_file":"pendulumtest","short_description":"Our simulations show that leg kinematics during the pendulum test in individuals with spastic cerebral palsy could be explained by increased muscle tone and sensory feedback gains if their interactions with muscle short-range stiffness are taken into consideration (De Groote et al., 2018. Plos One: 13(10)).","long_description":"We simulated the dynamics of a pendulum test based on a torque-driven biomechanical model of the lower leg. Our model consisted of a planar lower leg segment with passive stiffness and damping to simulate non-contractile musculotendon properties. Active joint torques representing muscle contractile behavior consisted of a constant baseline torque to represent increased muscle tone, a short-range stiffness torque dependent on the level of muscle tone, and a delayed sensory feedback torque to simulate reflex activity. Muscle short-range stiffness was scaled as a function of muscle tone. We simulated the reflex contributions to the pendulum test by modeling sensory feedback pathways based on either joint position and velocity to represent muscle length and velocity, or active torque and derivative of active torque to represent muscle fiber force and derivative of force. All model parameter values were held constant over the time course of each simulation. To simulate different degrees of spasticity, we altered both muscle tone and the sensitivity of the simulated reflex pathways, i.e. feedback gain values. Our evaluations of the model were based on published kinematic trajectories of the pendulum test in individuals with CP. We were able to reproduce all three key features of the pendulum test associated with increased spasticity: 1) reduced amplitude of the first swing excursion, 2) reduced number of oscillations, and 3) less vertical resting angle. The simulations are freely available (simtk.org) such that other researchers can reproduce our results and perform additional analyses.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Friedl De Groote,Kyle Blum,Lena Ting,Brian Horslen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1582","unix_group_name":"srs-hill","modified":"1536605294","downloads":"0","group_name":"Short range stiffness","logo_file":"","short_description":"SRS as modeled by de groote","long_description":"SRS as modeled by de groote","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thijs Franzen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1584","unix_group_name":"gait-run","modified":"1537377719","downloads":"0","group_name":"Gait simulation in running","logo_file":"","short_description":"The aim of the project is to see how the human body behaves when doing day to day activities such as walking, running or jumping. The data collected will be used in developing advance bio-mechanical systems to improve human mobility.","long_description":"The aim of the project is to see how the human body behaves when doing day to day activities such as walking, running or jumping. The data collected will be used in developing advance bio-mechanical systems to improve human mobility.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ionut Maries","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1585","unix_group_name":"readysim","modified":"1570643079","downloads":"147","group_name":"ReadySim: Build Finite Element Musculoskeletal Simulations from Laboratory Data","logo_file":"readysim","short_description":"ReadySim aims to provide a means for researchers to perform musculoskeletal simulations directly in a finite element framework. Software and models are made open source to improve the dissemination of knowledge and drive continuous improvements to modeling design and techniques.","long_description":"ReadySim aims to provide a means for researchers to perform musculoskeletal simulations directly in a finite element framework. Both the framework and the models are made open source to improve the dissemination of knowledge and drive continuous improvements to model design and modeling techniques.\n\nThe software uses MATLAB and Python to interface with ABAQUS/Explicit input and output files and includes modules for model segment scaling, kinematics estimation, and muscle force optimization. The JobQueue API allows for asynchronous process control via MATLAB to parallelize optimization problems and improve computational runtime when possible.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Kevin Shelburne,Donald Hume","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1587","unix_group_name":"akone","modified":"1537465777","downloads":"0","group_name":"My Walk","logo_file":"","short_description":"Analyse my walk from 17.09.2018","long_description":"Analyse my walk from 17.09.2018","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Alessia Kober","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1596","unix_group_name":"2018vw","modified":"1539806415","downloads":"0","group_name":"2018 Fall OpenSim Virtual Workshop","logo_file":"","short_description":"The 2018 Fall Virtual Workshop will bring together a group of international scholars and OpenSim experts to help each other advance their research using modeling and simulation. This forum is a place to accelerate OpenSim based projects while also buildin","long_description":"The 2018 Fall Virtual Workshop will bring together a group of international scholars and OpenSim experts to help each other advance their research using modeling and simulation. This forum is a place to accelerate OpenSim based projects while also building relationships with a network of researchers and engineers who are using OpenSim for research.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"jimmy d,Ajay Seth,Christopher Dembia,Jennifer Hicks,Joy Ku","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1600","unix_group_name":"pc-lasso-pet","modified":"1549061114","downloads":"13","group_name":"Application of PCA and LASSO methods to visualize and quantify neurodegeneration","logo_file":"pc-lasso-pet","short_description":"Data-driven, voxel-based analysis of brain PET images: application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration.","long_description":"This is a public data repository for the PLOS ONE (https://journals.plos.org/plosone/) publication entitled &quot;Data-driven, voxel-based analysis of brain PET images: application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration&quot;. The repository contains imaging and clinical data from healthy controls and Parkinson's disease patients that is necessary to replicate the findings of the study.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ivan Klyuzhin","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"1601","unix_group_name":"biphasicbf","modified":"1543607073","downloads":"34","group_name":"Biphasic Blood Flow Simulation","logo_file":"","short_description":"The simulation results from biphasic blood flow.","long_description":"The simulation results from biphasic blood flow.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Grant Hartung","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1603","unix_group_name":"refcurv","modified":"1540836418","downloads":"0","group_name":"RefCurv: A Software for the Construction of Pediatric Reference Curves","logo_file":"refcurv","short_description":"RefCurv is a software providing methods to create pediatric reference curves from data. Furthermore, it comes with a big number of features to analyze reference curves. The graphical user interface (GUI) is written in Python. RefCurv uses R and the GAMLSS","long_description":"RefCurv is a software providing methods to create pediatric reference curves from data. Furthermore, it comes with a big number of features to analyze reference curves. The graphical user interface (GUI) is written in Python. RefCurv uses R and the GAMLSS add-on package as the underlying statistical engine.\n\nFor more information: refcurv.com","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Christian Winkler","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1607","unix_group_name":"imuerrordetect","modified":"1541617770","downloads":"0","group_name":"Using IMU data for Detection of Movement Errors","logo_file":"","short_description":"Simulated wearable sensors are attached to a dynamic human model which is executing a movement.  Analysis is done to detect and classify the errors in the movement only from the simulated sensors.","long_description":"Simulated wearable sensors are attached to a dynamic human model which is executing a movement.  Analysis is done to detect and classify the errors in the movement only from the simulated sensors.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Rowan Ferrabee","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1608","unix_group_name":"mcomp","modified":"1562877401","downloads":"20","group_name":"Tool for Modeling Coactivation Muscle Patterns (MCoMP)","logo_file":"","short_description":"MCoMP computes muscle activations that optimally brace for impact. MCoMP also allows for comparison of different objective functions that intentionally result in coactivation of muscles. ","long_description":"Determining muscle activations required to &quot;brace for impacts&quot; requires optimization techniques that employ objective functions other than what are commonly used in OpenSim (such as in So and CMC). Here we present a tool (MCoMP) in the form of MATLAB script that allows for comparison of different objective functions that intentionally result in coactivation of muscles. MCoMP accepts as inputs an OpenSim model, joints to be stiffened through coactivation, and loading parameters of a force to be resisted. MCoMP outputs forward simulations that utilize muscle activations obtained through objective functions from previous literature and an objective function that we developed. This builds on our previous work (Mortensen, J., et al., 2018. Exploring novel objective functions for simulating muscle coactivation in the neck. Journal of Biomechanics), and will be made available here, pending approval of a manuscript currently in development.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jonathan Mortensen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1609","unix_group_name":"stiffness","modified":"1590740397","downloads":"57","group_name":"Stiffness Modulation of Redundant Musculoskeletal Systems","logo_file":"stiffness","short_description":"Computing the task and joint stiffness using inverse methods that account for the musculoskeletal redundancy effects.","long_description":"This work presents a framework for computing the limbs' stiffness using inverse methods that account for the musculoskeletal redundancy effects. The musculoskeletal task, joint and muscle stiffness are regulated by the central nervous system towards improving stability and interaction with the environment during movement. Many pathological conditions, such as Parkinson's disease, result in increased rigidity due to elevated muscle tone in antagonist muscle pairs, therefore the stiffness is an important quantity that can provide valuable information during the analysis phase. Musculoskeletal redundancy poses significant challenges in obtaining accurate stiffness results without introducing critical modeling assumptions. Currently, model-based estimation of stiffness relies on some objective criterion to deal with muscle redundancy, which, however, cannot be assumed to hold in every context. To alleviate this source of error, our approach explores the entire space of possible solutions that satisfy the action and the physiological muscle constraints. Using the notion of null space, the proposed framework rigorously accounts for the effect of muscle redundancy in the computation of the feasible stiffness characteristics. To confirm this, comprehensive case studies on hand movement and gait are provided, where the feasible endpoint and joint stiffness is evaluated. Notably, this process enables the estimation of stiffness distribution over the range of motion and aids in further investigation of factors affecting the capacity of the system to modulate its stiffness. Such knowledge can significantly improve modeling by providing a holistic overview of dynamic quantities related to the human musculoskeletal system, despite its inherent redundancy.\n\nhttps://github.com/mitkof6/musculoskeletal-stiffness","has_downloads":true,"keywords":"musculoskeletal system,Neuromuscular control,stiffness,redundancy","ontologies":"","projMembers":"Dimitar Stanev,Konstantinos Moustakas","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1611","unix_group_name":"rfs-ffs-pfs","modified":"1607049960","downloads":"163","group_name":"Plantar Flexor Mechanics during Rearfoot Striking and Forefoot Striking Running","logo_file":"rfs-ffs-pfs","short_description":"Simulations of plantar flexor mechanics for 16 runners using both a rearfoot striking and forefoot striking pattern. Results were generated in OpenSim 3.3.","long_description":"Running is thought to be efficient largely due to elastic energy storage in muscles and tendons, particularly the plantar flexor muscles and the Achilles tendon. Although plantar flexor muscle mechanics have been explored during rearfoot striking, little is known about how converting to a forefoot striking running pattern affects energy storage in the Achilles tendon or alters demands placed on the plantar flexor muscles. This study examines how plantar flexor muscle-tendon mechanics during running differ between rearfoot and forefoot striking. Plantar flexor mechanics were estimated using musculoskeletal simulations driven by joint angles and electromyography recorded from runners using both rearfoot and forefoot striking running patterns.  The simulations revealed that foot strike pattern affected the gastrocnemius and the soleus differently. For the gastrocnemius, forefoot striking resulted in greater force generation ability (i.e. the force generated per unit of activation) and increased negative fiber work compared to rearfoot striking. For the soleus, forefoot striking resulted in decreased energy storage in the tendon and decreased positive fiber work compared to rearfoot striking. Forefoot striking appears to place greater demands on the gastrocnemius and take advantage of its improved force generation ability.  Based on increased activation and negative fiber work during early stance, runners interested in altering their foot strike pattern should be mindful of the increased demands on the gastrocnemius when converting to forefoot striking.  ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Christopher Dembia,Scott Delp,Jennifer Yong","trove_cats":[{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1612","unix_group_name":"luteolin","modified":"1542308744","downloads":"0","group_name":"DETERMINATION OF 3D STRUCTURE FOR APTAMER","logo_file":"","short_description":"This is my final year under graduate project.","long_description":"This is my final year under graduate project.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"siti hajar abd rahim","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1613","unix_group_name":"greyhoundleg","modified":"1621530330","downloads":"0","group_name":"Musculoskeletal simulation of a greyhound hindlimb","logo_file":"greyhoundleg","short_description":"From this paper: Ellis, R., Rankin, J.W., Hutchinson, J.R. 2018. Limb kinematics, kinetics and muscle dynamics during the sit-to-stand transition in greyhounds. Frontiers in Bioengineering and Biotechnology 6:162. doi: 10.3389/fbioe.2018.00162","long_description":"From this paper: Ellis, R., Rankin, J.W., Hutchinson, J.R. 2018. Limb kinematics, kinetics and muscle dynamics during the sit-to-stand transition in greyhounds. Frontiers in Bioengineering and Biotechnology 6:162. doi: 10.3389/fbioe.2018.00162\n\nMore data at:\nhttps://figshare.com/projects/Limb_Kinematics_Kinetics_and_Muscle_Dynamics_During_the_Sit-to-Stand_Transition_in_Greyhounds_data/55904","has_downloads":false,"keywords":"","ontologies":"","projMembers":"John Hutchinson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1615","unix_group_name":"newguy","modified":"1543259551","downloads":"0","group_name":"seated leg extension_UCCS Human Kinetics","logo_file":"","short_description":"Computational modeling of seated leg extension exercise to sadlk","long_description":"Computational modeling of seated leg extension exercise to sadlk","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Nathan Katzer","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1617","unix_group_name":"openarm","modified":"1696178083","downloads":"12538","group_name":"OpenArm: Volumetric &amp; Time Series Models of Muscle Deformation","logo_file":"openarm","short_description":"The OpenArm data sets are designed to enable study of force- and kinematic-induced muscle deformation for applications in biomechanics research, computer graphics, and assistive device development.\n\n**NEW:** The OpenArm Multisensor 2.0 data set contains new time series of ultrasound-measured brachioradialis deformation, surface electromyography (sEMG)-measured activation, force, and goal trajectory data, for various force-, deformation-, and activation-based trajectory tracking tasks, under refined conditions from those of the 1.0 data set. Also included are all analysis, real-time muscle deformation tracking, and display code.\n\nThe OpenArm Multisensor 1.0 data set contains time series cross-sectional ultrasound scans of the brachioradialis muscle under variable elbow loading, alongside corresponding surface electromyography (sEMG), acoustic myography (AMG), and force data.\n\nThe OpenArm 1.0 and 2.0 data sets encompass factorial sets of volumetric scans of the arm, generated using ultrasound and motion capture, that allow for analysis of both force- and configuration-associated muscle deformation.","long_description":"We invite anyone in the research community to use the OpenArm and OpenArm Multisensor data sets to validate existing muscle deformation models or to devise new ones.\n\nFull details can be found in the following papers:\n\nLaura A. Hallock, Bhavna Sud, Chris Mitchell, Eric Hu, Fayyaz Ahamed, Akash Velu, Amanda Schwartz, and Ruzena Bajcsy. &quot;Toward Real-Time Muscle Force Inference and Device Control via Optical-Flow-Tracked Muscle Deformation.&quot; In IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE). IEEE, 2021. (Under review.)\n\nLaura Hallock, Akash Velu, Amanda Schwartz, and Ruzena Bajcsy. &quot;Muscle deformation correlates with output force during isometric contraction.&quot; In IEEE RAS/EMBS International Conference on Biomedical Robotics &amp; Biomechatronics (BioRob). IEEE, 2020. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/hallock2020biorob.)\n\nYonatan Nozik*, Laura A. Hallock*, Daniel Ho, Sai Mandava, Chris Mitchell, Thomas Hui Li, and Ruzena Bajcsy, &quot;OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics,&quot; in International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019. *Equal contribution. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/nozikhallock2019embc.)\n\nLaura Hallock, Akira Kato, and Ruzena Bajcsy. &quot;Empirical quantification and modeling of muscle deformation: Toward ultrasound-driven assistive device control.&quot; In IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/hallock2018icra.)\n\nThis project is currently in development in the Human-Assistive Robotic Technologies (HART) Lab at the University of California, Berkeley (http://hart.berkeley.edu).","has_downloads":true,"keywords":"calibrated neuromusculoskeletal modelling,musculoskeletal system,Muscle forces,NeuroMusculoSkeletal Modeling,muscle force,Muscle wrapping,neuromusculoskeletal modelling,neuromusculoskeletal simulation,Muscle moment arms,skeletal muscle- mechanical model,muscle,muscle architecture parameters,Muscle modeling,muscle modeling,muscle synergies,muscle synergies,muscle architecture parameters,,Muscle Architecture,muscle function,Muscle-tendon length,Muscle loading,Muscle geometry,muscle force prediction,muscle force validation,ultrasound,motion capture,Image segmentation,tissue mechanics","ontologies":"Shape_Analysis,Image_Processing,Physioloigcal_Model,Individual_Human_Data,Imaging,Three_Dimensional_Data,Publication,Structural_Model,Modeling_and_Simulation,Data_Exploration,Neuromuscular_Model","projMembers":"Akash Velu,Bhavna Sud,Laura Hallock,Daniel Ho,Akira Kato,Fayyaz Ahamed,Jaeyun  Seo,Yonatan Nozik,Gregorij Kurillo,Christopher Mitchell,Eric Hu","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"1618","unix_group_name":"subj-spec-model","modified":"1649659419","downloads":"107","group_name":"Materials to build MSK models from medical images","logo_file":"subj-spec-model","short_description":"This project aims to collect materials (guide and scripts) to guide users in creating musculoskeletal models of the lower limb from medical images.","long_description":"This project aims to collect materials (guide and scripts) to guide users in creating musculoskeletal models of the lower limb from medical images.\nCurrently the material associated with the published paper is available from download at this website in its basic version, \nAn extended version of this material, including results of the simulations is also available at Figshare.com, via the following links:\n1) the &lt;a href=&quot;https://figshare.com/articles/Data_for_paper_Investigation_of_the_dependence_of_joint_contact_forces_on_musculotendon_parameters_using_a_codified_workflow_for_image-based_modelling_/5863422&quot;&gt; first Figshare link &lt;/a&gt; includes a detailed guide on how to build the models starting from the segmented bones and an archive with all the simulations run during the study.\n2) the &lt;a href=&quot;https://figshare.com/articles/Code_for_paper_Investigation_of_the_dependence_of_joint_contact_forces_on_musculotendon_parameters_using_a_codified_workflow_for_image-based_modelling_/6392423&quot;&gt; second Figshare link &lt;/a&gt;  points to a collection of simple matlab scripts referenced in the codified procedure.\nPlease note that the procedure streamlines multiple programs: &lt;ul&gt;&lt;li&gt;&lt;a href=&quot;http://www.nmsbuilder.org/&quot;&gt; NMSBuilder &lt;/a&gt; &lt;/li&gt;&lt;li&gt;&lt;a href=&quot;http://www.meshlab.net/&quot;&gt; Meshlab &lt;/a&gt;&lt;/li&gt;&lt;li&gt; &lt;a href=&quot;http://www.eurometros.org/gen_report.php?category=distributions&amp;pkey=14&amp;subform=yes&quot;&gt; LSGE Matlab library&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;\nYou will need to download and install them from the provided links.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Luca Modenese,Erica Montefiori","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1619","unix_group_name":"biomechanicsss3","modified":"1543606769","downloads":"0","group_name":"Biomechanics Elite Jumper Model","logo_file":"","short_description":"This is a project for my final in biomechanics taught by David Corr at Rensselaer Polytechnic Institute.","long_description":"This is a project for my final in biomechanics taught by David Corr at Rensselaer Polytechnic Institute.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Shreyas Sriram","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1620","unix_group_name":"lvdatamap","modified":"1559570977","downloads":"153","group_name":"Building Personalized Left Ventricular Models from Cardiac MRI Data","logo_file":"lvdatamap","short_description":"This project provides MATLAB and Python code to register multiple imaging protocols to create a patient-specific finite element mesh of a left ventricle suitable for simulation in FEBio. This work was recently published in the Journal of Biomechanical Engineering \"Open-Source Routines for Building Personalized Left Ventricular Models from Cardiac MRI\" (doi:10.1115/1.4043876)","long_description":"This project provides MATLAB and Python code to register multiple imaging protocols to create a patient-specific finite element mesh of a left ventricle suitable for simulation in FEBio. The imaging data sets used were processed in the freely available segmentation software Segment (http://medviso.com/segment/). We use landmark locations in the LV to register different imaging data into a common cardiac coordinate system and pass information via the epicardial surface.\n\nTwo fitting examples are provided- fitting LGE MRI scar and DENSE MRI mechanical activation.\nThe data files are provided under &quot;Downloads&quot;\nThe library of scripts are provided under &quot;Source Code&quot;","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Thien-Khoi Phung,Christopher Waters","trove_cats":[{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1622","unix_group_name":"backwardswalkin","modified":"1544043967","downloads":"0","group_name":"Backwards Walking","logo_file":"","short_description":"I'm trying to create a gait cycle figure for backwards walking","long_description":"I'm trying to create a gait cycle figure for backwards walking","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Henry Do","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1623","unix_group_name":"menigitis","modified":"1544116845","downloads":"0","group_name":"dentification of inhibitors for the E.coli meningitis virulence factor IbeA","logo_file":"","short_description":"This project is to use combined biological assays and computational methods to design and identify the inhibitors for bacterial meningitis.","long_description":"This project is to use combined biological assays and computational methods to design and identify the inhibitors for bacterial meningitis.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"xiaoqian xu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1624","unix_group_name":"mcclain","modified":"1651721150","downloads":"152","group_name":"Simulating human machine interaction for the development of assistive devices.","logo_file":"mcclain","short_description":"Simulating human machine interaction for the development of assistive devices.","long_description":"As human assistive devices become more advanced the development and testing of these devices may need to include the use of simulation. We find this to be the case for a device currently in development, a quadrupedal human assistance device. With the desired behavior of this system being similar to the standard wheeled walker, we can use the behavior of humans using this system to evaluate the performance of our system and controls in simulation. This work presents a method for evaluating the performance of such a system using experimental data gathered in an ideal use scenario and extending this to a simulation of the new system and the user. This simulation allows for design and testing to occur without needing constant access to test subjects and without placing users at risk of injury from an improperly functioning device.\n\nThis work is supported by the NIH/NINR under grant #R01NR016151","has_downloads":true,"keywords":"stability,assistive devices,Robotics,human machine interaction","ontologies":"","projMembers":"Eric McClain","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"1627","unix_group_name":"ad4d","modified":"1544406913","downloads":"0","group_name":"AD4D-Study: CFD / 4D Flow in Aortic dissection","logo_file":"","short_description":"In vitro / in silico / in vivo comparison of flow and wall shear stress changes in aortic dissection and surgical treated aortas with a focus on 4D flow at 3T.","long_description":"In vitro / in silico / in vivo comparison of flow and wall shear stress changes in aortic dissection and surgical treated aortas with a focus on 4D flow at 3T.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Tilman Emrich","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1631","unix_group_name":"pfdeficitsgait","modified":"1653662330","downloads":"591","group_name":"Predicting gait adaptations due to plantarflexor muscle weakness and contracture","logo_file":"","short_description":"This project contains files related to the paper, \"Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations\" by Ong, Geijtenbeek, Hicks, and Delp. Pre-print available: https://doi.org/10.1101/597294","long_description":"Using an optimization framework (SCONE) and a musculoskeletal modeling package (OpenSim), we generated realistic simulations of walking at many speeds. We then modeled muscle weakness or contracture in the ankle plantarflexor muscles to study how these deficits contribute to observed gait changes.\n\nThis project provides results from all of our simulations. We also provide Docker build instructions and SCONE setup files for others to reproduce our results.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jennifer Hicks,Scott Delp,Carmichael Ong,Thomas Geijtenbeek","trove_cats":[{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1632","unix_group_name":"predicsubjexosk","modified":"1566888819","downloads":"140","group_name":"Prediction of subject-exoskeleton collaborative movements and their interaction","logo_file":"","short_description":"This project deals with the calibration of foot-ground and subject-exoskeleton models to then predict new subject-exoskeleton collaborative movements and their forces.","long_description":"We developed a calibration process to estimate the parameter values of foot-ground contact and subject-exoskeleton contact models, so that these models can reproduce experimental data of sit-to-stand trials with the exoskeleton in passive mode. Then, we can predict new sit-to-stand movements of the subject wearing an exoskeleton in active mode.\n\nThis project provides the code used to run the three phrases described in the manuscript (https://ieeexplore.ieee.org/document/8744250). Phase A: foot-ground contact model calibration; Phase B: subject-exoskeleton contact model calibration; Phase C: prediction of collaborative  movement and subject-exoskeleton interaction forces. It also contains a description of the code and the experimental data (motion capture, ground reaction and subject-exoskeleton data).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Friedl De Groote,Christopher Dembia,Gil Serrancolí","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1635","unix_group_name":"fullbody-py","modified":"1547016465","downloads":"0","group_name":"OpenSim Full Body Model with Python","logo_file":"","short_description":"This project extends Full Body Model for use in Dynamic Simulations of Human Gait with Python scripts which are equivalent to the Matlab scrips provided with the model.\n\nIt could be useful as an example of porting Matlab code to Python.\n","long_description":"The project Full Body Model for use in Dynamic Simulations of Human Gait (https://simtk.org/projects/full_body) uses Matlab as a programming language. \n\nHere we present Python code that was derived from the original Matlab code and is functionally equivalent to it.  The names of scripts, the comments, and the names of variables were preserved where possible.\n\nThe scripts were tested on Xubuntu 16.04.5 LTS 64\n\nProject files and instructions are available at github: \nhttps://github.com/rebrik/OpenSimFullBodyWithPython\n\n","has_downloads":false,"keywords":"OpenSim,Python,full-body model","ontologies":"","projMembers":"Sergei Rebrik","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1637","unix_group_name":"mrmc","modified":"1547656894","downloads":"0","group_name":"Middle-way flexible docking using mixed-resolution Monte Carlo in ER α","logo_file":"","short_description":"Supporting data and plotting scripts for our paper \"Middle-way flexible docking:  Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α,\" to be submitted for publication in PLOS One.","long_description":"Supporting data and plotting scripts for our paper &quot;Middle-way flexible docking:  Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α,&quot; to be submitted for publication in PLOS One.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Justin Spiriti","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1638","unix_group_name":"fes-prop-sym","modified":"1671041679","downloads":"367","group_name":"FastFES Optimization for Propulsive Force Symmetry","logo_file":"","short_description":"This project contains Matlab code for calibrating an OpenSim-based musculoskeletal model to a specific individual. Data sets collected for an individual in post-stroke gait rehabilitation are also included. Further Matlab code is included for optimal cont","long_description":"This project contains Matlab code for calibrating an OpenSim-based musculoskeletal model to a specific individual. Data sets collected for an individual in post-stroke gait rehabilitation are also included. Further Matlab code is included for optimal control of the calibrated model using GPOPS-II for the purpose of optimizing FastFES stimulation parameters to improve post-stroke gait propulsive force symmetry.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"B.J. Fregly,Nathan Sauder","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1643","unix_group_name":"3dpredictsim","modified":"1564199869","downloads":"216","group_name":"Rapid 3D muscle-driven predictive simulations","logo_file":"","short_description":"This project contains code and data to perform 3D muscle-driven predictive simulations of gait.","long_description":"Predictive simulations of human movement have great potential but are often limited by large computational costs. In this project, we developed an OpenSim-based framework to perform computationally efficient predictive simulations of movement. \n\nThe framework relies on numerical tools including direct collocation, implicit differential equations, and algorithmic differentiation, and generates predictive simulations of gait in about 35 minutes (single core of a standard laptop computer) with muscle-driven 3D models (29 degrees of freedom and 92 muscles). \n\nThe code contains a series of example predictive simulations in which we varied objective function, musculoskeletal properties, and gait speed. Details of the results can be found in the associated publication: Falisse et al, Rapid predictive simulations with complex musculoskeletal models suggest that diverse healthy and pathological human gaits can emerge from similar control strategies, Journal of the Royal Society Interface (2019), in press.\n\nThe released folder contains readme files with details of the folder structure. Please contact me for any questions (antoine.falisse@kuleuven.be) or post an issue on GitHub https://github.com/antoinefalisse/3dpredictsim.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Friedl De Groote,Christopher Dembia,Ilse Jonkers,Gil Serrancolí","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1645","unix_group_name":"forefootfea","modified":"1548355753","downloads":"20","group_name":"Optimization of nonlinear hyperelastic coefficients for foot tissues","logo_file":"","short_description":"","long_description":"A forefoot model for finite element analysis is disseminated in this project. The model was used for optimization of nonlinear hyperelastic coefficients of foot tissues. Prediction of the internal stress distribution using finite element models requires that realistic descriptions of the material properties of the soft tissues are incorporated into the model. The three-dimensional forefoot model includes multiple soft tissue layers (skin, fat pad, and muscle).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ahmet Erdemir","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1648","unix_group_name":"massive","modified":"1548899919","downloads":"0","group_name":"A novel approach for Sensitivity analysis of agent-based simulation","logo_file":"","short_description":"An essential step in the analysis of agent-based simulation is sensitivity analysis, which namely examines the dependency of parameter values on simulation results. Although a number of approaches have been proposed for sensitivity analysis, they still ha","long_description":"An essential step in the analysis of agent-based simulation is sensitivity analysis, which namely examines the dependency of parameter values on simulation results. Although a number of approaches have been proposed for sensitivity analysis, they still have limitations in exhaustivity and interpretability. In this study, we propose a novel methodology for sensitivity analysis of agent-based simulation, MASSIVE (Massively parallel Agent-based Simulations and Subsequent Interactive Visualization-based Exploration). MASSIVE takes a unique paradigm, which is completely different from those of sensitivity analysis methods developed so far, By combining massively parallel computation and interactive data visualization, MASSIVE enables us to inspect a broad parameter space intuitively. We demonstrated the utility of MASSIVE by its application to cancer evolution simulation, which successfully identified conditions that generate heterogeneous tumors. We believe that our approach would be a de facto standard for sensitivity analysis of agent-based simulation in an era of evergrowing computational technology.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Atsushi Niida","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1650","unix_group_name":"vhk","modified":"1549478400","downloads":"0","group_name":"The Virtual Human Knee: ligament &amp; articular surface mechanics","logo_file":"","short_description":"The Virtual Human Knee (VHK) is a model of ligament and  articular surface mechanics in healthy adults combining imaging, knee testing, human experiments and statistical methods.","long_description":"The Virtual Human Knee (VHK) aims at quantifying individual variations in ligament and tibiofemoral articular surface mechanics across healthy adults. To this purpose, imaging, 6DOF testing of knee specimens, human experiments and statistical methods are used. The results will constitute e reference database for optimizing orthopedic practice toward personalized knee reconstruction surgery.\n\nMirror website: https://www.researchgate.net/project/The-Virtual-Human-Knee-ligament-and-articular-surface-mechanics-in-healthy-adults","has_downloads":false,"keywords":"","ontologies":"","projMembers":"saulo martelli","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1652","unix_group_name":"cpsoleusdti","modified":"1549450307","downloads":"0","group_name":"Diffusion tensor imaging of soleus muscles in cerebral palsy","logo_file":"","short_description":"Cerebral palsy (CP) is associated with movement disorders and reduced muscle size. This latter phenomenon has been observed by computing muscle volumes from conventional MRI, with most studies reporting significantly reduced volumes in leg muscles. This i","long_description":"Cerebral palsy (CP) is associated with movement disorders and reduced muscle size. This latter phenomenon has been observed by computing muscle volumes from conventional MRI, with most studies reporting significantly reduced volumes in leg muscles. This indicates impaired muscle growth, but without knowing muscle fiber orientation, it is not clear whether muscle growth in CP is impaired in the along-fiber direction (indicating shortened muscles and limited range of motion) or the cross-fiber direction (indicating weak muscles and impaired strength). Using Diffusion Tensor Imaging (DTI) we determined muscle fiber orientations and constructed 3D muscle architectures to examine both along-fiber length and cross-sectional area. This is the first approach to our knowledge to use DTI applied to the muscles of subjects with CP. In this project, we present fiber vector data and soleus muscle masks to be used in subsequent analysis and modeling of muscle architecture data in CP.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Geoffrey Handsfield","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1655","unix_group_name":"gsmorph","modified":"1549652059","downloads":"0","group_name":"G alpha S morph","logo_file":"","short_description":"Morph between G alpha S in GDP bound form and G0 state","long_description":"Morph between G alpha S in GDP bound form and G0 state","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sebastian Furness","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1660","unix_group_name":"febioccj","modified":"1554476695","downloads":"82","group_name":"FEBio Finite Element Models of the Craniocervical Junction","logo_file":"febioccj","short_description":"Developing open access finite element models of the human craniocervical junction.","long_description":"The purpose of this project is to develop open access finite element models of the human craniocervical junction using open source software (FEBio Software Suite (https://febio.org/)).  Models are developed from CT image data segmented in our laboratory (https://mrl.sci.utah.edu/) and from importing models to FEBio that were first developed for commercial codes. This project is a collaboration between the Departments of Biomedical Engineering and Neurosurgery at the University of Utah, and we work closely with the FEBio development team.\n\nPlease see the following references for details about the models and cite these references when using the models for further publications:\n\nPhuntsok R, Mazur MD, Ellis BJ, Ravindra VM, Brockmeyer DL: Development and initial evaluation of a finite element model of the pediatric craniocervical junction. Journal of Neurosurgery: Pediatrics, 17(4): 497-503, 2016.\n\nPhuntsok R, Ellis BJ, Herron MR, Provost CW, Dailey AT, Brockmeyer DL: The occipitoatlantal capsular ligaments are the primary stabilizers of the occipitoatlantal joint in the craniocervical junction: a finite element analysis.  Journal of Neurosurgery: Spine, 2019, In Press.\n\nPhuntsok R, Provost CW, Dailey AT, Brockmeyer DL, Ellis BJ: The atlantoaxial capsular ligaments and transverse ligament are the primary stabilizers of the atlantoaxial joint in the craniocervical junction: a finite element analysis.  Journal of Neurosurgery: Spine, 2019, In Press.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Rinchen Phuntsok,Ben Ellis","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1661","unix_group_name":"lfbmodel","modified":"1550715598","downloads":"857","group_name":"Lifting Full-Body Model","logo_file":"","short_description":"The existing full-body-lumbar-spine (FBLS) model was adapted and validated for lifting motions to produce the lifting full-body (LFB) model to estimate spinal loads during lifting. The model was validated through comparisons with experimental electromyogr","long_description":"The existing full-body-lumbar-spine (FBLS) model was adapted and validated for lifting motions to produce the lifting full-body (LFB) model to estimate spinal loads during lifting. The model was validated through comparisons with experimental electromyography muscle activity, reported intradiscal pressures, and reported vertebral body implant measurements. The validation results demonstrated the suitability of this model to evaluate changes in lumbar loading during lifting.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Brandon,Dominic Thewlis,Will Robertson,Ryan Graham,Erica Beaucage-Gauvreau","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1665","unix_group_name":"carpal-database","modified":"1591643936","downloads":"340","group_name":"Wrist Anatomy and Kinematics Data Collection","logo_file":"carpal-database","short_description":"The current collection includes carpal bone (not metacarpals) anatomy models from 90 healthy subjects (120 wrists), and the carpal bone kinematics in 1215 unique wrist positions. A graphical user interface (GUI) is also developed to maximize user interaction with this collection.","long_description":"&lt;div align=&quot;justify&quot;&gt;CT images of wrists from 90 healthy volunteers (43 males and 47 females) were acquired in various wrist positions. The outer cortical surfaces of the carpal bones, radius, and ulna in a 3D format, and each bone kinematics were calculated for each wrist position using a methodology described in the README file associated with the database. The database does not include soft tissue or the cartilage information of the wrist. Moreover, there is a MATLAB graphic user interface (GUI) available for you to observe the database. This dataset comes from four different NIH funding between 2001 and 2014.&lt;/div&gt;\n\nPlease cite the work if you're using this database:\n&lt;div align=&quot;justify&quot;&gt;&lt;a href=&quot;https://onlinelibrary.wiley.com/doi/abs/10.1002/jor.24435&quot;&gt;Akhbari, B., Moore, D. C., Laidlaw, D. H., Akelman, E., Weiss, A-P. C., Wolfe, S. W., Crisco, J. J., 2019. Predicting Carpal Bone Kinematics using an Expanded Digital Database of Wrist Carpal Bone Anatomy and Kinematics, Journal of Orthopaedic Research. DOI:10.1002/jor.24435&lt;/a&gt;&lt;/div&gt;\n\nIf you want the pdf version of the manuscript, please send your request on &lt;a href=&quot;http://bit.ly/2YU2tTh&quot;&gt;ResearchGate&lt;/a&gt;.\n","has_downloads":true,"keywords":"bone models,carpal database, kinematics model,wrist anatomy,wrist motion","ontologies":"","projMembers":"J.J. Trey Crisco,Bardiya Akhbari","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"1672","unix_group_name":"guineafowl","modified":"1578582820","downloads":"53","group_name":"Guinea Fowl Model","logo_file":"","short_description":"This project includes model files and descriptions for an OpenSim musculoskeletal model of the guinea fowl (Numida meliagris) pelvic limb.","long_description":"We used the guinea fowl (Numida meliagris) pelvic limb model in an initial study explaining the development of the model including muscle-tendon unit modeling and joint modeling.  The study implements the model to assess the interaction between activation level and muscle-tendon unit compliance on muscle force-length operating ranges and force generating capacity. \n\nS M Cox, K L Easton, M Cromie Lear, R L Marsh, S L Delp, J Rubenson (2019). The interaction of compliance and activation on the force-length operating range and force generating capacity of skeletal muscle: a computational study using a guinea fowl musculoskeletal model, Integrative Organismal Biology, obz022, https://doi.org/10.1093/iob/obz022\n\nSupported in part through a Company of Biologists Travelling Fellowship to J.R. and in part through The National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under award number R21AR071588. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jonas Rubenson,zanne cox","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1673","unix_group_name":"stab-so","modified":"1616027149","downloads":"149","group_name":"MATLAB-based framework for implementing mechanical joint stability within SO","logo_file":"","short_description":"In this project, we developed a MATLAB-based framework for OpenSim to calculate muscle activations and forces that also satisfy mechanical stability requirements.  ","long_description":"In this project, we developed a MATLAB-based framework to calculate muscle forces that also satisfy mechanical stability requirements. \n\nAny user of these functions please cite: \nAkhavanfar, M. H., Brandon, S. C., Brown, S. H., &amp; Graham, R. B. (2019).  Development of a novel matlab-based framework for implementing mechanical joint stability constraints within OpenSim musculoskeletal models. Journal of Biomechanics (https://doi.org/10.1016/j.jbiomech.2019.05.007).\n\nIn this project, we used the powerful features of OpenSim such as inverse dynamics, point kinematics analysis, muscle analysis, etc., to make our framework applicable to many OpenSim models . We tested this framework for a new developed spine model, as well as the gait2392 model to show many models regardless of the number of joints and degrees of freedom can use the framework. \n\nWe compared our results and the results that obtained by the default static optimization solver against EMG data and showed that the calculated muscle activations by our new framework are more promising than those calculated by the current static optimization.\n\nThis framework is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Brandon,Ryan Graham,Mohammadhossein Akhavanfar,Stephen Brown","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1675","unix_group_name":"gaitmodel","modified":"1553106361","downloads":"0","group_name":"Gait modeling of human with impaired mobility","logo_file":"","short_description":"People with reduced or impaired mobility often rely on mechanical support. The current designs of such artificial support tend to focus on functional compensation, which does not necessarily encourage the user to walk independently nor help recover to a n","long_description":"People with reduced or impaired mobility often rely on mechanical support. The current designs of such artificial support tend to focus on functional compensation, which does not necessarily encourage the user to walk independently nor help recover to a normal gait in the long run. To the contrary, some aids are known to enforce the user’s dependency on the support and as a result, fail to exploit human adaptability. For the design of adaptable human-machine interaction, there is a fundamental need for quantitative models that consider, not only musculo-skeletal patterns of gait, but also cognitive modes involved (e.g., willingness to walk on their own, or the degree to which the user depends on machine). To address the gap, this paper aims to develop and test an integrative cognitive-musculoskeletal model that can represent and simulate human gait for the humans with impaired mobility.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Inki Kim","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"1676","unix_group_name":"muscleprofile","modified":"1554403020","downloads":"0","group_name":"Muscle Force Profile and Muscle Profile Score","logo_file":"muscleprofile","short_description":"The aim of this project was to find a neat way to visualize and summarize muscle force results from musculoskeletal simulations.","long_description":"The Muscle Force Profile (MFP) and Muscle Profile Score (MPS), similar to the Gait Profile Score (https://www.ncbi.nlm.nih.gov/pubmed/19632117) analyze and summarize pathological muscle force deviations to typically developing muscle functions during gait. The MFP was considered as a tool to analyze individual muscles (e.g. during clinical gait analysis), whereas the advantage of the MPS is to have an overall score for muscle force deviations, which is useful for statistical analysis.\n\nWe will upload the MATLAB code and reference data within the next three weeks.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Friedl De Groote,Ilse Jonkers,Hoa Hoang,hans kainz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1678","unix_group_name":"spine-children","modified":"1677098062","downloads":"601","group_name":"Full-body models with detailed thoracolumbar spine for children and adolescents","logo_file":"spine-children","short_description":"This project provides validated musculoskeletal full-body models including a detailed thoracolumbar spine for boys and girls aged 6-18 years.","long_description":"We created a total of 26 children and adolescent full-body models (a male and female model for each year of age between 6 and 18 years), consisting of a detailed and fully articulated thoracolumbar spine and rib cage, a lumped head and neck body as well as upper and lower extremities. \n\nInitial version (compatible with OpenSim 3.3):\nMale and female versions of our previously developed and validated “thoracolumbar spine and rib cage model” (https://simtk.org/projects/spine_ribcage) were combined with the “Gait2354 model” (https://simtk-confluence.stanford.edu/display/OpenSim/Gait+2392+and+2354+Models) and adjusted for segmental length and mass distributions, center of mass positions and moments of inertia of the major body segments, sagittal pelvis and spinal alignment as well as trunk muscle cross-sectional areas (CSA) based on data obtained from the literature.  \nThe models were validated by comparing predictions of maximal trunk muscle strength, lumbar disc compressibility, intradiscal pressure and trunk muscle activity to in vivo measurements reported in the literature (https://doi.org/10.1016/j.jbiomech.2019.07.049).\n\nLatest version (compatible with OpenSim 4.x):\nMale and female versions of the most recent release of the thoracolumbar spine and rib cage model (Fullbody_OS4.x_v2.0; https://simtk.org/projects/spine_ribcage) were adjusted for segmental length and mass distributions, center of mass positions and moments of inertia of the major body segments, sagittal pelvis and spinal alignment as well as trunk muscle cross-sectional areas (CSA) based on data obtained from the literature. In addition, the abdomen body was realigned, and the muscle tendon-to-fiber length ratios were adjusted to the new total muscle length resulting from the implementation of age-specific sagittal alignment.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Stefan Schmid,Katelyn Burkhart,Daniel Grindle,Brett Allaire,Dennis Anderson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1680","unix_group_name":"kneejointv1","modified":"1557737571","downloads":"304","group_name":"An extended OpenSim knee model for analysis of strains of connective tissues","logo_file":"","short_description":"OpenSim musculoskeletal models provide an accurate simulation environment that eases limitations of in vivo and in vitro studies. In this work, a bio- mechanical knee model was formulated with femoral articular cartilages and menisci along with 25 connect","long_description":"OpenSim musculoskeletal models provide an accurate simulation environment that eases limitations of in vivo and in vitro studies. In this work, a bio- mechanical knee model was formulated with femoral articular cartilages and menisci along with 25 connective tissue bundles representing ligaments and capsules. The strain patterns of the connective tissues in the presence of femoral articular cartilage and menisci in the OpenSim knee model was probed in a first of its kind study. The effect of knee flexion (0°–120°), knee rotation (− 40° to 30°) and knee adduction (− 15° to 15°) on the anterior cruciate, posterior cruciate, medial collateral, lateral collateral ligaments and other connective tissues were studied by passive simulation. Further, a new parameter for assessment of strain namely, the differential inter-bundle strain of the connective tissues were analysed to provide new insights for injury kinematics. ACL, PCL, LCL and PL was observed to follow a parabolic strain pattern during flexion while MCL represented linear strain patterns. All connective tissues showed non-symmetric parabolic strain variation during rotation. During adduction, the strain variation was linear for the knee bundles except for FL, PFL and TL. Strains higher than 0.1 were observed in most of the bundles during lateral rotation followed by abduction, medial rotation and adduction. In the case of flexion, highest strains were observed in aACL and aPCL. A combination of strains at a flexion of 0° with medial rotation of 30° or a flexion of 80° with rotation of 30° are evaluated as rupture-prone kinematics","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Arnab Sikidar,Dinesh Kalyanasundaram","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1681","unix_group_name":"nlsrv1","modified":"1556372937","downloads":"188","group_name":"Plugin for non-linear strain rate behaviour of dense connective tissues","logo_file":"","short_description":"The force-length characteristics of dense connective tissues\n(DCTs) vary non-linearly as a function of strain rate. However, there is\nno existing class of OpenSim® to incorporate strain rate effect into the\nOpenSim® model. In this work, a new plugin ","long_description":"The force-length characteristics of dense connective tissues\n(DCTs) vary non-linearly as a function of strain rate. However, there is\nno existing class of OpenSim® to incorporate strain rate effect into the\nOpenSim® model. In this work, a new plugin for OpenSim® was developed to\nincorporate the non-linear strain rate behaviour of dense connective\ntissues (DCTs) of the human knee. Experimental force-length plots from\nthe literature was used to extract the shape factor, scale factor, the\ncoefficient of viscosity and elastic stiffness corresponding to specific\nstrain rates. A new class object termed as NonLinearLigament was\nformulated using a custom plugin based on a structural constitutive\nmodel. A test platform was created to evaluate the force-length patterns\nat multiple strain rates ranging from 0.0001 s-1 to 100 s-1 for the DCT\nbundles. Knee kinematics of 25 DCT bundles were subjected to forward\nsimulation at time frames corresponding to failure at dynamic activities.\nTo understand the significance, the force-length characteristics of each\nof the DCTs were simulated as a function of strain rate for both existing\nLigament class of OpenSim® and the proposed NonLinearLigament class.\nDuring simulation of tensile loading, higher forces were observed with an\nincrease of strain rate in DCTs. Existing Ligament class in OpenSim® was\ndevoid of the technique in calculating forces at different strain rates.\nThe developed plugin takes into account the short term viscoelastic\nbehaviour of DCTs and hence, would help in accurate simulation of tissue\nbehaviour understanding their injury mechanics.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Arnab Sikidar,Dinesh Kalyanasundaram","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1688","unix_group_name":"febiocervical","modified":"1603816288","downloads":"261","group_name":"FEBio Finite Element Models of the Human Cervical Spine","logo_file":"febiocervical","short_description":"Developing open access finite element models of the human cervical spine.","long_description":"The purpose of this project is to develop open access finite element models of the human cervical spine using open source software (FEBio Software Suite (https://febio.org/)). We are developing whole cervical spine models (C0-C7) and models of functional spine units (FSUs). Models are developed from CT image data segmented in our laboratory (https://mrl.sci.utah.edu/) and from porting models to FEBio that were first developed for commercial codes. This project is a collaboration between the Departments of Biomedical Engineering and Neurosurgery at the University of Utah, and we work closely with the FEBio development team.\n\nPlease see the following reference for details about the models and cite this reference when using the models for further publications:\n\nHerron MR, Brockmeyer DL, Dailey AT, Ellis BJ:  FEBio finite element models of the human cervical spine.  Journal of Biomechanics, 2020, In Press.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ben Ellis,Michael Herron","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1690","unix_group_name":"lipadesign","modified":"1555529095","downloads":"0","group_name":"LipA Design","logo_file":"","short_description":"Computational redesign of LipA for catalytic activity with Roche ester.","long_description":"Computational redesign of LipA for catalytic activity with Roche ester.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dennis Della Corte","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1693","unix_group_name":"techdevclub","modified":"1556229248","downloads":"0","group_name":"prosthetic foot model 1.0","logo_file":"","short_description":"model of a foot","long_description":"model of a foot","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Nathaniel Shaw","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1695","unix_group_name":"exoss","modified":"1556302089","downloads":"0","group_name":"ExoSS","logo_file":"","short_description":"I study humanoid robotics in Berlin. I have to create an exoskeleton in order to succeed the course. My group and I intend to design an exoskleton which helps the wearer to decrease back pain and increase strengt to carry weights. We need a programm to si","long_description":"I study humanoid robotics in Berlin. I have to create an exoskeleton in order to succeed the course. My group and I intend to design an exoskleton which helps the wearer to decrease back pain and increase strengt to carry weights. We need a programm to simulate the human body and its forces while walking and carrying around weights up to 10kg. Thanks for any help. If any more information needed, do not hesitate to tell us. This is our first experience with your service.\nThank you","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Gyanie Saleh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1696","unix_group_name":"windkessel","modified":"1556307421","downloads":"0","group_name":"Windkessel Parameter Estimation and Initial Test","logo_file":"","short_description":"Testing the use of the windkessel model for CFD models of the aortic arch.","long_description":"Testing the use of the windkessel model for CFD models of the aortic arch.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Linnea Warburton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1698","unix_group_name":"predictfastfes","modified":"1556664361","downloads":"199","group_name":"Computational Prediction of FastFES Muscle Stimulation in Post-stroke Gait","logo_file":"","short_description":"This project provides experimental data for an individual who was classified as a non-responder to post-stroke FastFES gait rehabilitation. Matlab code for predicting muscle stimulation to minimize propulsive force asymmetry is also provided. Beginning wi","long_description":"This project provides experimental data for an individual who was classified as a non-responder to post-stroke FastFES gait rehabilitation. Matlab code for predicting muscle stimulation to minimize propulsive force asymmetry is also provided. Beginning with the raw data, the provided code calibrates a subject-specific OpenSim/Matlab model including skeletal scaling, joint center locations, muscle parameters, foot-ground contact spring parameters, and muscle synergy-based neural control model parameters. Matlab code is also provided for a series of prediction problems designed to predict optimal muscle stimulation parameters (amplitude, timing, and muscle targets) to minimize propulsive force asymmetry between the two legs. Optimal control problems (including the prediction problems, foot-ground contact calibration, and neural control model calibration) utilized GPOPS-II, a software toolbox which operates within the Matlab programming environment.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Nathan Sauder","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1706","unix_group_name":"pykneer","modified":"1557748755","downloads":"0","group_name":"pyKNEEr: Image-based workflow for transparent research on femoral knee cartilage","logo_file":"pykneer","short_description":"","long_description":"pyKNEEr is an image analysis workflow for open and reproducible research on femoral knee cartilage. It is implemented in python using Jupyter notebooks as a user interface. The workflow is divided into three modules for: 1) image preprocessing; 2) segmentation of femoral knee cartilage for intersubject, multimodal, and longitudinal acquisitions; and 3) morphologic and relaxometry analysis.\n\nDocumentation:  https://sbonaretti.github.io/pyKNEEr/\nGitHub repository: https://github.com/sbonaretti/pyKNEEr\nPreprint: https://doi.org/10.1101/556423\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Gary Beaupre,Serena Bonaretti","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"}],"is_toolkit":false,"is_model":false,"is_application":true,"is_data":false},{"group_id":"1708","unix_group_name":"thoracoscapular","modified":"1564068266","downloads":"760","group_name":"Thoracoscapular muscle contributions to shoulder movement and work.","logo_file":"thoracoscapular","short_description":"A shoulder model with an accurate scapulothoracic joint and includes scapular muscles to drive its motion.  The model can be used to compute the work done by the thoracoscapular muscles during shrugging and any arm elevation task.","long_description":"We developed a shoulder model with an accurate scapulothoracic joint and includes scapular muscles to drive its motion. We used the model to compute the work done by the thoracoscapular muscles during shrugging and arm elevation. We found that the bulk of the work done in upper-extremity tasks is performed by the largest muscles of the shoulder: trapezius, deltoids, pectoralis major, and serratus-anterior. Trapezius and serratus anterior prove to be important synergists in performing upward-rotation of the scapula. We show that the large thoracoscapular muscles do more work than glenohumeral muscles during arm-elevation tasks.  The model, experimental data and simulation results are available herein.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ajay Seth,Meilin Dong,Scott Delp,Ricardo Matias","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1710","unix_group_name":"antibodysim","modified":"1557943619","downloads":"0","group_name":"Antibody Dynamics Dalby group","logo_file":"","short_description":"Fully atomistic and solvated molecular dynamics simulations of antibody fragments under a wide range of simulated solution conditions. The data is useful in understanding the impact of solution conditions on protein stability, unfolding and dynamics.  Thi","long_description":"Fully atomistic and solvated molecular dynamics simulations of antibody fragments under a wide range of simulated solution conditions. The data is useful in understanding the impact of solution conditions on protein stability, unfolding and dynamics.  This will continue to grow with the addition of formulation excipients into simulations.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Paul Dalby","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1711","unix_group_name":"algodiff","modified":"1571386948","downloads":"0","group_name":"Algorithmic differentiation for trajectory optimization problems with OpenSim ","logo_file":"","short_description":"Code to solve trajectory optimization problems with OpenSim, direct collocation, and algorithmic differentiation .","long_description":"This repository contains code and data to generate OpenSim-based trajectory optimization of human movement while exploiting algorithmic differentiation (AD) as described in: Falisse A, Serrancoli G, Dembia C, Gillis J, De Groote F, (2019). Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement. PLoS ONE 14(10): e0217730, https://doi.org/10.1371/journal.pone.0217730.\n\nWe enabled the use of AD in OpenSim through a custom source code transformation tool named Recorder and through the operator overloading tool ADOL-C. For this purpose, we modified the source code of OpenSim and Simbody (https://github.com/antoinefalisse/opensim-core/tree/AD-recorder).\n\nWe then developed an interface between OpenSim and CasADi to solve trajectory optimization problems. CasADi is a tool for nonlinear optimization and algorithmic differentiation. \n\nWe compared the computational efficiency of using standard finite differences (FD) versus AD, implemented through Recorder (AD-Recorder) and ADOL-C (AD-ADOLC), when solving a series of trajectory optimization problems. These problems consisted of simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We found that using AD through Recorder was between 1.8 ± 0.1 and 17.8 ± 4.9 times faster than using FD, and between 3.6 ± 0.3 and 12.3 ± 1.3 times faster than using AD through ADOL-C. The larger the problem the larger the computational benefit of using AD instead of FD.\n\nIn this repository, we provide code necessary to perform the simulations with AD-Recorder and FD.  We also provide code for analyzing the results and reproducing the figures presented in the associated paper.\n\nThis repository is actively maintained on GitHub: https://github.com/antoinefalisse/algodiff\n\nThanks for citing our work in any derived publication. Feel free to reach us for any questions: antoine.falisse@kuleuven.be | antoinefalisse@gmail.com | friedl.degroote@kuleuven.be | gil.serrancoli@upc.edu.\n\nThis code has been developed on Windows using MATLAB2017b. There is no guarantee that it runs smooth on other platforms. Please let us know if you run into troubles.\n\nNOTE:\nThe repository has been split into three parts to meet the file size requirements. Make sure you reconstruct the full repository as described in the README.  ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Christopher Dembia,Friedl De Groote,Gil Serrancolí","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1712","unix_group_name":"gyroid","modified":"1558116289","downloads":"0","group_name":"Triply Periodic Minimal Surface (TPMS) based Bio-Scaffolds","logo_file":"","short_description":"With advancement in CAD & AM technologies, TPMS become an important tool for design and manufacture of porous scaffolds for tissue engineering application. Our project is primarily focused on trabecular bone scaffold.","long_description":"With advancement in CAD &amp; AM technologies, TPMS become an important tool for design and manufacture of porous scaffolds for tissue engineering application. Our project is primarily focused on trabecular bone scaffold.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yogesh Tripathi","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1715","unix_group_name":"opensense","modified":"1625163160","downloads":"3581","group_name":"OpenSense","logo_file":"opensense","short_description":"OpenSense enables users to compute the motions of body segments based on inertial measurement unit (IMU) data.","long_description":"OpenSense is a new, free and open source software tool for analyzing movement with inertial measurement unit (IMU) data. OpenSense provides tools for (i) reading and converting IMU sensors data into a single orientation format, (ii) associating and registering IMU sensors with body segments of an OpenSim model (as an IMU Frame), and (iii) performing inverse kinematics studies to compute joint angles. The OpenSense capabilities are available through the command line and through Matlab and Python scripting. \n\nOpenSense is now distributed as part of OpenSim. To get the latest version of OpenSense, download and install &lt;a href=&quot;https://simtk.org/frs/?group_id=91&quot;&gt;OpenSim 4.1&lt;/a&gt; version or later.\n\nTo learn more about OpenSense please review our &lt;a href=&quot;https://simtk-confluence.stanford.edu:8443/display/OpenSim/OpenSense+-+Kinematics+with+IMU+Data&quot;&gt;documentation and hands on example&lt;/a&gt;.\n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jennifer Hicks,Ajay Seth,Ayman Habib,jimmy d,Scott Delp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1717","unix_group_name":"3drism-gist","modified":"1560878238","downloads":"0","group_name":"3D RISM - GIST","logo_file":"","short_description":"Simulation setup and data for \"A molecular reconstruction approach to site-based 3D-RISM and comparison to GIST hydration thermodynamic maps in an enzyme active site\"","long_description":"This SimTk project shares the data for the following submitted research paper:\n\nTitle: A molecular reconstruction approach to site-based 3D-RISM and comparison to GIST hydration thermodynamic maps in an enzyme active site\n\nAuthors: Crystal Nguyen, Takeshi Yamazaki, Andriy Kovalenko, David A. Case, Michael K. Gilson, Tom Kurtzman, Tyler Luchko \n\nABSTRACT: \nComputed, high-resolution, spatial distributions of solvation energy and entropy can provide detailed information about the role of water in molecular recognition. While grid inhomogeneous solvation theory (GIST) provides rigorous, detailed thermodynamic information from explicit solvent molecular dynamics simulations, recent developments in the 3D reference interaction site model (3D-RISM) theory allow many of the same quantities to be calculated in a fraction of the time. However, 3D-RISM produces atomic-site, rather than molecular, density distributions, which are difficult to extract physical meaning from. To overcome this difficulty, we introduce a method to reconstruct molecular density distributions from atomic-site density distributions. Furthermore, we assess the quality of the resulting solvation thermodynamics density distributions by analyzing the binding site of coagulation Factor Xa with both GIST and 3D-RISM. We find good qualitative agreement between the methods for oxygen and hydrogen densities as well as direct solute-solvent energetic interactions.  However, 3D-RISM predicts lower energetic and entropic penalties for moving water from the bulk to the binding site.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thomas Kurtzman,Tyler Luchko","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1720","unix_group_name":"cnn-dud-e","modified":"1559759813","downloads":"0","group_name":"Hidden bias in the DUD-E dataset leads to misleading performance of CNN models","logo_file":"","short_description":"Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening \n\nAuthors: Lieyang Chen, Anthony Cruz, Steven Ramsey, Callum J. Dickson, Jose S. Duca, Viktor Hornak, David R. Koes, and Tom Kurtzman*","long_description":"Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening \n\nAuthors: Lieyang Chen, Anthony Cruz, Steven Ramsey, Callum J. Dickson, Jose S. Duca, Viktor Hornak, David R. Koes, and Tom Kurtzman*\n\nThis Project shares data from the submitted publication (link at end)  \n\nAbstract:  Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliable protein-ligand x-ray structures and binding affinity data has required the use of constructed datasets for the training and evaluation of CNN molecular recognition models. Here, we outline various sources of bias in one such widely-used dataset, the Directory of Useful Decoys: Enhanced (DUD-E). We have constructed and performed tests to investigate whether CNN models developed using DUD-E are properly learning the underlying physics of molecular recognition, as intended, or are instead learning biases inherent in the dataset itself. We find that superior enrichment efficiency in CNN models can be attributed to the analogue and decoy bias hidden in the DUD-E dataset rather than successful generalization of the pattern of protein-ligand interactions. Comparing additional deep learning models trained on PDBbind datasets, we found that their enrichment performances using DUD-E are not superior to the performance of the docking program AutoDock Vina. Together, these results suggest that biases that could be present in constructed datasets should be thoroughly evaluated before applying them to machine learning based methodology development.\nhttps://chemrxiv.org/articles/Hidden_Bias_in_the_DUD-E_Dataset_Leads_to_Misleading_Performance_of_Deep_Learning_in_Structure-Based_Virtual_Screening/7886165","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thomas Kurtzman,Eric Chen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1721","unix_group_name":"canoe-shoulder","modified":"1559757082","downloads":"0","group_name":"Canoe Shoulder Project","logo_file":"","short_description":"Developing understanding of shoulder muscle force production through range for rehab and performance.","long_description":"Developing understanding of shoulder muscle force production through range for rehab and performance.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Andrew Powell","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1726","unix_group_name":"fes-gait","modified":"1580131549","downloads":"89","group_name":"Control strategies for functional electrical stimulation (FES) gait","logo_file":"","short_description":"Lower-limb rehabilitation for spinal cord injury (SCI) and other motor disorders is often a lengthy process for the patient. The combination of active orthoses and functional electrical stimulation (FES) promises to accelerate therapy outcome, while simul","long_description":"Lower-limb rehabilitation for spinal cord injury (SCI) and other motor disorders is often a lengthy process for the patient. The combination of active orthoses and functional electrical stimulation (FES) promises to accelerate therapy outcome, while simultaneously reducing the physical burden of the therapist. In this work, we propose a controller to a hybrid neuroprosthesis (HNP) composed of a hip orthosis and FES-controlled knee motion. In our simulation analysis using a detailed musculoskeletal model, we use experimental data from an able-bodied subject during slow-speed walking to compare the performance provided by such a system. Furthermore, we analyzed the obtained results in comparison to gait data collected from experiments where we used an active hip orthosis. Although the knee stimulation controller still oscillated during gait, we acquired control results with errors smaller than five degrees. Besides, we were able to examine the performance at very slow speeds.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Antonio Bo,Ana  de Sousa","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1728","unix_group_name":"horse","modified":"1560949820","downloads":"0","group_name":"Musculoskeletal model of a horse forelimb","logo_file":"horse","short_description":"Data for the modeling and simulation of the horse forelimb at trotting and jumping a fence of 1m high. ","long_description":"The data published here a linked to a paper submitted to the Journal of Bionic Engineering. The geometry of the model and the simulation data are provided. The methods used to build the complete model and to correct the simulation data would be given in this paper. ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Joanne Becker","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1735","unix_group_name":"3d-muscles","modified":"1633392000","downloads":"72","group_name":"Three-dimensional muscle geometries for subject-specific models","logo_file":"3d-muscles","short_description":"This project aims to contribute to musculoskeletal modelling by presenting a fully automated method for generating anatomical muscle models from segmented geometries typically obtained from MRI scans. For each muscle geometry, the method creates an arbitrary number of lines of action of user-defined complexity, i.e. number of segments in the muscle path. \nThe package available for download consists of musculoskeletal models of the hip joint, including \"traditional\" and highly-discretised muscle representations, and all the MATLAB scripts and data required to generate the main results and figures presented in the associated publication.","long_description":"Please note that all the materials and scripts for this publication were developed using the API of OpenSim 3.3. More information about requirements, limitation and intended use of the resources and data shared with the publication are available in the &quot;README.pdf&quot; file within the package and at our GitHub project page: https://github.com/ComputationalBiomechanics/3d-muscles.\n\nThe highly-discretised muscle models included in the package were generated automatically using the method described in the paper through a software called LHPBuilder, a multimodal viewer for biomechanical applications that is not developed or supported anymore. The same method is currently being implemented as an OpenSim (v4.1) plugin, a demo of which is visible here:\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/BW_jjCcbf5o&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nRelease of the plugin will be associated with a future publication.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Luca Modenese,Josef Kohout","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1744","unix_group_name":"imu2hca","modified":"1563231100","downloads":"0","group_name":"Using IMU Data to Build a Biomechanics Model and Calculate Hip Contact Angles","logo_file":"","short_description":"This project will involve collecting data using inertial measurement units (IMUs) while subjects complete a set of prescribed activities (i.e. standing, lying down, walking, ascending/descending stairs). That data will be combined with a dynamic model wit","long_description":"This project will involve collecting data using inertial measurement units (IMUs) while subjects complete a set of prescribed activities (i.e. standing, lying down, walking, ascending/descending stairs). That data will be combined with a dynamic model within OpenSim to calculate hip contact angles.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Megan McCabe","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1748","unix_group_name":"ad3d","modified":"1564003024","downloads":"0","group_name":"Aortic Dissection 3D Model","logo_file":"","short_description":"Create a 3D model of an aortic dissection","long_description":"Create a 3D model of an aortic dissection","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Christian Beke Onana","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1749","unix_group_name":"rnamake","modified":"1564433464","downloads":"55","group_name":"RNAMake","logo_file":"","short_description":"RNAMake is a toolkit for designing and optimizing RNA 3D structure.","long_description":"RNAMake is a toolkit for designing and optimizing RNA 3D structure.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Joseph Yesselman","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1750","unix_group_name":"nlid-py","modified":"1564555891","downloads":"0","group_name":"Nonlinear Identification Toolbox","logo_file":"","short_description":"Python tools for nonlinear identification This project will port the MATLAB NLID toolbox developed by Rob Kearney and colleagues into Python as an open source package. This toolbox includes routines for time-invariant and time-varying identification of:\n","long_description":"Python tools for nonlinear identification This project will port the MATLAB NLID toolbox developed by Rob Kearney and colleagues into Python as an open source package. This toolbox includes routines for time-invariant and time-varying identification of:\n\n-Linear dynamic models comprising impulse response function models, frequency response function, state-space models;\n-Block oriented nonlinear structures comprising Hammerstein systems, Wiener systems, sandwich and parallel-cascade models;\n-Arbitrary nonlinear dynamic models comprising Volterra and Wiener basis expansions.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kian Jalaleddini","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1751","unix_group_name":"nima-","modified":"1564555880","downloads":"0","group_name":"Neuromechanics Identification Movement Analysis toolbox","logo_file":"","short_description":"Joint neuromechanics arises from the complex interaction of dynamic, nonlinear elements including muscles, tendons, and neural circuits. The system has limited non-invasive observability and exhibits time-varying behaviour during functional tasks, which f","long_description":"Joint neuromechanics arises from the complex interaction of dynamic, nonlinear elements including muscles, tendons, and neural circuits. The system has limited non-invasive observability and exhibits time-varying behaviour during functional tasks, which further necessitates the use of advanced data analysis and modeling techniques - reductionist and holistic (or system-level), physics-based and data-driven, time-invariant and time-varying, etc. Therefore, the human movement, and neuromechanics communities have developed and validated multiple softwares for analysis and modeling of their data.  However, these tools are fragmented, lack standardization, and are often not intended to be used by the larger community. These limitations slow  scientific progress as researchers often find themselves spending a significant amount of time writing the code to replicate the analysis of a scientific paper which is always prone to errors, or to use fragmented pieces of code written in different languages.\nIn this project we will address these problems by developing the Neuromechanics Identification Movement Analysis toolbox software. We will achieve this through a combination of a) developing new code, b) integrating Python packages that contain the required numerical and data analysis methods, c) porting code from other languages, d) making code customizations to address specific needs of neuromechanics data analysis, e) developing easy to use graphical user interface (GUI), and  f) developing a neuromechanics data repository that will serve as a stepping stone to standardize maintenance and sharing of human movement and neuromechanics data for testing, validation and exploration purposes.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kian Jalaleddini","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1752","unix_group_name":"gcnnpoclig","modified":"1598415619","downloads":"1440","group_name":"Graph Convolutional Neural Networks for Predicting Drug-Target Interactions","logo_file":"","short_description":"A graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions.","long_description":"Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-autoencoder to learn fixed-size representations of protein pockets from a set of representative druggable protein binding sites. Second, we trained two Graph-CNNs to automatically extract features from pocket graphs and 2D ligand graphs, respectively, driven by binding classification labels. We demonstrate that graph-autoencoders can learn fixed-size representations for protein pockets of varying sizes and the Graph-CNN framework can effectively capture protein-ligand binding interactions without relying on target-ligand co-complexes. Across several metrics, Graph-CNNs achieved better or comparable performance to 3DCNN ligand-scoring, AutoDock Vina, RF-Score, and NNScore on common virtual screening benchmark datasets. Visualization of key pocket residues and ligand atoms contributing to the classification decisions confirms that our networks recognize meaningful interactions between pockets and ligands.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Wen Torng","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1753","unix_group_name":"neuman","modified":"1564595067","downloads":"0","group_name":"Toolbox for NEUromechanics and MOvement ANalysis (NEUMAN)","logo_file":"","short_description":"Joint neuromechanics arise from the complex interaction of dynamic, nonlinear elements including muscles, tendons,proprioceptors,  and neural circuits. The system has limited non-invasive observability and exhibits time-varying behaviour during functional","long_description":"Joint neuromechanics arise from the complex interaction of dynamic, nonlinear elements including muscles, tendons,proprioceptors,  and neural circuits. The system has limited non-invasive observability and exhibits time-varying behaviour during functional tasks. This  necessitates the use of advanced data analysis and modeling techniques - reductionist and holistic (or system-level), physics-based and data-driven, etc. Therefore, the movement, and neuromechanics communities have developed and validated a variety of software tools  to analyze and model their data.  However, these tools are fragmented, lack standardization, and are rarely  intended for use by the larger community. These limitations slow  scientific progress as researchers often must  spend time writing the code needed  to replicate the analysis of a scientific paper which is always prone to errors, or to piece together fragmented pieces of code written in different languages.\n\nIn this project, we will address these problems by developing the NEUromechanics and MOvement ANalysis (NEUMAN-Py) package. We will achieve this through a combination of  developing new code, integrating Python packages that contain the required methods, porting code from other languages, customizing code to address specific needs of neuromechanics analysis, developing easy to use graphical user interface (GUI), and developing a neuromechanics data repository that will serve as a stepping stone to standardize maintenance and sharing of human movement and neuromechanics data for testing, validation and exploration purposes.\n\nNEUMAN-Py will comprise the following  key modules:\n\n-Time-Invariant (TI) Dynamic Joint Stiffness : Parallel-Cascade, SubSpace, Structural Decomposition SubSpace (SDSS), Short-Segment SDSS methods.\n-TI Intrinsic Stiffness : non-parametric Impulse Response Function (IRF), and parametric mass-spring-damper models.\n-Reflex Stiffness Parameterization: parameterization of the static nonlinearity and linear dynamics element of the reflex stiffness.\n-Endpoint Stiffness: 2D/3D endpoint elasticity ellipsoids estimation and related tools.\nTI Intrinsic Compliance : non-parametric (IRF), and parametric (transfer function, state-space, and mass-spring-damper) models.\n-Time-Varying Joint Stiffness Identification: ensemble-based and temporal expansion methods,\nParameter Varying Stiffness: Linear Parameter Varying (LPV) of IRF models, LPV Subspace, LPV Laguerre, NPN Hammerstein, NPV Parallel-Cascade models.\n-EMG Analysis: EMG filtering, activation calculation, time- and frequency domain EMG analysis, reflex EMG modeling, voluntary EMG modeling, EMG-EMG and EMG-force synchronization\n-Muscle Synergies: non-negative matrix factorization (NMF).-\nMotion Capture Data Analysis: Parsing various data files from different marker-based motion capture systems; inverse kinematics analysis; inverse dynamics analysis.\n-IMU and Gyro Data Analysis: Kalman filtering to estimate joint kinematics (position, velocity, and attitude/orientation/posture) from accelerometer data recorded by IMUs and magnetic field recorded by gyros. Overall, this is the module that requires most research as many of the algorithms are still only available in scientific papers.\n-Actigraphy: Analysis of actigraphy data including  accelerometer data, body temperature, skin resistance or conductance, and EEG; as well as environmental data such as ambient light and sound levels.\n-Data Visualization: Visualization of human movement and joint neuromechanics data as well as the results of modeling and analysis. This includes developing context dependent and physiologically relevant visualization capabilities for NEUMAN.   \n-Graphical User Interface (GUI): A GUI to facilitate and promote the use of NEUMAN among research communities who use data analysis but are not used to (or mandated to use) coding/scripting and prefer to analyze their experimental data via a graphical interface.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kian Jalaleddini","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1754","unix_group_name":"taw01","modified":"1564625307","downloads":"0","group_name":"tawa01","logo_file":"","short_description":"numerical model for postural control","long_description":"numerical model for postural control","has_downloads":false,"keywords":"","ontologies":"","projMembers":"yuta tawaki","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1760","unix_group_name":"preopemg","modified":"1565251305","downloads":"0","group_name":"Pre-treatment EMG can model post-treatment muscle coordination","logo_file":"","short_description":"We collected EMG data from two groups of patients with CP that underwent either botulinum-toxin injections or single-event multilevel surgeries, as well as data coming from a typically developing population. \nWe computed muscle synergies from the pre-tre","long_description":"We collected EMG data from two groups of patients with CP that underwent either botulinum-toxin injections or single-event multilevel surgeries, as well as data coming from a typically developing population. \nWe computed muscle synergies from the pre-treatment EMG and quantified how good is the reconstruction of the patients' post-treatment and normal EMG signals based upon the patients' pre-treatment muscle synergies. \nWe also investigated if the characteristics of the muscle synergies could correlate with the changes in motor performance observed in the patients. The motor performance was computed as the Gait Profile Score based on the inverse kinematics results obtained with the OpenSim tools.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"lorenzo pitto","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1761","unix_group_name":"microgravsim","modified":"1565200485","downloads":"0","group_name":"Assistive device simulation for resistance training in microgravity environment","logo_file":"","short_description":"Simulation of control inputs of assistive device to track the state variables obtained from 1g normal walking in microgravity environment.","long_description":"Simulation of control inputs of assistive device to track the state variables obtained from 1g normal walking in microgravity environment.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jong In Han","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1763","unix_group_name":"shoulder-sims","modified":"1565619929","downloads":"0","group_name":"Predictive Simulations for Examining Shoulder Function with Changes in Strength","logo_file":"","short_description":"This project leverages OpenSim and OpenSim MoCo to generate predictive simulations of shoulder joint movements with altered strength parameters of specified muscle groups.","long_description":"This project leverages OpenSim and OpenSim MoCo to generate predictive simulations of shoulder joint movements with altered strength parameters of specified muscle groups.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Aaron Fox","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1766","unix_group_name":"rbkneemodeling","modified":"1692403200","downloads":"29","group_name":"Rule Based Knee Modeling","logo_file":"","short_description":"This project aims to reconstruct the passive flexion motion of the knee based on anatomical data alone, which includes bony anatomy, cartilage geometry and ligament insertion sites geometry.","long_description":"This project aims to reconstruct the passive flexion motion of the knee based on anatomical data alone, which includes bony anatomy, cartilage geometry and ligament insertion sites geometry. Current knowledge of ligament behavior and articular contact mechanics has been mainly based on anatomical and mechanical data from cadaveric studies along with computational models of the knee joint. In current knee modeling techniques, mechanical properties of soft tissues, are either adopted from literature or estimated through the involved inverse modeling approach that relies on experimental measurements of the joint kinematics. Thus, modeling results are tied to the quality of the collected data, which is always susceptible to errors. The ability to predict joint kinematics without the need to collect kinematics-kinetics data is a groundbreaking mean which can be easily translated into a clinical setting or other applications.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ahmet Erdemir,Jason Halloran,Snehal Chokhandre,Neda Abdollahi,Sean  Doherty,Ben Landis,Ellen Klonowski","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1767","unix_group_name":"aeva-apps","modified":"1651674449","downloads":"8365","group_name":"Practical Annotation and Exchange of Virtual Anatomy","logo_file":"aeva-apps","short_description":"This site serves for dissemination of aeva and its use cases. aeva is a free and open source software for annotation and exchange of virtual anatomy. The use cases drive design and testing of aeva features and demonstrate various workflows that rely on virtual anatomy.","long_description":"Representation of anatomy in a virtual form is at the heart of clinical decision making, biomedical research, and medical training. Virtual anatomy is not limited to description of geometry but also requires appropriate and efficient labeling of regions - to define spatial relationships and interactions between anatomical objects; effective strategies for pointwise operations - to define local properties, biological or otherwise; and support for diverse data formats and standards - to facilitate exchange between clinicians, scientists, engineers, and the general public. Development of aeva, a free and open source software package (library, user interfaces, extensions) capable of automated and interactive operations for virtual anatomy annotation and exchange, is in response to these currently unmet requirements. This site serves for aeva outreach, including dissemination the software and use cases. The use cases drive design and testing of aeva features and demonstrate various workflows that rely on virtual anatomy. \n\naeva downloads: \nDownloads (https://simtk.org/frs/?group_id=1767)\nKitware data repository (https://data.kitware.com/#folder/5e7a4690af2e2eed356a17f2)\n\naeva documentation: \nGuides and tutorials (https://aeva.readthedocs.io)\n\naeva videos:\nShort instructions (https://www.youtube.com/channel/UCubfUe40LXvBs86UyKci0Fw)\n\naeva source code: \nKitware source code repository (https://gitlab.kitware.com/aeva)\n\naeva forum: \nForums (https://simtk.org/plugins/phpBB/indexPhpbb.php?group_id=1767 )","has_downloads":true,"keywords":"geometry,image-based geometric modeling,Mesh generation,anatomy,mesh,finite element,Image segmentation","ontologies":"Image_Processing,Mesh_Model,Finite_Element_Model,Meshing","projMembers":"Ahmet Erdemir,Aaron Bray,Snehal Chokhandre,Jianfeng Yan,Erica Neumann (Morrill),venkata arikatla,Ariel Schwartz,Sean  Doherty,Andinet Enquobahrie,David 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Mah","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1769","unix_group_name":"lrle","modified":"1567553258","downloads":"18","group_name":"Mimicking muscle without using muscles / Multi-CPU optimal control with Matlab","logo_file":"","short_description":"Code for \"Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation\", SIGGRAPH 2019 (https://arxiv.org/abs/1904.13041). We transform optimal control problems formulated with muscle actuators to equivalent problems using simple and effi","long_description":"Code for &quot;Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation&quot;, SIGGRAPH 2019 (https://arxiv.org/abs/1904.13041). We transform optimal control problems formulated with muscle actuators to equivalent problems using simple and efficient joint torque actuators, while retaining motion quality comparable to using muscles. We do so by learning two neural-net functions, state-dependent torque-limit function and metabolic-energy function from OpenSim muscle model by solving static redundancy problems. This project shows how to learn the two functions, and how to incorporate the learned neural networks into multi-CPU optimal control problems using Matlab, IPOPT and OpenSim.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Yifeng Jiang","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1770","unix_group_name":"bioproj1","modified":"1567459069","downloads":"0","group_name":"Biomechanics project 1","logo_file":"","short_description":"The relationship between the required muscle force (erector spinae) and the angle at the\nknees","long_description":"The relationship between the required muscle force (erector spinae) and the angle at the\nknees","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emily Clark","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1771","unix_group_name":"biomechproject1","modified":"1567459131","downloads":"0","group_name":"Lifting Mechanics (Engineering Biomechanics Class Project)","logo_file":"","short_description":"Study loading in the erector spinae during lifting as a function of the angle at the knees","long_description":"Study loading in the erector spinae during lifting as a function of the angle at the knees","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Logan Bilcik","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1772","unix_group_name":"biomech1","modified":"1567459210","downloads":"0","group_name":"Lifting Linkage","logo_file":"","short_description":"Create a lifting model with a four bar linkage to find the relationship between the force on the spine and the anterior angle of the knee","long_description":"Create a lifting model with a four bar linkage to find the relationship between the force on the spine and the anterior angle of the knee","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Alex Ponts","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1773","unix_group_name":"llsm","modified":"1614533793","downloads":"346","group_name":"London Lumbar Spine Model","logo_file":"llsm","short_description":"The aim of this project is to provide a reliable model of the lumbar spine and lower limbs to estimate muscle and joint reaction forces for daily living activities including locomotion and lifting tasks.","long_description":"The aim of this project is to provide a reliable model of the lumbar spine and lower limbs to estimate muscle and joint reaction forces for daily living activities including locomotion and lifting tasks. The model is based on bone and muscle geometries segmented from MRI scans of a single volunteer to allow full compatibility with Finite Element models.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Andrew Phillips,Clement Favier","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1774","unix_group_name":"ergonomicsusage","modified":"1567618681","downloads":"0","group_name":"The Risk Assessment of Work-related Musculoskeletal Disorders based on OpenSim","logo_file":"","short_description":"Work-related musculoskeletal disorders cause physical and mental illnesses in workers, reduce their productivity and cause great losses to industries and society. This thesis focuses on the assessment of the physical risk of work-related musculoskeletal d","long_description":"Work-related musculoskeletal disorders cause physical and mental illnesses in workers, reduce their productivity and cause great losses to industries and society. This thesis focuses on the assessment of the physical risk of work-related musculoskeletal disorders in industry, for which four key points are identified: measuring workloads, assessing the effect of workload accumulation, quantifying individual characteristics and integrating the risk assessment into digital human modeling tools. In the state of the art, the epidemiologic studies of musculoskeletal disoders and the current methods used for its physical risk assessment are reviewed, as well as the studies concerning the four key points. The second part presents an experimental study involving 17 subjects to explore a new indicator to muscle fatigue with surface EMG.  In the next part, efforts are made\tto integrate a muscle fatigue model into OpenSim, a digital human modeling software, with which the capacity decrease of each muscle is predictable for a given task. The predicted values could be applicable to the physical risk assessment. The fourth part introduce the work to build up a Full-chain musculoskeletal model in OpenSim in view that no current model covers muscles of the torso and all the limbs. Special attention is paid to the method used by OpenSim to adapt the model inertial properties to individuals. Errors of the method is evaluated with reference data coming from the whole-body 3D scan. In the last part, the newly built Full-chain model is applied on the posture analysis of an overhead drilling task. The muscle activition varies as a function of postures, which is suggested as the indicator of posture loads.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Damien Chablat","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1775","unix_group_name":"anklestability","modified":"1567639540","downloads":"0","group_name":"Muscle and ligament contributions to ankle stability","logo_file":"","short_description":"The project seeks to determine the individual contributions of muscles and ligament to ankle stability using a combination of experimental and modelling techniques.","long_description":"The project seeks to determine the individual contributions of muscles and ligament to ankle stability using a combination of experimental and modelling techniques.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Michael Asmussen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1776","unix_group_name":"predictcpgait","modified":"1582031439","downloads":"0","group_name":"Predictive simulation of CP gait","logo_file":"","short_description":"This project contains code and data to perform predictive simulations of gait for a child with cerebral palsy (CP) and explore the differential effects of motor control and musculoskeletal deficits on gait dysfunction.","long_description":"Physics-based simulations of walking have the theoretical potential to support clinical decision-making by predicting the functional outcome of treatments in terms of walking performance. Yet before using such simulations in clinical practice, their ability to identify the main treatment targets in specific patients needs to be demonstrated. \n\nIn this study, we generated predictive simulations of walking with a medical imaging based neuro-musculoskeletal model of a child with cerebral palsy presenting crouch gait. We explored the influence of altered muscle-tendon properties, reduced neuromuscular control complexity, and spasticity on gait dysfunction in terms of joint kinematics, kinetics, muscle activity, and metabolic cost of transport. We modeled altered muscle-tendon properties by personalizing Hill-type muscle-tendon parameters based on data collected during functional movements, simpler neuromuscular control by reducing the number of independent muscle synergies, and spasticity through delayed muscle activity feedback from muscle force and force rate. \n\nOur simulations revealed that, in the presence of aberrant musculoskeletal geometries, altered muscle-tendon properties rather than reduced neuromuscular control complexity and spasticity were the primary cause of the crouch gait pattern observed for this child, which is in agreement with the clinical\nexamination. These results suggest that muscle-tendon properties should be the primary target of interventions aiming to restore an upright gait pattern for this child. This suggestion is in line with the gait analysis following muscle-tendon property and bone deformity corrections. \n\nFind more information about this work in the associated publication: https://www.frontiersin.org/articles/10.3389/fnhum.2020.00040/full.\n\nThe released folder contains readme files with details of the folder structure. Please contact me for any questions (antoine.falisse@kuleuven.be) or post an issue on GitHub: https://github.com/antoinefalisse/predictcpgait. Please note that the GitHub repository is actively maintained, so bugs are likely to be solved on Github than on SimTK.\n\n\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Friedl De Groote,Ilse Jonkers","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1779","unix_group_name":"2019hansbos","modified":"1568914904","downloads":"0","group_name":"Boston","logo_file":"","short_description":"To see if a person fits in a certain size bathtub","long_description":"To see if a person fits in a certain size bathtub","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kris Jaeger-Helton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1783","unix_group_name":"bmed2250","modified":"1569352882","downloads":"0","group_name":"BMED 2250","logo_file":"","short_description":"Determining the muscle loads of a Parkinson's Disease Patient at each phase of their gait cycle.","long_description":"Determining the muscle loads of a Parkinson's Disease Patient at each phase of their gait cycle.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Lily Englander","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1786","unix_group_name":"runpws","modified":"1569609697","downloads":"0","group_name":"biomechanics of different running styles","logo_file":"","short_description":"looking at how different running techniques in sprinting affect force application","long_description":"looking at how different running techniques in sprinting affect force application","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Maël  Kramer","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1787","unix_group_name":"mdrana","modified":"1569868933","downloads":"0","group_name":"aaaaaaaaaa","logo_file":"","short_description":"run protein ligand MD","long_description":"run protein ligand MD","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ranabir Majumder","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1791","unix_group_name":"simexo","modified":"1570789893","downloads":"8268","group_name":"Closed loop simulation of a human-exoskeleton and kinetic and kinematic modeling","logo_file":"simexo","short_description":"This project attempts to tackle some of the challenges in developing an exoskeleton device using simulation methods. in this project, the closed loop simulation of a human exoskeleton is performed .In this study, a simulation-based method is presented for the designing and analysis of the parameters of an exoskeleton and its wearer’s kinetics and kinematics. Model-based design software, including OpenSim and Inventor, and mathematical software, such as MATLAB, are integrated. This method can assist in the modification of exoskeleton devices and allow physiologists,  and physical therapists to generate new solutions for rehabilitation programs using exoskeletons.","long_description":"As exoskeletons are used in human rehabilitation, there are many challenges in the development of exoskeletons in terms of the connection their structure to the human body. These challenges are affected by many factors such as interaction of the exoskeleton and the human body, and exoskeleton actuators way of assisting muscles. Moreover, every human has her/his own movement and force pattern; thus, how experimental data can be extracted from each human body is another challenging issue. This project attempts to tackle some of the challenges in developing an exoskeleton device using simulation methods and software. Simulation can help researchers to develop exoskeleton structures and can give them insight into how this device can help muscle movement. Therefore, a simulation based method is presented to design and analyze the parameters of an exoskeleton and its wearer’s kinetics and kinematics.\nThe proposed method consists of four stages. In the first stage, the human model is edited to create a human-exoskeleton model. In the next stage, OpenSim is integrated with MATLAB in order to simulate the effect of exoskeletons on the human body. Considering the sole effect of an exoskeleton on each individual subject with his/her specific characteristics, stages three and four are conducted. In the third stage, the experimental data of a subject is produced by motion capture systems. The computed muscle control (CMC) tool of the OpenSim has been applied to obtain the muscle control values . In stage four, the closed loop simulation of a human-exoskeleton is performed using the results of the third stage. In addition, in this stage, the controller is implemented and simulated to obtain control parameters for the exoskeleton actuators.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Maryam khamar,Mehdi Edrisi,Mohsen Zahiri","trove_cats":[{"id":"1001","fullname":"OpenSim"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1792","unix_group_name":"ue-synergies","modified":"1588395350","downloads":"67","group_name":"Upper Extremity Muscle Synergies","logo_file":"","short_description":"It has been hypothesized that the central nervous system simplifies the  production  of  movement  by  limiting  motor  commands  to  a  smalls et of modules known as muscle synergies.  Recently, investigators have questioned  whether  a  low-dimensional ","long_description":"It has been hypothesized that the central nervous system simplifies the  production  of  movement  by  limiting  motor  commands  to  a  smalls et of modules known as muscle synergies.  Recently, investigators have questioned  whether  a  low-dimensional  controller  can  produce  the  rich and  flexible  behaviors  seen  in  everyday  movements.   We  implemented muscle synergies in a biomechanically realistic human upper extremity model and performed computational experiments to determine whether synergies introduced task performance deficits, generalized to different movements, and facilitated the learning of new movements as occurs during motor skill acquisition.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Mazen Al Borno","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1796","unix_group_name":"bmech-trainer","modified":"1586965131","downloads":"0","group_name":"Biomechanics Training Material","logo_file":"","short_description":"Provide introduction to experimentation and computational tools used in biomechanics research.","long_description":"This project contains material provided by Erdemir Laboratory at the Cleveland Clinic for introducing concepts and tools in biomechanics related experimentation and computational methods.  The material aims to include introduction to mechanical characterization at joint and tissue level, finite element analysis, constitutive modeling, and musculoskeletal models.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Snehal Chokhandre,Ahmet Erdemir,Neda Abdollahi,Callan Gillespie,Ariel Schwartz,Ellen Klonowski","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1797","unix_group_name":"energyest","modified":"1572025432","downloads":"990","group_name":"Rapid energy expenditure estimation for assisted and inclined loaded walking","logo_file":"","short_description":"This project aims to estimate energy expenditure during ankle assisted and inclined loaded walking from wearable sensor signals. In this work we trained models using input features of muscle activity (EMG) and ground reaction forces to estimate energy exp","long_description":"This project aims to estimate energy expenditure during ankle assisted and inclined loaded walking from wearable sensor signals. In this work we trained models using input features of muscle activity (EMG) and ground reaction forces to estimate energy expenditure during walking (1) with an ankle exoskeleton and (2) during various loaded and inclined walking conditions. These estimates were made every single gait cycle. We measure the performance of these models for cases where conditions or subjects data are not included in the training data to simulate real use cases. For research or clinical experiments where the accuracy of our models is sufficient these can be used to replace the standard indirect calorimetry methods of estimating energy expenditure which require expensive equipment and provide slow, noisy measurements. This repository contains the relevant formatted datasets, code to train models, and final model parameters to recreate our reported results. This code base should offer a support to apply our techniques to new datasets or utilize our fully trained models.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Joy Ku,Patrick Slade","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1799","unix_group_name":"autoscoper","modified":"1628211404","downloads":"306","group_name":"Autoscoper (Bone/Implant Tracking Software)","logo_file":"autoscoper","short_description":"&lt;div align=&quot;justify&quot;&gt;Autoscoper is a 2D to 3D image registration software developed at Brown University in 2013 as a tool to investigate intra-articular joint motion during dynamic tasks. 3D position and orientation of bones and implants can be resolved in Autoscoper using volumetric density (CT) data and multi-view 2D radiographs acquired at high-speed (videoradiographs; VRG). So far, Autoscoper has been used for tracking the shoulder, spine, wrist, hip, knee, and ankle joints. &lt;/div&gt;","long_description":"&lt;div align=&quot;justify&quot;&gt;This is Autoscoper v2.7 upgraded and maintained by &lt;a href=&quot;https://sites.google.com/view/bardiya-akhbari/&quot;&gt;Bardiya Akhbari&lt;/a&gt;. Autoscoper is a 2D-to-3D registration software that gives the users the ability to track bones or implants in the videoradiographs. This version supports the particle swarm optimization algorithm and active feedback on normalized cross-correlation to improve the accuracy and speed of registration. Earlier version (v 2.0) was programmed by Dr. &lt;a href=&quot;http://bknoerlein.de/index.html&quot;&gt;Ben Knoerlein&lt;/a&gt;. Version 2 combined the sources of both the CUDA and OpenCL versions and allows usage of either one. Version 2 has improved processing, several bug fixes, and new functionality, e.g. multi bone, batch processing, when compared to the original versions. The first version of this software was developed by Andy Loomis (original CUDA version) and Mark Howison (OpenCL reimplementation).&lt;/div&gt;\nPlease cite this article when using the latest version of Autoscoper: \n&lt;div align=&quot;justify&quot;&gt;&lt;a href=&quot;https://www.sciencedirect.com/science/article/abs/pii/S0021929019303847&quot;&gt; Akhbari, B., Morton, A. M., Moore, D. C., Weiss, A-P. C., Wolfe, W. S., Crisco, J. J., 2019. Accuracy of Biplane Videoradiography for Quantifying Dynamic Wrist Kinematics, Journal of Biomechanics.&lt;/a&gt;&lt;/div&gt;\nYou can find the full protocol in the Journal of Visualized Experiment:\n&lt;div align=&quot;justify&quot;&gt;&lt;a href=&quot;https://www.jove.com/t/62102/biplanar-videoradiography-to-study-wrist-distal-radioulnar&quot;&gt; Akhbari, B., Morton, A. M., Moore, D. C., Crisco, J. J. Biplanar Videoradiography to Study the Wrist and Distal Radioulnar Joints. &lt;em&gt;J. Vis. Exp.&lt;/em&gt; (168), e62102, doi:10.3791/62102 (2021).\n&lt;/a&gt;&lt;/div&gt;\nTo watch the tutorials, please check out our &lt;a href=&quot;https://www.youtube.com/playlist?list=PLQkw3tZ6MA9QaVnSUyh9K-OeY9dsjb2P1&quot;&gt;YouTube playlist&lt;/a&gt;.\n\nPlease help us to improve this software package by responding to this &lt;a href=&quot;https://brown.co1.qualtrics.com/jfe/form/SV_4Nq1M03vthQzigm&quot;&gt;quick survey&lt;/a&gt;.","has_downloads":true,"keywords":"image registration,autoscoper,tracking,xromm,biplanar videoradiography,biomechanics","ontologies":"","projMembers":"Bardiya Akhbari","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"}],"is_toolkit":true,"is_model":false,"is_application":true,"is_data":false},{"group_id":"1801","unix_group_name":"jeyemodel","modified":"1647569875","downloads":"17","group_name":"jEye: Ocular Biomechanics model","logo_file":"jeyemodel","short_description":"Simulating and analysing eye movement is useful for assessing visual system contribution to discomfort with respect to body movements, especially in virtual environments where simulation sickness might occur. ","long_description":"Simulating and analysing eye movement is useful for assessing visual system contribution to discomfort with respect to body movements, especially in virtual environments where simulation sickness might occur. It can also be used in the design of eye prosthesis or humanoid robot eye. \nThe model is based on physiological and kinematic properties of the human eye. It incorporates an eye-globe, orbital suspension tissues and six muscles with their connective tissues (pulleys). Pulleys were incorporated in rectus and inferior oblique muscles. \n\nFor more details about the model check our publication\nAn ocular biomechanic model for dynamic simulation of different eye movements. \nhttps://www.sciencedirect.com/science/article/abs/pii/S0021929018300927","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Julie Iskander","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1802","unix_group_name":"sbmopenmm","modified":"1633085646","downloads":"0","group_name":"SBMOpenMM: Structure-based model library for protein simulations in OpenMM","logo_file":"","short_description":"SBMOpenMM is a Python library to run protein structure-based model (SBM) simulations using the OpenMM toolkit.","long_description":"The library offers flexibility for creating SBM force fields that can be customized to capture different aspects of protein SBM potential energy exploration.\n\nConsidering an input structure, the library automatizes the creation of forces to specify it as the only minimum configuration in the potential energy function. Bonds, angles, and torsions are maintained close to their equilibrium configuration, while native contact interactions can form and break using regular or modified Lennard-Jones potentials. This allows complete and local protein unfolding, restricting the interactions only to the evolutionarily relevant chemical contacts and exploring the relevant configurational space of protein folding and function. \n\nDifferent granularities for the models can be selected as All-heavy-Atom and alpha-carbon representations. These basic models can also be extended to multi-basin potentials employing more than one input configuration. Here, shared native contacts are modeled with special Gaussian functions to allow for more than one equilibrium distance. \n\nThe library offers many methods to tailor forcefield parameters and definitions for each force term. Combining these basic methods and force implementations, sbmOpenMM offers an easy setup of a more complex force field definition that can aid in better exploration of different biophysical phenomena.","has_downloads":false,"keywords":"Protein folding,Structure-based models","ontologies":"","projMembers":"Martin Floor","trove_cats":[{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"406","fullname":"Protein"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1803","unix_group_name":"ue-reaching","modified":"1607141912","downloads":"225","group_name":"Upper Extremity Reaching","logo_file":"","short_description":"We introduce a computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements and is more biomechanically realistic than previous models.","long_description":"Code to perform a predictive simulation for upper extremity reaching.  We use the upper the model introduced here: https://simtk.org/projects/upexdyn.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Mazen Al Borno","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1809","unix_group_name":"sprinthurdles","modified":"1571962015","downloads":"0","group_name":"Sprint Hurdling","logo_file":"","short_description":"Looking to create a visualization of muscular activity in sprint hurdling in regards to the sport of track and field.","long_description":"Looking to create a visualization of muscular activity in sprint hurdling in regards to the sport of track and field.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Andrew Chabon","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1810","unix_group_name":"spine-stiffness","modified":"1575989098","downloads":"115","group_name":"Thoracolumbar spine model with physiological functional spinal units","logo_file":"","short_description":"We presented a novel model of level-specific, nonlinear functional spinal stiffness and integrated this model in an existing rigid-body model of the thoracolumbar spine (https://simtk.org/projects/spine_ribcage). Also stiffening effects of compressive loa","long_description":"We presented a novel model of level-specific, nonlinear functional spinal stiffness and integrated this model in an existing rigid-body model of the thoracolumbar spine (https://simtk.org/projects/spine_ribcage). Also stiffening effects of compressive loading and thoracic spine interaction were successfully integrated. All 6-DoF of the intervertebral joints spanning from T1 to S1 were enabled.\nThe stiffness formulations are comprehensive enough to capture the physiological response of spinal kinematics under various loading conditions, and are also simple and generic enough to be used for various spine modelling studies.\n\nFor more information, please see our paper: \nImplementation of physiological functional spinal units in a rigid-body model of the thoracolumbar spine. Journal of Biomechanics. https://doi.org/10.1016/j.jbiomech.2019.109437","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Wei Wang","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1811","unix_group_name":"cat-hindlimb","modified":"1582274596","downloads":"677","group_name":"Three Dimensional Musculoskeletal Model of The Cat Hindlimb","logo_file":"","short_description":"This project provides the musculoskeletal model of the cat hindlimb. A three-dimensional model of the cat hindlimb skeleton was created using CT images obtained from one male adult cat. The two-dimensional CT images were converted into a three-dimensional","long_description":"This project provides the musculoskeletal model of the cat hindlimb. A three-dimensional model of the cat hindlimb skeleton was created using CT images obtained from one male adult cat. The two-dimensional CT images were converted into a three-dimensional model using an image processing software package called 3D Doctor (Able Software Corp., USA). The model of the cat hindlimb consisted of five rigid bodies – pelvis, thigh, shank, foot, and the digits – and 13 DOF.  The pelvis was represented as a 6-DOF free joint, the hip as a 3-DOF ball-and-socket joint, the knee as a 1-DOF hinge joint, the ankle as a 2-DOF universal joint, and the metatarsophalangeal joint as a 1-DOF hinge joint. Muscle origin and insertion sites were determined using published anatomical records. Via points and wrapping surfaces were added to provide an anatomically realistic path for each muscle-tendon unit (MTU). \nPhysiological parameters for the model of muscle-tendon actuation (i.e., peak isometric muscle force and the corresponding optimal muscle-fiber length and pennation angle as well as tendon slack length) were obtained from the literature.\nAssociated publication includes more detail regarding modeling process and results of the study.\n\n\nIf you use this study for your work, please cite the following publication:\n\nKarabulut, D., Dogru, S.C., Lin, Y.C., Pandy, M.G., Herzog, W., Arslan. Y.Z, 2020, &quot;Direct Validation of Model Predicted Muscle Forces in the Cat Hindlimb During Locomotion,&quot; ASME J. Biomech. Eng., 142(5).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Yunus Ziya Arslan,Derya Karabulut,suzan cansel doğru","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1812","unix_group_name":"postureinoffice","modified":"1572503373","downloads":"0","group_name":"human pose recognition","logo_file":"","short_description":"to recognize human posture while sitting in an office","long_description":"to recognize human posture while sitting in an office","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yijing Xiao","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1814","unix_group_name":"mp1","modified":"1572824204","downloads":"0","group_name":"Midterm Project 200000 elements","logo_file":"","short_description":"Midterm Project 200000 elements","long_description":"Midterm Project 200000 elements","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Pablo Gonzalez Polanco","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1815","unix_group_name":"opensim-moco","modified":"1600300800","downloads":"2433","group_name":"OpenSim Moco","logo_file":"opensim-moco","short_description":"OpenSim Moco solves optimal control problems with musculoskeletal models defined in OpenSim, using direct collocation.","long_description":"OpenSim Moco is a software toolkit to solve optimal control problems with musculoskeletal models defined in OpenSim, including those with kinematic constraints. Using the direct collocation method, Moco can solve a wide range of problems, including motion tracking, motion prediction, and parameter optimization. The design of Moco focuses on ease-of-use, customizability, and extensibility. Just like OpenSim itself, Moco has interfaces in XML/command-line, Matlab, Python, Java, and C++.\n&lt;ul style=&quot;line-height: 100%;&quot;&gt;\n  &lt;li&gt;&lt;a href=&quot;https://opensim.stanford.edu/moco&quot;&gt;Read the &lt;b&gt;documentation&lt;/b&gt;&lt;/a&gt;&lt;/li&gt;\n  &lt;li&gt;&lt;a href=&quot;https://github.com/opensim-org/opensim-moco&quot;&gt;View the source code, report bugs, suggest features, or contribute on &lt;b&gt;GitHub&lt;/b&gt;&lt;/a&gt;&lt;/li&gt;\n  &lt;li&gt;&lt;a href=&quot;https://www.biorxiv.org/content/10.1101/839381v1&quot;&gt;Read the Moco preprint on &lt;b&gt;bioRxiv&lt;/b&gt;&lt;/a&gt;&lt;/li&gt;\n  &lt;li&gt;&lt;a href=&quot;https://github.com/stanfordnmbl/mocopaper&quot;&gt;Obtain the models, data, and code used to produce the Moco preprint&lt;/a&gt;&lt;/li&gt;\n  &lt;li&gt;&lt;a href=&quot;https://opensim.stanford.edu/support/event_details.php?id=236&amp;title=Webinar-OpenSim-Moco-Software-to-optimize-the-motion-and-control-of-OpenSim-models&quot;&gt;Watch the recording of the Moco &lt;b&gt;webinar&lt;/b&gt; from November, 2019&lt;/a&gt;&lt;/li&gt;\n&lt;/ul&gt;","has_downloads":true,"keywords":"direct collocation,musculoskeletal simulation","ontologies":"","projMembers":"Christopher Dembia,Antoine Falisse,Jennifer Hicks,Scott Delp,Nicholas Bianco","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1819","unix_group_name":"ststransfer","modified":"1573498957","downloads":"0","group_name":"STStransferasistance","logo_file":"","short_description":"STStransferasistance","long_description":"STStransferasistance","has_downloads":false,"keywords":"","ontologies":"","projMembers":"聂 倩文","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1822","unix_group_name":"thumbindexdynam","modified":"1607093132","downloads":"252","group_name":"Dynamic musculoskeletal model of the human hand and wrist","logo_file":"","short_description":"This is a detailed model of the human index finger, thumb and wrist.  All muscles of the index finger and thumb were modeled to match anatomical moment arm curves for each joint present in literature.  All extrinsic muscles were modeled with appropriate w","long_description":"This is a detailed model of the human index finger, thumb and wrist.  All muscles of the index finger and thumb were modeled to match anatomical moment arm curves for each joint present in literature.  All extrinsic muscles were modeled with appropriate wrist flexion/extension moment arms as well.\nEach joint axes was modeled to match anatomical representations of the human index finger and thumb from literature.  This is especially important for the multiple DOF of the thumb joints, which were modeled as non-orthogonal, non-intersecting axes of rotation.\nPassive joint stiffness has been added to the finger and thumb joints as well, matching what is seen anatomically, this allows the model to react dynamically.\n\nModel used in literature at: https://doi.org/10.1016/j.jbiomech.2018.06.017","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Alexander Barry","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1825","unix_group_name":"pesiiitutorial1","modified":"1574314438","downloads":"0","group_name":"pesiiitutorial1","logo_file":"","short_description":"my first opensim tutorial","long_description":"my first opensim tutorial","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Phillip Smith","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1828","unix_group_name":"eternabot2","modified":"1574655554","downloads":"0","group_name":"Eternabot 2.0","logo_file":"","short_description":"Eternabot 2.0 is a set of rules developed by the Eterna project (eternagame.org) to predict sequences for a target RNA secondary structure","long_description":"Eternabot 2.0 is a set of rules developed by the Eterna project (eternagame.org) to predict sequences for a target RNA secondary structure","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Joseph Yesselman","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1830","unix_group_name":"abf","modified":"1575184022","downloads":"0","group_name":"Aortic Blood Flow after Complex TEVAR","logo_file":"abf","short_description":"TEVAR has become the prefered option for the treatment of aortic diseases. Computational hemodynamics is conducive to demostrate the impact of complex TEVAR and aid clinicians to improve decision-making process.","long_description":"TEVAR has become the prefered option for the treatment of aortic diseases. Despite short-term clinical follow-up data are satisfactory, long-term effeacy is still not clear. Computational hemodynamics is conducive to demostrate the impact of TEVAR and aid clinicians to improve decision-making process.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yonghui Qiao","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1833","unix_group_name":"twofootjump","modified":"1575485818","downloads":"0","group_name":"Bilateral Approach Jumping","logo_file":"","short_description":"Studying a model of bilateral approach jumping commonly used in team sports and unstudied when compared to the stationery counterparts.","long_description":"Studying a model of bilateral approach jumping commonly used in team sports and unstudied when compared to the stationery counterparts.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sam Liu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1835","unix_group_name":"j-1-t2dm","modified":"1576740848","downloads":"0","group_name":"Intestinal flora in type 2 diabetic patients","logo_file":"","short_description":"The study explored the intestinal flora of patients with type 2 diabetes (T2DM) during low-fat diets by 16S high-throughput sequencing. Group C is the intestinal flora of healthy individuals which is a control. Group T0-T3 is the intestinal flora of T2DM ","long_description":"The study explored the intestinal flora of patients with type 2 diabetes (T2DM) during low-fat diets by 16S high-throughput sequencing. Group C is the intestinal flora of healthy individuals which is a control. Group T0-T3 is the intestinal flora of T2DM patients. T0, T1, T2, and T3 are the composition of the intestinal flora at the time of initial diagnosis, 1 month, 3 months, and 6 months after low-fat diet treatment. In addition, blood glucose and other data of T2DM patients were added to the table.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jie Jin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1839","unix_group_name":"pdwalking","modified":"1576652353","downloads":"0","group_name":"Understanding Walking in Parkinson's Disease with Wearable Sensors","logo_file":"","short_description":"The goal of this project is to better understand walking and impaired walking (slow, shuffling or freezing of gait), in Parkinson's disease as it compares to that of age-matched healthy controls using data from wearable inertial measurement unit sensors.","long_description":"The goal of this project is to better understand walking and impaired walking (slow, shuffling or freezing of gait), in Parkinson's disease as it compares to that of age-matched healthy controls. We use data from wearable inertial measurement unit sensors (IMUs, made by APDM Inc.) to calculate gait parameters as previously described (Syrkin-Nikolau et al., Neurobiology of disease, 2017) that characterize the dynamics of people walking in both a turning and barrier course designed to elicit freezing of gait (Syrkin-Nikolau et al., Neurobiology of disease, 2017) and over forty meters of straight forward walking, the clinical test of Parkinsonian gait. Sensors are applied to both feet, shanks along with the lumbar and chest regions of the trunk. \n\nThe records in this database are from patients with Parkinson's disease who experience severe walking symptoms like freezing of gait (FOG), also identified as &quot;freezers&quot; as well as data from people with Parkinson's disease who do not experience FOG (non-freezers). For comparison, we also have a small cohort of age-matched healthy controls who do not have Parkinson's disease and who completed the walking tasks. There were 20 total subjects with Parkinson's disease that completed the walking tasks, but 11 of these subjects returned for a later research visit where they completed the same tasks (at least 1 year later). This makes a total of 31 records from subjects with Parkinson's disease and 9 records from age-matched healthy control subjects, for a total of 40 records. \n\nRelated publication:\nO'Day, J., Syrkin-Nikolau, J., Anidi, C., Kidzinski, L., Delp, S., &amp; Bronte-Stewart, H. (2019). The Turning and Barrier Course: a standardized tool for identifying freezing of gait and demonstrating the efficacy of deep brain stimulation. bioRxiv, 671479.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Johanna O'Day","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1841","unix_group_name":"shmcbykt","modified":"1701609455","downloads":"0","group_name":"Effects of Inhibitory Kinesiotaping on Scapular Neuromuscular Control ","logo_file":"","short_description":"This study examined the acute effects of inhibitory Kinesiotaping (iKt) on the activity ratio of the lower trapezius (LTr), scapular posterior tilt (PT), and total shoulder proprioceptive variability (E). ","long_description":"The intricate control of the shoulder girdle involves the interplay among scapular muscles, scapulohumeral kinematics, and proprioceptive signals from the shoulder joint. Kinesiotaping, used by therapists, facilitates, or inhibits activities of treated and non-treated muscle. This study examined the acute effects of inhibitory Kinesiotaping (iKt) on the activity ratio of the lower trapezius (LTr), scapular posterior tilt (PT), and total shoulder proprioceptive variability (E). Twenty healthy participants underwent iKt on the dominant upper trapezius. Participants were blindfolded and performed five active shoulder position sense at 100° (proprioceptive tasks). Simultaneous recordings of both the lower trapezius and serratus anterior activity, along with scapulohumeral kinematics, were conducted in both frontal and scapular planes under iKt and no-tape conditions. ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"RAUL FIGUEROA","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1848","unix_group_name":"bkga","modified":"1579041010","downloads":"0","group_name":"BK Amputation","logo_file":"","short_description":"Gait analysis for below knee amputees with a passive prosthetic device.","long_description":"Gait analysis for below knee amputees with a passive prosthetic device.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Matthieu HAENTJENS","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1849","unix_group_name":"abm-vml","modified":"1587754906","downloads":"11","group_name":"ABM of tissue regeneration following VML injury","logo_file":"abm-vml","short_description":"This agent-based model (ABM) simulates cellular behaviors following volumetric muscle loss (VML) injury to predict the tissue remodeling response.","long_description":"This agent-based model (ABM) simulates cellular behaviors following volumetric muscle loss (VML) injury to predict the tissue remodeling response. The simulation focuses on the dynamics of fibroblasts and satellite stem cells and incorporates the behaviors and interactions of muscle fibers, ECM, growth factors and pro-inflammatory and anti-inflammatory macrophages. The model has the ability to simulate the tissue regeneration following an unrepaired VML injury, an injury treated with decellularized ECM, or an injury treated with minced muscle graft. \n\nThe project includes source code for the ABM. The model was built in Repast, a Java based modeling platform.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Silvia Blemker,Amanda Westman,Shayn Peirce-Cottler","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1853","unix_group_name":"knee-contact","modified":"1587568741","downloads":"356","group_name":"A model for medial/lateral knee contact forces during high knee and hip flexions","logo_file":"knee-contact","short_description":"This model adopts a generic MSKM that allows a large range of motion of the knee and hip joints to compute medial and lateral tibiofemoral contact forces (TFCF) during gait and squat tasks.","long_description":"Current musculoskeletal models (MSKM) that allow high knee and hip flexions, only estimate the joint reaction load in the tibiofemoral joint at a single point, not providing the loading at the medial and lateral knee compartments. This study aimed to adapt a generic MSKM that allows a large range of motion of the knee and hip joints to compute medial and lateral tibiofemoral contact forces (TFCF) during gait and squat tasks. The new model was customized by including medial and lateral compartments geometries that allow computing the vertical TFCF forces in both compartments, and further compared to the original MSKM. Both MSKM did not differ their kinematics and kinetics outputs in both of the tasks, and the sum of the vertical TFCF in the medial and lateral was equivalent to the net TFCF of the original MSKM. The modified MSKM brings an important contribution toward understanding the medial and lateral knee loading during movements involving a large range of motion of the lower-limb joints.\n\nTo cite this article: Bedo, BLS; Catelli, DS; Lamontagne, M; Santiago, PRP. (2020) A custom musculoskeletal model for estimation of medial and lateral tibiofemoral contact forces during tasks with high knee and hip flexions. Computer Methods in Biomechanics and Biomedical Engineering, in press.\ndoi: 10.1080/10255842.2020.1757662","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Mario Lamontagne,Paulo Santiago,Bruno Bedo,Danilo Catelli","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1857","unix_group_name":"eyesim","modified":"1580153038","downloads":"0","group_name":"A Geometrical Approach to Human Saccade Simulation","logo_file":"","short_description":"This project focuses on implementing a saccadic eye simulator for binocular eye movements that arise due to visual cues. ","long_description":"2EOMs version\nhttps://www.youtube.com/watch?v=kkeva8QgbnU\n\n6EOMs version\nhttps://www.youtube.com/watch?v=mlapG5lRzvs\n\n\nModeling and simulation of human eye movement have a wide range of applications in many domains. Various attempts have been made to model and simulate eye movements in a physically accurate manner. All the existing models show limitations and problems in simulating secondary and tertiary eye movements. A recent investigation of pulley models (passive and active hypotheses) in representing human eye motion has recognized mathematical complexity in modeling eye behavior. Sophisticated techniques of modeling are required to investigate eye movements. This paper presents a procedure for eye movement simulation through geometrical modeling (an OpenSim script with its recent MATLAB binding) for binocular vision. First-order neural dynamics with Millard’s muscle model are used to actuate six Extra Ocular Muscles (EOMs). Pulse-step inputs are used to generate muscle forces around the eye globe. The implemented model is successful in simulating horizontal and vertical movements of the human eye with respect to the prescribed activation. The developed technique is evaluated using responses from lumped parameter models and EOG recordings.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Hiroshan Gunawardane ","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1865","unix_group_name":"normal-load","modified":"1629205220","downloads":"108","group_name":"NORMAL-LOAD","logo_file":"normal-load","short_description":"The aim of this project is to use a multi-scale modelling approach,  combining musculoskeletal rigid-body simulations with adaptive finite element analyses, to predict femoral bone growth. ","long_description":"In the first study, we investigated the impact of femoral geometry, i.e. different neck-shaft angle and anteversion angle, on hip joint contact forces and femoral growth prediction. We uploaded following material:\n- motion capture data\n- used OpenSim model\n- finite element model of the femur\n- results from the musculoskeletal and femoral growth simulations\n\nThis project and HK was funded by a H2020-MSCA individual fellowship (796120).\n\nMore details can be found in our publication (please also cite this paper when re-using the data):\n\nKainz H., Killen B.A., Wesseling M., Perez-Boerema F., Pitto L., Garcia Aznar J.M., Shefelbine S., Jonkers I. (2020) A multi-scale modelling framework combining musculoskeletal rigid-body simulations with adaptive finite element analyses, to evaluate the impact of femoral geometry on hip joint contact forces and femoral bone growth. PLoS ONE 15(7): e0235966.\n\nhttps://doi.org/10.1371/journal.pone.0235966\n\nIn the second study, we investigated reasons for pathological and typical growth in children with cerebral palsy. The paper was published as the ESB Award Paper in the Journal of Clinical Biomechanics.\n\nhttps://doi.org/10.1016/j.clinbiomech.2021.105405\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ilse Jonkers,Lorenzo Pitto,Bryce Killen,hans kainz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1867","unix_group_name":"labmusc","modified":"1581381832","downloads":"0","group_name":"Understanding skeletal muscle function","logo_file":"","short_description":"Understanding skeletal muscle function","long_description":"Understanding skeletal muscle function","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thiago Matta","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1869","unix_group_name":"os-files-reader","modified":"1595494894","downloads":"783","group_name":"STO / TRC / MOT files plotter","logo_file":"os-files-reader","short_description":"This Matlab tool allows the user to load and plot data from up to 4 different files among sto, trc and mot extensions.\n\n","long_description":"This Matlab tool allows the user to load up to 4 different files among sto, trc and mot extensions.\nThe user has the possibility to add a TAG name for each file.\nThis TAG name will be used as legend to plot the selected data in the next window.\nThe user will also have the possibility to extract ALL or only the desired data in a mat file.\n\nThis tool is more convenient to use than the Plot tab in OpenSIM for the user.","has_downloads":true,"keywords":"visualization,opensim,Motion Data,plot,biomechanics","ontologies":"","projMembers":"emmanuel ayad","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1871","unix_group_name":"opensim-jam","modified":"1708872295","downloads":"716","group_name":"OpenSim JAM","logo_file":"opensim-jam","short_description":"OpenSim Joint and Articular Mechanics (JAM)\n\nThis project is a set of new components, models, and simulation tools developed to enable multibody simulations of joint mechanics in OpenSim.","long_description":"This page is a work in progress as the project transitions from the original plugin version to the new version described below. Downloads and documentation will be provided shortly.\n\nThe Downloads page contains release versions of OpenSim JAM. \n\nOpenSim-JAM Core\nOpenSim-JAM has now been integrated into a forked version of the opensim-core source code. This means that all standard OpenSim interfaces (command line, python and MATLAB APIs) can be used if you download the packaged form. Release versions of the Windows, Linux (ubuntu), and Mac packages are available on the Downloads page.\nhttps://github.com/opensim-jam-org/opensim-core\n\nOpenSim-JAM Plugin\nThe OpenSim-JAM download does not include the OpenSim GUI. To visualize models and simulation results in the GUI, you must download the standard OpenSim installation and load the OpenSim-Jam Plugin. \nhttps://github.com/opensim-jam-org/jam-plugin\n\nOpenSim-JAM Resources\nDocumentation, Models, and examples are made available through the opensim-jam-resources package. The documentation is best viewed on the github page.   \nhttps://github.com/opensim-jam-org/jam-resources\n\nFor the original plugin version, all documentation and source code is archived at the following link but no longer actively developed: \nhttps://github.com/clnsmith/opensim-jam\n\nFor installation in MATLAB use the steps defined here:\nhttps://simtk-confluence.stanford.edu:8443/display/OpenSim/Scripting+with+Matlab","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Colin Smith","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1875","unix_group_name":"4her","modified":"1582576518","downloads":"2187","group_name":"Resources for Reproductive Research in Women's Health","logo_file":"","short_description":"This project curates data, models, and related resources for research in women's reproductive health.","long_description":"This project curates data, models, and related resources for research in women's reproductive health.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ahmet Erdemir,Kristin Myers,Steven Abramowitch,Raffaella De Vita","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1876","unix_group_name":"chem-rxn","modified":"1588645422","downloads":"0","group_name":"Chemical Reaction Snorkel Application Supplemental Data","logo_file":"","short_description":"This website contains supplemental data files for 'Extracting chemical reactions from text using Snorkel' (Mallory et al, 2020).\n\nArticle title: Extracting Chemical Reactions from Text using Snorkel\nAuthors: Emily K Mallory, Matthieu de Rochemonteix, A","long_description":"This website contains supplemental data files for 'Extracting chemical reactions from text using Snorkel' (Mallory et al, 2020).\n\nArticle title: Extracting Chemical Reactions from Text using Snorkel\nAuthors: Emily K Mallory, Matthieu de Rochemonteix, Alex Ratner, Ambika Acharya, Chris Re, Roselie A Bright, Russ B Altman","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Russ Altman,Emily Mallory","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1878","unix_group_name":"knee-segment","modified":"1585323261","downloads":"0","group_name":"Tools for automatic segmentation and refinement of knee geometries from MRI","logo_file":"knee-segment","short_description":"Personalized knee geometry modelling based on multi-atlas segmentation and mesh refinement","long_description":"The development of personalized finite element models of the knee anatomy is critically important in the simulation of knee joint mechanics, prediction of optimal treatments in cases of pathological conditions and prevention of injuries. Subject-specific models can be obtained from diagnostic images with multi-atlas segmentation being a pertinent choice when prior anatomical information of the structures of interest is available. Although multi-atlas segmentation has been prevalent in some parts of the body, its exploitation for the segmentation of the knee complex has not been illustrated yet. This work utilizes a multi-atlas segmentation method based on deformable registration and joint label fusion in conjunction with anatomically-adopted mesh refinement in order to generate subject-specific models of the knee. The success of finite element simulations strongly depends on the properties of the 3D surface and the quality of the volumetric meshes. Therefore, emphasis was given to create structured meshes with well-shaped hexahedra for the knee cartilages and menisci. The segmentation performance is assessed using cross-validation on 7 subjects from the Open Knee project and 78 subjects from the Osteoarthritis Initiative. Our results indicate that our developed state-of-the-art processing scheme can achieve competitive performance, opening the path for better diagnostics and patient-specific interventions.\n\nThe source code and example scripts for performing the automatic segmentation of MRI and geometry refinement suitable for finite element analysis can be found on the following link:\n\nhttps://gitlab.com/vvr/OActive/knee_segmentation_tools.git","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dimitar Stanev,Filippos Nikolopoulos,Konstantinos Moustakas,Evangelia Zacharaki","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1882","unix_group_name":"babymod1","modified":"1587002739","downloads":"0","group_name":"Modelling infant kinematics","logo_file":"","short_description":"The model and kinematics data was generated in fulfillment of a Master of Engineering thesis. An infant model was generated by scaling the full-body model provided by Rajagopal et al. (2016) and used to calcute IK from marker data collected from a novel m","long_description":"The model and kinematics data was generated in fulfillment of a Master of Engineering thesis. An infant model was generated by scaling the full-body model provided by Rajagopal et al. (2016) and used to calcute IK from marker data collected from a novel markerless mocap system. IK of the same infant motions were also capture on a Vicon system which acted as a benchmark.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thor Besier,Lilian Lim,Angus McMorland","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1889","unix_group_name":"strokegait","modified":"1584139751","downloads":"0","group_name":"Mechanical simulation of stroke gait","logo_file":"","short_description":"In this project, we will perform a mechanical simulation of the stroke gait to understand the interaction between the limb center of mass and the body center of mass. We seek to provide new insights into the mechanics and energetics following a stroke.","long_description":"In this project, we will perform a mechanical simulation of the stroke gait to understand the interaction between the limb center of mass and the body center of mass. We seek to provide new insights into the mechanics and energetics following a stroke.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Gustavo Balbinot","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1893","unix_group_name":"longdistrun","modified":"1675578884","downloads":"129","group_name":"Dataset of Biomechanics in the Lower Extremity following Distance Running","logo_file":"longdistrun","short_description":"This project is aimed to investigate the biomechanical changes in the lower extremity via musculoskeletal modelling during prolonged running activities. The raw dataset (in C3D files) and pre-processed dataset (in mat files) are provided for continued research.","long_description":"This project is aimed the investigate the biomechanical changes in the lower extremity via musculoskeletal modelling during pre 5k and post 5k running sessions. The angles, moments, and contact forces of the hip, knee and ankle joints were calculated for pre and post comparison. Statistical parametric mapping (spm1d) was taken to check the significance in the time-series data. Principal component analysis (PCA) modeling was conducted the reveal the key features of variation.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Qichang Mei","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1901","unix_group_name":"mfs","modified":"1585528844","downloads":"0","group_name":"Mouse Forelimb Model","logo_file":"","short_description":"Device motion movement","long_description":"Device motion movement","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Syed Rizvi","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1905","unix_group_name":"sars-cov-2","modified":"1587357614","downloads":"0","group_name":"Protonations states changes of repurposing drugs in binding with TMPRSS2 enzyme","logo_file":"sars-cov-2","short_description":"This research will computationally study the influence of pH on the SARS-CoV-2 and SARS-CoV virus receptors. the objective will be to find out which pH is the most suitable for each complex, and which will allow greater spontaneity in the interaction between the drug and the receptors involved in viral replication. ","long_description":"According to the World Health Organisation, on April 19, 2020, the number of confirmed cases of COVID-19 had already surpassed the mark of 2 240 000, with about 152 000 confirmed deaths worldwide. In Brazil, these numbers translated into 36 929 in patients affected by the virus and 2 372 deaths, with community transmission of COVID-19 throughout the national territory. The new beta-Coronavirus has been causing immense losses worldwide, in different aspects. Researches have indicated a correlation between the potential for inhibition of the SARS-CoV virus and the physiological pH of the cellular microenvironment. This research will computationally study the influence of pH on the SARS-CoV-2 and SARS-CoV virus receptors. Among the drugs researched for the remission of symptoms, Chloroquine and its derivatives, have stood out as potential for treatment. However, many doubts and contradictions still remain. In this research, we will seek to understand how the physiological pH of the different subcellular compartments can affect the protonation state of the receptor-ligand complex, and consequently the free energy value of interaction. Thus, the objective will be to find out which pH is the most suitable for each complex, and which will allow greater spontaneity in the interaction between the drug and the receptors involved in viral replication. The protonation and addition of partial charges will be calculated varying pH conditions in range 4.0-9.0. The structures will be optimized with mechanical-quantum calculations. Molecular docking simulations will be performed to understand the correlation dependence between the pH value and free energy. Classical molecular dynamics simulations will also be employed to better understand the conformational changes in the complex. ADMET studies will be conducted to understand which Chloroquine derivatives are safer and more effective in treatment.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Micael Davi Lima de Oliveira","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1906","unix_group_name":"shoulder-ssdm","modified":"1621439201","downloads":"253","group_name":"Statistical Shape Models and Statistical Density Models of the Shoulder Bones","logo_file":"","short_description":"Statistical Shape Models (SSMs) and Statistical Density Models (SDMs) of the Shoulder Bones Based upon 75 CT Images. \n","long_description":"Please download our MATLAB codes and run the main file to use the SSMs and SDMs of the humerus and scapula bones.\nThe SSM will create a surface model whose shape is sensitive to the normalized PC scores chosen in the app. The SDM will apply node-by-node HU values to a 3D template volumetric mesh (from the average geometry), allowing you to visualize the bone density distribution which is sensitive to the normalized PC scores chosen in the app.\nThe output files can be visualized using open-source software like ParaView (www.paraview.org).\n\nPLEASE CITE:\nPendar Soltanmohammadi, Josie Elwell, Vishnu Veeraraghavan, George S. Athwal, and Ryan Willing. &quot;Investigating the Effects of Demographics on Shoulder Morphology and Density Using Statistical Shape and Density Modeling.&quot; Journal of Biomechanical Engineering 142, no. 12 (2020).","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Pendar Soltanmohammadi,Ryan Willing","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1908","unix_group_name":"acl-injury","modified":"1616526231","downloads":"0","group_name":"Assessment of factors influencing non-contact anterior cruciate ligament injury","logo_file":"","short_description":"This project aims to provide a platform to explore mechanical markers for non-contact acl injury.","long_description":"Anterior cruciate ligament (ACL) injury is a serious concern for athletes, such as soccer\nplayers. In young, active athletes – the population most at risk of sustaining an ACL injury – re-injury or contralateral ACL injury can be as high as 20%. Identifying mechanical markers that may indicate potential risk of such re-injuries is thus a critical element of safe return to sport. Computational methods can address this need by providing means to study factors that cannot be studied directly in vivo or in vitro. Assessment of factors such as injury kinematics, ACL tissue and knee geometry, material properties, etc, performed using finite element methods may help to understand how non-contact ACL injury occurs or if there is predisposition for re-injury. \n\nThe goal of this study is to assess and identify potential mechanical markers which may indicate predisposition to ACL injuries and subsequently post-traumatic osteoarthritis.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Snehal Chokhandre,Ahmet Erdemir,Neda Abdollahi,Sebastian Janampa,Ben Landis","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1909","unix_group_name":"highheel","modified":"1586632878","downloads":"0","group_name":"Innovative High Heel","logo_file":"","short_description":"Designing an innovative high heel shoe. Hoping to test design with some modified gait analysis or force distribution.","long_description":"Designing an innovative high heel shoe. Hoping to test design with some modified gait analysis or force distribution.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Hunter Hasley","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1910","unix_group_name":"umocod","modified":"1689000464","downloads":"405","group_name":"Moco simulations at University of Maryland","logo_file":"","short_description":"Models and codes from the University of Maryland \"Neuromechanics Research Core\" for performing optimal control simulations of human movement with OpenSim Moco software.  We provide both 2D and 3D versions of the model and code for performing \"tracking\" and \"predictive\" simulations of locomotion.","long_description":"Models and codes from the University of Maryland &quot;Neuromechanics Research Core&quot; for performing optimal control simulations of human movement with OpenSim Moco software.  Currently we provide a 2-D version of the Rajagopal et al. (2016) model for performing simulations of walking, as well as a 3-D version with two intact limbs and a 3-D version with a unilateral transtibial prosthesis.  Mean experimental data from Miller et al. (2014) are included for performing data-tracking simulations or as a basis of comparison for predictive simulations.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ross Miller","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1912","unix_group_name":"pd-tf-socket","modified":"1586890794","downloads":"0","group_name":"Estimating Pressure Distribution in Transfemoral Amputee Socket","logo_file":"","short_description":"The goal of this project will be to use a contact modeling approach to estimate pressure distribution in the socket-stump interface for both Static and Dynamic(gait cycle) loads.","long_description":"Limb amputation currently affects 1.7 million people in the United States. Approximately half have experienced a below knee amputation. A challenge faced today by several amputees is the discomfort caused in prosthetics. Although attempts have been made to perfect contact made between prosthetic socket and stump of the amputated limb, discomfort is still caused due to several reasons. Studies have been conducted to identify these reasons for pain or discomfort in the transfemoral prosthesis. Although factors like temperature and heat are factors in causing discomfort, their impact on the patient is insignificant compared to the discomfort caused by irregular pressure distribution, friction, shear and slippage. Therefore, to quantify the discomfort caused due to the prosthetic and amputee stump surface contact conditions, it is first important to measure the pressure distribution in the prosthetic socket-amputee stump contact area.\n\nBy estimating pressure distribution in the socket-stump interface, conclusions such as design alternatives to reduce discomfort or optimal pressure sensor placement in test rigs can be made.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Suranjan Ottikkutti","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1913","unix_group_name":"gh-caps-sims","modified":"1606382311","downloads":"139","group_name":"Simulating Selective Glenohumeral Capsulorrhaphy","logo_file":"gh-caps-sims","short_description":"Code and data for project simulating selective glenohumeral capsulorrhaphy in an upper limb musculoskeketal model and running predictive simulations of movement. \n\nThis project is related to the the paper:\n\nFox AS, Bonacci J, Gill SD, Page RS. Simulating the effect of glenohumeral capsulorrhaphy on kinematics and muscle function. J Orthop Res. https://doi.org/10.1002/jor.24908\n\nPlease cite our work if you use this code or data.","long_description":"Code and data for project simulating selective glenohumeral capsulorrhaphy in an upper limb musculoskeketal model and running predictive simulations of movement. Information on the use of code and data can be found in the read-me file contained within the archive folder download.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Aaron Fox","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1916","unix_group_name":"senseback-ephap","modified":"1587482724","downloads":"1","group_name":"Modelling the Effects of Ephaptic Coupling on Selectivity and Response Patterns","logo_file":"","short_description":"This work presents simulations of stimulation with cuff electrodes and propagation in a peripheral nerve model which includes a novel method to define ephaptic interactions.","long_description":"This work presents simulations of stimulation with cuff electrodes and propagation in a peripheral nerve model which includes a novel method to define ephaptic interactions.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Miguel Capllonch Juan","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1918","unix_group_name":"video-gaitlab","modified":"1606854737","downloads":"322","group_name":"Quantitative movement analysis using single-camera videos","logo_file":"","short_description":"In this project we provide tools for analyzing gait using a video footage from a single camera. Our software is based on machine learning models, in particular on convolutional neural networks. \nTo train our machine learning models we used a dataset of o","long_description":"In this project we provide tools for analyzing gait using a video footage from a single camera. Our software is based on machine learning models, in particular on convolutional neural networks. \nTo train our machine learning models we used a dataset of over 3,000 videos processed with computer vision software finding body landmarks in frames (OpenPose). We provide the source code, dataset of clinical measurements as well as trajectories of body landmarks from OpenPose.\n\nTwo key objectives of this SimTK project are:\n- providing a toolset for derviation of gait parameters from videos\n- providing a dataset allowing further research on gait analysis from videos using data-driven methods.\n\nTry out an online demo at: http://gaitlab.stanford.edu\nDownload the source code at: https://github.com/stanfordnmbl/mobile-gaitlab","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Łukasz Kidziński","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1920","unix_group_name":"scaffold","modified":"1587764990","downloads":"0","group_name":"Scaffold Hops","logo_file":"","short_description":"A set of scripts used with PocketFEATURE to search for protein scaffold hops.","long_description":"This project was developed by Allison M Keys with support and guidance from Stefano Rensi, Russ Altman, and the Helix Group at Stanford University. \n\nPurpose: Use PocketFEATURE to find highly similar protein binding pockets to for use in scaffold hop finding and drug repurposing. Scripts can be used to prepare PDB files, run PocketFEATURE scripts, and analyze output data. \n\nGithub: https://github.com/allikeys/pocketFEATURE_analysis","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Allison Keys","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1921","unix_group_name":"squirrel-foot","modified":"1587943287","downloads":"0","group_name":"Musculoskeletal Model for Squirrel Limbs","logo_file":"","short_description":"A kinematic simulation of a squirrel's leg and foot to study, visualize, and understand how bones, muscles, and ligaments affect nimble arboreal locomotion. OpenSim would most likely help us by allowing us to run sensitivity analyses on the musculoskeleta","long_description":"A kinematic simulation of a squirrel's leg and foot to study, visualize, and understand how bones, muscles, and ligaments affect nimble arboreal locomotion. OpenSim would most likely help us by allowing us to run sensitivity analyses on the musculoskeletal structure. As a visualization tool, an easy to change virtual model is fitting.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sebastian Lee","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1922","unix_group_name":"1dna","modified":"1588009117","downloads":"0","group_name":"draw DNA sequence","logo_file":"","short_description":"I need to draw DNA sequence to turn it to pdb later for docking","long_description":"I need to draw DNA sequence to turn it to pdb later for docking","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Hanaa Alam El-Din","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1925","unix_group_name":"humangait","modified":"1588613032","downloads":"0","group_name":"Human Gait Analysis","logo_file":"","short_description":"I want to find information related to gait patterns of different individuals of the same age group and I want to compare them with the health conditions of the individuals.","long_description":"I want to find information related to gait patterns of different individuals of the same age group and I want to compare them with the health conditions of the individuals.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Rima Kalita","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1934","unix_group_name":"147seq","modified":"1589930145","downloads":"0","group_name":"uc.147 structure","logo_file":"","short_description":"The aim of this project is to study the interactions of uc.147 with other proteins.","long_description":"The aim of this project is to study the interactions of uc.147 with other proteins.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ana Carolina","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1936","unix_group_name":"auto-sk-models","modified":"1626307200","downloads":"65","group_name":"Automatic Generation of Personalised Skeletal Models of the Lower Limb","logo_file":"auto-sk-models","short_description":"The aim of this project is to automatically generate skeletal OpenSim models using three-dimensional bone geometries obtained from medical images using the <a href=\"https://simtk.org/projects/msk-staple\">STAPLE toolbox</a>. \nThe automatically created models are compared against models manually created from the same anatomical datasets. ","long_description":"To take full advantage of the materials shared in this project page we recommend consulting also the resources listed below:\n&lt;ul&gt;\n&lt;li&gt; detailed documentation on how to use the package downloadable from this page, together with a source-versioned history of each file, is available at: https://github.com/modenaxe/auto-lowerlimb-models-paper\n\n&lt;li&gt;the latest stable release of the STAPLE toolbox will be available at https://simtk.org/projects/msk-staple.\n\n&lt;li&gt; a freely accessible copy of the publication is available at https://www.sciencedirect.com/science/article/pii/S0021929020306102\n&lt;/ul&gt;\nThis work is framed in a long-term plan to advance the state of the art of anatomical modelling and subject-specific modelling of the musculoskeletal system through automation of its most challenging technical tasks.  The projects linked to this project are also part of this longer term plan.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Luca Modenese,Jean-Baptiste RENAULT","trove_cats":[],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":false},{"group_id":"1939","unix_group_name":"ankleexopred","modified":"1698631999","downloads":"2436","group_name":"Data-driven prediction of gait with ankle exoskeletons","logo_file":"ankleexopred","short_description":"We used these datasets to predict changes in kinematics and muscle activity in response to varying ankle exoskeleton stiffness.","long_description":"The datasets included on this page contain walking data from twelve unimpaired adults walking on a treadmill while wearing bilateral passive ankle exoskeletons. Datasets are four minutes long, and contain kinematic and ground reaction force data, and electromyography from seven leg muscles bilaterally. \n\nThe associated Python code can be used to generate data-driven predictive models of response to the ankle exoskeletons. The associated MATLAB code can be used to perform statistical analyses of the data.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Katherine Steele,Michael Rosenberg","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"},{"id":"426","fullname":"Network Modeling and Analysis"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"1940","unix_group_name":"contactmodeling","modified":"1590957017","downloads":"0","group_name":"Contact Modeling of the Fingerpad","logo_file":"","short_description":"Provide models and simulations to study hand contact modeling and comparing the Hunt-Crossley and Elastic Foundation contact models.","long_description":"This work strives to build upon the current contact modeling literature by filling a gap in upper-extremity musculoskeletal modeling of hand contact mechanics. Notably, we examine how two common contact models (Hunt-Crossley and Elastic Foundation) can be used to represent the fingerpad. To evaluate the use of Hunt-Crossley and Elastic Foundation contact models in a low-force environment (i.e. the fingerpad), we developed a musculoskeletal model and performed a sensitivity analysis of key model parameters.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kevin Hao","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1942","unix_group_name":"msk-staple","modified":"1707350400","downloads":"148","group_name":"STAPLE: Shared Tools for Automatic Personalised Lower Extremity modelling","logo_file":"msk-staple","short_description":"&lt;b&gt;STAPLE&lt;/b&gt;, acronym for &lt;i&gt;Shared Tools for Automatic Personalised Lower Extremity modelling&lt;/i&gt;, is a MATLAB toolbox that enables researchers in the biomechanical field to create models of the lower extremity from subject-specific bone geometries with minimum effort in negligible processing time. In most cases, that can be done just changing the input data in one of the provided workflow and running a MATLAB script.\n\nPlease note that STAPLE is released under a &lt;u&gt;&lt;b&gt;NON COMMERCIAL CC-BY-NC license&lt;/b&gt;&lt;/u&gt; and it is free to use &lt;u&gt;for academic purposes only&lt;/u&gt;. For any other use please contact the authors.\n\nPlease note that the toolbox is still released as beta version. If you encounter any issue please report it &lt;a href=&quot;https://github.com/modenaxe/msk-STAPLE/issues&quot;&gt;at this link&lt;/a&gt;. \n\nAt this page we will release the stable versions of STAPLE. You can follow, participate and contribute to the open development of the toolbox at &lt;a href=&quot;https://github.com/modenaxe/msk-STAPLE&quot;&gt;https://github.com/modenaxe/msk-STAPLE&lt;/a&gt;. ","long_description":"STAPLE requires three-dimensional bone geometries as an input. These geometries are normally surface models segmented from medical images like magnetic resonance imaging (MRI) or computed tomography (CT) scans. STAPLE performs morphological analyses on the provided bone geometries and defines reference systems used to create models of entire legs or few joints, depending on the available data or the research intent. Currently the toolbox creates kinematic and kinetic skeletal models but will soon be extended with complete musculoskeletal capabilities.\n\nUsing STAPLE is currently possible to perform the following operations:\n&lt;ul&gt;\n  &lt;li&gt;Creating complete skeletal models of the lower limb from segmented bone geometries with provided workflows or customizable degrees of freedom for the joints.&lt;/li&gt;\n  &lt;li&gt;Creating partial skeletal models of the lower limb. For example models of hip, knee and ankle joints can be created as individual models.&lt;/li&gt;\n  &lt;li&gt;Extracting the articular surfaces of the lower limb joints, for example the tibiofemoral articular surfaces.&lt;/li&gt;\n&lt;li&gt; Merging generic and personalised models.&lt;/li&gt;\n&lt;li&gt;Basic identification of bony landmarks, intended as first guess for registration with gait analysis motion capture data.&lt;/li&gt;\n&lt;/ul&gt;","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Luca Modenese,Jean-Baptiste RENAULT","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1946","unix_group_name":"gait-event","modified":"1599812257","downloads":"32","group_name":"Automatic real-time gait event detection in children using deep neural networks","logo_file":"","short_description":"Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. Through analysis of 9092 gait cycle measurements we build a predictive model using Long Short-Term Memory (LSTM) artificia","long_description":"Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. Through analysis of 9092 gait cycle measurements we build a predictive model using Long Short-Term Memory (LSTM) artificial neural networks. The best-performing model identifies foot-contact and foot-off events with an average error of 10 and 13 milliseconds respectively, outperforming popular heuristic-based approaches.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Łukasz Kidziński","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1949","unix_group_name":"mtu-darren","modified":"1592253591","downloads":"0","group_name":"Muscle tendon unit","logo_file":"","short_description":"Muscle tendon unit","long_description":"Muscle tendon unit","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Darren Dong ","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1953","unix_group_name":"lysg","modified":"1594654875","downloads":"0","group_name":"LYSG","logo_file":"","short_description":"Persistent Upload of MD run files for Paper in final review stages at Nature Communications. Content of paper is the design of a regulator protein.","long_description":"Persistent Upload of MD run files for Paper in final review stages at Nature Communications. Content of paper is the design of a regulator protein.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dennis Della Corte","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1958","unix_group_name":"shoulderexo","modified":"1606938405","downloads":"255","group_name":"Mechanically Passive Shoulder Exoskeleton Model","logo_file":"shoulderexo","short_description":"This project includes a model of a mechanically passive (i.e. spring-powered), cable-driven shoulder exoskeleton that was added to an existing dynamic model of the upper extremity. The dynamic upper extremity model, the \"MoBL-ARMS Dynamic Upper Limb Model","long_description":"This project includes a model of a mechanically passive (i.e. spring-powered), cable-driven shoulder exoskeleton that was added to an existing dynamic model of the upper extremity. The dynamic upper extremity model, the &quot;MoBL-ARMS Dynamic Upper Limb Model&quot;, is available on the following SimTK project page: Upper Extremity Dynamic Model (https://simtk.org/frs/?group_id=657). The exoskeleton model is described in more detail in the peer-reviewed article listed below. Please cite this article if the model facilitates or inspires your own research. \n\nNelson AJ, Hall PT, Saul KR, Crouch DL. Effect of a Wearable Mechanically Passive Shoulder Exoskeleton on Muscle Activity: A Computational Simulation Study. Journal of Applied Biomechanics. 36(2), pp59-67, 2020. DOI: https://doi.org/10.1123/jab.2018-0369","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Dustin Crouch","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1959","unix_group_name":"sars","modified":"1594403258","downloads":"0","group_name":"sarscov","logo_file":"","short_description":"sars cov alternative stem","long_description":"sars cov alternative stem","has_downloads":false,"keywords":"","ontologies":"","projMembers":"anuja kibe","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1960","unix_group_name":"mlk-taichi","modified":"1594403144","downloads":"0","group_name":"Knee Compartmental Load Distributions During Tai Chi Gait","logo_file":"","short_description":"This project compares the knee compartmental load distributions for Tai Chi and Normal Walking gait. A joint reaction analysis was used in OpenSim to resolve the joint contact loads at the medial and lateral condyles in the knee joint. The results were an","long_description":"This project compares the knee compartmental load distributions for Tai Chi and Normal Walking gait. A joint reaction analysis was used in OpenSim to resolve the joint contact loads at the medial and lateral condyles in the knee joint. The results were analyzed and compared.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Colin Holtkamp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1961","unix_group_name":"daafp","modified":"1594717735","downloads":"0","group_name":"Design of aptamers applied in food packaging","logo_file":"","short_description":"Smart packaging can be applied a promising strategy to inform all people the customers in the production, sales, and distribution chains, such as wholesalers, retailers, and final purchasers, both educated and uneducated, about the authentication and frau","long_description":"Smart packaging can be applied a promising strategy to inform all people the customers in the production, sales, and distribution chains, such as wholesalers, retailers, and final purchasers, both educated and uneducated, about the authentication and fraud of foodstuff. Smart packages are non-destructive methods of for determining the quality of food, the advantages of which are being on-line, no need for specialists, equipment, timereact quicly with a low, and high cost. Herein, smart packaging is designed to authenticate and detect the adulteration of foods and particularly, saffron and milk.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Iman Katouzian","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1962","unix_group_name":"functneuroemerg","modified":"1595758973","downloads":"0","group_name":"Functional neurological disorders presenting as emergencies to secondary care","logo_file":"","short_description":"Data regarding study on functional neurological disorders presenting to hospital and requiring inpatient admission.","long_description":"Data regarding study on functional neurological disorders presenting to hospital and requiring inpatient admission.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"James Beharry","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1963","unix_group_name":"opencolab","modified":"1655290667","downloads":"141","group_name":"ＯｐｅｎＳｉｍＣｏｌａｂ (OpenSim in Google Colab)","logo_file":"opencolab","short_description":"This project aims to make the life of a researcher and end-user a bit easier in dealing with Neuro-Biomechanical models. If you are new to modeling, or even an expert in computational and human/animal movement science, you may have to deal with the installation of several packages. OpenSimColab (OpenSim + Google Colab) allows you to use 𝐎𝐩𝐞𝐧𝐒𝐢𝐦 𝐨𝐧 𝐭𝐡𝐞 𝐰𝐞𝐛 (e.g. Google Colab) with &quot;𝐙𝐞𝐫𝐨 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝, 𝐅𝐫𝐞𝐞 𝐚𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐆𝐏𝐔𝐬 𝐚𝐧𝐝 𝐄𝐚𝐬𝐲 𝐬𝐡𝐚𝐫𝐢𝐧𝐠&quot;. We also called it OpenColab at some point.","long_description":"This project aims to make the life of a researcher a bit easier in dealing with Bio-mechanical models. OpenSim has got 𝐚𝐦𝐚𝐳𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞𝐬 including C++, Python, and Matlab. Normally, the user installs them on their PC. \n\nThis project adds another interface where one can 𝐫𝐮𝐧 𝐧𝐞𝐮𝐫𝐨𝐦𝐮𝐬𝐜𝐮𝐥𝐨𝐬𝐤𝐞𝐥𝐞𝐭𝐚𝐥 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 &amp; 𝐬𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐰𝐞𝐛 (particularly on 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐨𝐥𝐚𝐛). You can imagine 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐨𝐜𝐬 (𝐨𝐧 𝐭𝐡𝐞 𝐰𝐞𝐛) 𝐯𝐬. 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐖𝐨𝐫𝐝 (𝐨𝐧 𝐚 𝐏𝐂).\n\n𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐨𝐥𝐚𝐛: \n&quot;𝐂𝐨𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐨𝐫𝐲, 𝐨𝐫 &quot;𝐂𝐨𝐥𝐚𝐛&quot; for short, allows you to write and execute Python in your browser, 𝐰𝐢𝐭𝐡 𝐙𝐞𝐫𝐨 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝, 𝐅𝐫𝐞𝐞 𝐚𝐜𝐜𝐞𝐬𝐬 𝐭𝐨 𝐆𝐏𝐔𝐬, 𝐄𝐚𝐬𝐲 𝐬𝐡𝐚𝐫𝐢𝐧𝐠. Whether you're a student, a data scientist, or an AI researcher, Colab can make your work easier. Watch Introduction to Colab to learn more, or just get started below! https://colab.research.google.com/ &quot;\n\nIf you are new to modeling, or even an expert in computational and human/animal movement science, you may have to deal with the installation of several packages, and set up a new environment on your computer which usually keeps you away from the actual research.\n\n\nIn this project, we use Google Cloud (esp. Google Colab notebooks) and 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐎𝐩𝐞𝐧𝐒𝐢𝐦 via Anaconda Cloud easily (&lt;𝟕𝐦𝐢𝐧) so that we can use it without any issue at any computer, collaborate with others and share models fast without even a need to install OpenSim on your computer. 𝒀𝒐𝒖 𝒋𝒖𝒔𝒕 𝒏𝒆𝒆𝒅 𝒕𝒐 𝒉𝒂𝒗𝒆 𝒂𝒄𝒄𝒆𝒔𝒔 𝒕𝒐 𝒕𝒉𝒆 𝒊𝒏𝒕𝒆𝒓𝒏𝒆𝒕 𝒂𝒏𝒅 𝑮𝒎𝒂𝒊𝒍 𝒂𝒄𝒄𝒐𝒖𝒏𝒕. The 𝐢𝐧𝐢𝐭𝐢𝐚𝐥 𝐬𝐞𝐭𝐮𝐩 𝐰𝐨𝐮𝐥𝐝 𝐭𝐚𝐤𝐞 &lt;𝟏 𝐦𝐢𝐧. \n\nThat is it! Enjoy OpenSimColabing!\n\nWe also created several 𝐯𝐢𝐝𝐞𝐨 𝐭𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬 (e.g. https://youtu.be/iEjd7OSOitg) to learn Google Colab (basic python programming on the web) and OpenColab (𝐎𝐩𝐞𝐧𝐒𝐢𝐦 + 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐨𝐥𝐚𝐛): https://tinyurl.com/xukhmnez\n \n\n\n𝐇𝐨𝐰 𝐎𝐩𝐞𝐧𝐂𝐨𝐥𝐚𝐛 𝐰𝐨𝐫𝐤𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨 𝐰𝐞 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐢𝐭?\n\nThe image below shows how we developed and validated OpenSimColab. As mentioned, the Conda package was developed to install OpenSim on Colab. For validation, we compared the GUI results (for Scaling, IK, ID, RRA, SO, and CMC) with OpenColab results. The outcomes matched very well. \n\n𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤𝐬:\nTo download the latest Ipython notebook  (OpenColab.ipynb), please visit Github:\nhttps://github.com/hmok/OpenColab \n\n𝐇𝐨𝐰 𝐭𝐨 𝐬𝐞𝐭 𝐮𝐩 𝐢𝐧 &lt;𝟏𝐦𝐢𝐧 𝐚𝐧𝐝 𝐫𝐮𝐧 𝐢𝐧𝐯𝐞𝐫𝐬𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐧 𝐎𝐩𝐞𝐧𝐂𝐨𝐥𝐚𝐛?\nPlease follow the following steps to start running OpenSim simulations in &lt; 1 min. \nGo to this website: https://colab.research.google.com/\n\na)\tUpload the following file from Supplementary Material 2: “OpenColab.ipynb“ or from this link https://github.com/hmok/OpenColab/blob/main/OpenColab.ipynb\nb)\tWait till the file is loaded. \nc)\tPress Ctrl+F9 or Runtime ---&gt; Run all (setup finished in &lt; 1 min)\nd)\tNo action needed by the user: OpenSim will be installed (5-7 min)\ne)\tThe simulations will generate the results of this paper.\n\n𝐓𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐯𝐢𝐝𝐞𝐨 𝐬𝐡𝐨𝐰𝐬 𝐡𝐨𝐰 𝐭𝐨 𝐝𝐨 𝐭𝐡𝐢𝐬:\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/bDK2hxOHb4g&quot; title=&quot;YouTube video player&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;\n\nIf you are interested in contributing, please feel free to reach out at 𝐦𝐨𝐤𝐡𝐭𝐚𝐫𝐳𝐚𝐝𝐞𝐡 𝐃𝐎𝐓 𝐡𝐨𝐬𝐬𝐞𝐢𝐧 𝐀𝐓 𝐆𝐦𝐚𝐢𝐥 𝐃𝐎𝐓 𝐜𝐨𝐦.\n\nPlease cite the following:\nMokhtarzadeh, Hossein, Fangwei Jiang, Shengzhe Zhao, and Fatemeh Malekipour. 2021. “Opencolab Project: Opensim in Google Colaboratory to Explore Biomechanics on the Web.” engrXiv. September 30. doi:10.31224/osf.io/f8a3h.\n\n&lt;img alt=&quot;&quot; src=&quot;https://github.com/hmok/OpenColab/blob/main/Fig1_6Jun21.png?raw=true&quot;/&gt;\n\n\n\n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Fangwei Jiang,Ayman Habib,Hossein Mokhtarzadeh,Fangwei Jiang","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1964","unix_group_name":"arka-sh3binding","modified":"1595969065","downloads":"0","group_name":"Data for simulations of ArkA peptide binding to Abp1p SH3 domain","logo_file":"","short_description":"Protein-protein interactions are involved in a wide range of cellular processes. These interactions often involve intrinsically disordered proteins (IDPs) and protein binding domains. However, the details of IDP binding pathways are hard to characterize u","long_description":"Protein-protein interactions are involved in a wide range of cellular processes. These interactions often involve intrinsically disordered proteins (IDPs) and protein binding domains. However, the details of IDP binding pathways are hard to characterize using experimental approaches, which can rarely capture intermediate states present at low populations. SH3 domains are common protein interaction domains that typically bind proline-rich disordered segments and are involved in cell signaling, regulation, and assembly. We hypothesized, given the flexibility of SH3 binding peptides, that their binding pathways include multiple steps important for function. Molecular dynamics simulations were used to characterize the steps of binding between the yeast Abp1p SH3 domain (AbpSH3) and a proline-rich IDP, ArkA. Before binding, the N-terminal segment 1 of ArkA is pre-structured and adopts a polyproline II helix, while segment 2 of ArkA (C-terminal) adopts a 310 helix, but is far less structured than segment 1. As segment 2 interacts with AbpSH3, it becomes more structured, but retains flexibility even in the fully engaged state. Binding simulations reveal that ArkA enters a flexible encounter complex before forming the fully engaged bound complex. In the encounter complex, transient nonspecific hydrophobic and long- range electrostatic contacts form between ArkA and the binding surface of SH3. The encounter complex ensemble includes conformations with segment 1 in both the forward and reverse orientation, suggesting that segment 2 may play a role in stabilizing the correct binding orientation. While the encounter complex forms quickly, the slow step of binding is the transition from the disordered encounter ensemble to the fully engaged state. In this transition, ArkA makes specific contacts with AbpSH3 and buries more hydrophobic surface. Simulating the binding between ApbSH3 and ArkA provides insight into the role of encounter complex intermediates and nonnative hydrophobic interactions for other SH3 domains and IDPs in general.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Aurelia Ball","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1965","unix_group_name":"wraptmor","modified":"1622208245","downloads":"0","group_name":"WraptMor","logo_file":"","short_description":"WraptMor is a model that can be used to estimate ligament fiber insertion-to-insertion length with wrapping around osseous surfaces.","long_description":"WraptMor is a model that can be used to estimate ligament fiber insertion-to-insertion length with wrapping around osseous surfaces. The code for the WraptMor model  and examples that demonstrate the model's usage can be found in SourceCode. See the Wiki page for information on the required packages needed to run the WraptMor code, and an example demonstrating the code's usage.\n\nCiting - Please cite this article if you use the WraptMor code or approach:\nZaylor, William, and Halloran, Jason P. (May 6, 2021). &quot;WraptMor: Confirmation of an Approach to Estimate Ligament Fiber Length and Reactions With Knee-Specific Morphology.&quot; ASME. J Biomech Eng. August 2021; 143(8): 081012. https://doi.org/10.1115/1.4050810","has_downloads":false,"keywords":"Ligament length","ontologies":"","projMembers":"Jason Halloran,Will Zaylor","trove_cats":[{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"},{"id":"420","fullname":"Tissue"}],"is_toolkit":true,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1966","unix_group_name":"cmupanopticdata","modified":"1684972800","downloads":"0","group_name":"CMU Panoptic Dataset 2.0","logo_file":"cmupanopticdata","short_description":"This dataset captures 86 subjects performing common tasks of daily living, such as overground and inclined walking, jogging, stair navigation, sit-to-stand, and range of motion activities for major joints.","long_description":"The field of biomechanics is at a turning point, with marker-based motion capture set to be replaced by portable and inexpensive hardware, rapidly improving markerless tracking algorithms, and open datasets that will turn these new technologies into field-wide team projects.  To expedite progress in this direction, we have collected the CMU Panoptic Dataset 2.0, which contains 86 subjects captured with 140 VGA cameras, 31 HD cameras, and 15 IMUs, performing on average 6.5 min of activities, including range of motion activities and tasks of daily living. \n\n\nData: \nThe data are now available under Downloads &gt; Data Share.\n\n\nCode:\nThe Fitting algorithm is published on Github: https://github.com/CMU-MBL/CMU_PanopticDataset_2.0\n\n\n\nCitation:\nIf you find this code or our data useful for your research, please cite the following paper:\n@article{HALILAJ2021110650,\n  title = {American society of biomechanics early career achievement award 2020: Toward portable and modular biomechanics labs: How video and IMU fusion will change gait analysis},\n  journal = {Journal of Biomechanics},\n  volume = {129},\n  pages = {110650},\n  year = {2021},\n  issn = {0021-9290},\n  doi = {https://doi.org/10.1016/j.jbiomech.2021.110650},\n  url = {https://www.sciencedirect.com/science/article/pii/S002192902100419X},\n  author = {Eni Halilaj and Soyong Shin and Eric Rapp and Donglai Xiang},\n  }\n\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Soyong Shin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1971","unix_group_name":"hat1r","modified":"1597426411","downloads":"0","group_name":"Molecular dynamics model of the apo human AT1R","logo_file":"","short_description":"AT1R Model preparation\nThe crystal structure of human AT1R bound to olmesartan (PDB: 4ZUD) was downloaded from the RCSB Protein Data Bank. 4ZUD contains apocytochrome b562RIL fused to the amino terminus, and many of the flexible regions, as well as helix","long_description":"AT1R Model preparation\nThe crystal structure of human AT1R bound to olmesartan (PDB: 4ZUD) was downloaded from the RCSB Protein Data Bank. 4ZUD contains apocytochrome b562RIL fused to the amino terminus, and many of the flexible regions, as well as helix 8, are not resolved. In order to generate an appropriate starting structure, olmesartan and the apocytochrome b562RIL fusion were removed from 4ZUD, and the missing regions were added to the protein with MOE software (Chemical Computing Group ULC, Montreal, Canada). Specifically, the N-Terminus (residues 1 to 25), intracellular loop 2 (residues 134 to 140), extracellular loop 2 (residues 186 to 188), intracellular loop 3 (residues 223 to 234), and helix 8 (residues 305 to 316) were added to the AT1R in accordance to the human AT1R sequence and PDB:4YAY. The remaining carboxyl-tail of the AT1R (residues 317 to 359) was not modeled. The AT1R model then underwent an energy minimization within MOE using the Amber10:Extended Huckel Theory (EHT) force field.\n\nMolecular dynamic (MD) simulations and analysis\nThe MOE minimized AT1R was loaded into CHARMM-GUI. An 80 Å by 80 Å lipid bi-layer composed of 13% cholesterol and 87% Phosphatidylcholine (POPC) was generated around the receptor. Water was packed 17.5 Å above and below the lipid bi-layer, and 150 mM Na+ and Cl- ions were added to the system via Monte-Carlo ion placing. The all-atom CHARMM C36 force field for proteins and ions, and the CHARMM TIP3P force field for water were selected. A hard non-bonded cutoff of 8.0 angstroms was utilized. All molecular dynamics simulations were performed using the PMEMD module of the AMBER16 package with support for MPI multi-process control and GPU acceleration code.  Orthorhombic periodic boundary conditions with a constant pressure of 1 atm was set via the NPT ensemble and temperature was set to 310.15°K (37°C) using Langevin dynamics. The SHAKE algorithm was used to constrain bonds containing hydrogens. The dynamics were propagated using Langevin dynamics with Langevin damping coefficient of 1 ps-1 and a time step of 2 fs. Before the production run, the AT1R model was minimized for 5000 steps using the steepest descent method and then equilibrated for 600 ps. The protein coordinates were saved in 10 ps intervals. The production run lasted 150 ns, at which point all three replicas were stable for at least the last 20 ns.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Bradley Andresen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1975","unix_group_name":"mocaptools","modified":"1598304846","downloads":"0","group_name":"Automated Gap Filling and Tools for Motion Capture","logo_file":"","short_description":"Tools to ease the MoCap analysis, including programmatically running OpenSim, and automatically gap-filling of data.\n\n\nOur team is part of the EPIC lab at Georgia Institute of Technology (www.epic.gatech.edu) focuses on instrumentation with wearable se","long_description":"Tools to ease the MoCap analysis, including programmatically running OpenSim, and automatically gap-filling of data.\n\n\nOur team is part of the EPIC lab at Georgia Institute of Technology (www.epic.gatech.edu) focuses on instrumentation with wearable sensors, including IMU, goniometers, pressure sensors and a novel epidermal flexible emg. We are interested in analyzing the information carried by these sensors to develop intent recognition algorithms and gait state estimation using machine learning techniques. During Fall, we are setting up a full data collection system including motion capture and force plates in our terrain park that includes ramps, stairs and ground level walking. This will allow to study the biomechanics of ambulation for different conditions and get a better background for the development of controllers for our assistive devices.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jonathan Camargo Leyva","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1977","unix_group_name":"syno","modified":"1598845119","downloads":"95","group_name":"Synergy Optimization","logo_file":"syno","short_description":"Synergy Optimization or SynO estimates muscle forces and activations by using the concept of muscle synergy.","long_description":"These MATLAB codes are an implementation of a novel approach for estimating muscle forces/activations by imposing a synergy structure within optimization (termed “synergy optimization,” or SynO). For comparison, users can switch to a MATLAB implementation of a static optimization in the code. Sample experimental overground walking data obtained from the first knee grand challenge competition are also included. For more details about this study and project, read the following paper:\n\nShourijeh, Mohammad S., and Benjamin J. Fregly. &quot;Muscle Synergies Modify Optimization Estimates of Joint Stiffness During Walking.&quot; Journal of Biomechanical Engineering 142.1 (2020).","has_downloads":true,"keywords":"muscle synergies; motor modules; musculoskeletal modeling; static optimization","ontologies":"","projMembers":"Mohammad S. Shourijeh,B.J. Fregly","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"1981","unix_group_name":"yogasana","modified":"1600196692","downloads":"0","group_name":"Yogasana impact on human body","logo_file":"","short_description":"Following project is aimed at studying and quantifying impact of yogasana on different parts of the human body. Yogasana have 3 main benefits: strengthening of muscles, loosening of body joints and increasing control over movements of different body joint","long_description":"Following project is aimed at studying and quantifying impact of yogasana on different parts of the human body. Yogasana have 3 main benefits: strengthening of muscles, loosening of body joints and increasing control over movements of different body joints. Through this, asanas help to not only cure and prevent various abnormalities, but also promote positive health. Through this project, we aim to study the impact a particular asana has on different human body. With a solid knowledge of this, we can recommend what asanas should be practiced by individuals having a particular condition.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ananyo Rao","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1982","unix_group_name":"ssaclmodels","modified":"1618485386","downloads":"0","group_name":"Subject-specific lower limb models to predit ACL dynamics","logo_file":"ssaclmodels","short_description":"This project includes 10 fully subject-specific lower limb musculoskeletal models which were used to predict individualized forces through the ACL during treadmill walking.\n\nThis is freely available data associated with:\nCharles, J. P., Fu, F. H., and Anderst, W. J. (December 10, 2020). \"Predictions of Anterior Cruciate Ligament Dynamics From Subject-Specific Musculoskeletal Models and Dynamic Biplane Radiography.\" ASME. J Biomech Eng. March 2021; 143(3): 031006.\n","long_description":"This project includes 10 fully subject-specific lower limb musculoskeletal models which were used to predict individualized forces through the ACL during treadmill walking. Simulations with these models are informed by motion capture and dynamic bi-plane radiography of the knee joint to fully capture in vivo bone motions and accurately predict ligament dynamics. The accuracy of the predicted forces confirms the validity of this subject-specific modelling framework and the OpenSim platform in predicting ACL dynamics, which could be useful in future studies seeking to use forward simulations to predict post-surgical and/or rehabilitation outcomes","has_downloads":false,"keywords":"","ontologies":"","projMembers":"James Charles","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1984","unix_group_name":"mskkneeforces","modified":"1600796670","downloads":"0","group_name":"Influence of MSK model parameter values on prediction of knee contact forces","logo_file":"","short_description":"Treat­ment de­sign for mus­cu­loskele­tal dis­or­ders us­ing in sil­ico pa­tient-spe­cific dy­namic sim­u­la­tions is be­com­ing a clin­i­cal pos­si­bil­ity. How­ever, these sim­u­la­tions are sen­si­tive to model pa­ra­me­t","long_description":"Treat­ment de­sign for mus­cu­loskele­tal dis­or­ders us­ing in sil­ico pa­tient-spe­cific dy­namic sim­u­la­tions is be­com­ing a clin­i­cal pos­si­bil­ity. How­ever, these sim­u­la­tions are sen­si­tive to model pa­ra­me­ter val­ues that are dif­fi­cult to mea­sure ex­per­i­men­tally, and the influence of un­cer­tain­ties in these pa­ra­me­ter val­ues on the ac­cu­racy of es­ti­mated knee contact forces re­mains un­known. This study eval­u­ates which mus­cu­loskele­tal model para­me­ters have the great­est in­flu­ence on es­ti­mat­ing ac­cu­rate knee con­tact forces during walk­ing. We per­formed the eval­u­a­tion us­ing a two-level op­ti­miza­tion al­go­rithm where mus­cu­loskele­tal model pa­ra­me­ter val­ues were ad­justed in the outer level and mus­cle ac­ti­va­tions were es­ti­mated in the in­ner level. We tested the al­go­rithm with differ­ent sets of de­sign vari­ables (com­bi­na­tions of op­ti­mal mus­cle fiber lengths, ten­don slack lengths, and mus­cle mo­ment arm off­sets) re­sult­ing in nine dif­fer­ent op­ti­miza­tion prob­lems. The most ac­cu­rate lat­eral knee con­tact force pre­dic­tions were ob­tained when ten­don slack lengths and mo­ment arm off­sets were ad­justed si­mul­ta­ne­ously, and the most ac­cu­rate me­dial knee con­tact force es­ti­ma­tions were ob­tained when all three types of pa­ra­me­ters were ad­justed to­gether. In­clu­sion of mo­ment arm off­sets as de­sign vari­ables was more im­por­tant than in­clud­ing ei­ther ten­don slack lengths or op­ti­mal mus­cle fiber lengths alone to ob­tain ac­cu­rate me­dial and lat­eral knee con­tact force predic­tions. These re­sults pro­vide guid­ance on which mus­cu­loskele­tal model pa­ra­me­ter val­ues should be cal­i­brated when seek­ing to pre­dict in vivo knee con­tact forces ac­cu­rately.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Gil Serrancolí","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1986","unix_group_name":"thumb-force","modified":"1697569409","downloads":"0","group_name":"Thumb-Tip Force during Lateral Pinch","logo_file":"thumb-force","short_description":"Large simulated datasets of lateral (key) pinch. The data provided represent shifts in thumb-tip force associated with scaling a musculoskeletal model of the wrist and thumb, as well as isolated adjustments to Hill-type parameters. ","long_description":"This work elucidates the biomechanics of the thumb during lateral (i.e. key) pinch (a clinical outcome measure and activity of daily living) under a variety of conditions. Using a previously published wrist-thumb model (Nichols et al., 2017), we forward dynamically simulated large datasets of lateral pinch. These datasets display the impact of anthropometrically scaling the wrist-thumb model, as well as isolated variations in Hill-type muscle parameters.\n\t\n     We have used the included datasets to determine the efficacy of using anthropometrically scaled generic models to capture age-dependent differences in lateral pinch force. The ages investigated include childhood, puberty, older adolescence, and adulthood. Simulated muscle activations (from computed muscle control) and lateral pinch forces (from forward dynamics) were compared against those from existing literature. While anthropometric scaling could capture variations in lateral pinch force, a generic muscle control strategy is not representative of all populations.  \n\t\n     Motivated by the difficulties of accurate muscle-tendon parameter selection, we also tested the ability of artificial neural networks to classify a Hill-type muscle parameter from lateral pinch force alone. This work used large, dynamic datasets of lateral pinch force to elucidate the impact of altering the maximum isometric force of thumb muscle actuators. The size of the datasets ranged from 120 to 4096 simulations, corresponding to the adjustment of additional thumb muscles. This work demonstrated that artificial neural networks may be an inexpensive approach for approximating Hill-type muscle parameters. We also identified that including muscles with redundant function may decrease machine learning model accuracy.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kalyn Kearney,Maximillian Diaz,Jennifer Nichols,Tamara Ordonez Diaz,Erica Lindbeck","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1988","unix_group_name":"imudeeplearning","modified":"1677620205","downloads":"13","group_name":"IMU Kinematics Using Deep Learning and Top-Down Optimization","logo_file":"imudeeplearning","short_description":"We introduce a new framework that combines deep learning and top-down optimization to predict lower extremity joint kinematics directly from inertial data, without relying on magnetometer readings.","long_description":"The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of motion-tracking sensors in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in out-of-laboratory settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings.\n\n\nData: We uploaded the sample data and code for the demo run. This includes pre-trained models and one-subject sample IMU data for each joint (hip, knee, and ankle) and activity (walking and running). To run the demo code, please follow:\n\n1. Download the demo.zip files in Downloads module and unzip it\n2. Unzip data.zip and models.zip in the directory\n3. Install the required dependencies listed in the requirements.txt file\n4. Run demo.py by specifying your target joint and activity.\n    (e.g., python3 demo.py 'Hip' 'Walking')\n\n\nCode: https://github.com/CMU-MBL/JointAnglePrediction_JOB\n\n\nCitation:  Eric Rapp*, Soyong Shin*, Wolf Thomsen, Reed Ferber, and Eni Halilaj. &quot;Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework.&quot; Journal of Biomechanics 116 (2021): 110229.\n\n* equal contribution","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Soyong Shin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1991","unix_group_name":"runmad","modified":"1604060034","downloads":"57","group_name":"RunMaD: Efficient simulation of 3D musculoskeletal model with implicit dynamics","logo_file":"runmad","short_description":"This project supplements the paper \"Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics\".\n","long_description":"It contains \n1) the supplementary information\n2) the proposed “running model for motions in all directions”, short “runMaD”, which originates from the model of Hamner et al. (2010)\n3) the experimental as well as the simulated data in OpenSim file format.\n\n\n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Marlies Nitschke","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1993","unix_group_name":"emsa","modified":"1601785191","downloads":"0","group_name":"nf1emsa","logo_file":"","short_description":"EMSA ZEB 2 NF1","long_description":"EMSA ZEB 2 NF1","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Aline souza","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1996","unix_group_name":"jipvan","modified":"1601920014","downloads":"0","group_name":"rundfunk","logo_file":"","short_description":"Modification of muscle path nalysis using motion capture data","long_description":"Modification of muscle path nalysis using motion capture data","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jip  van montfort montfort","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"1999","unix_group_name":"ballandsocket","modified":"1602006876","downloads":"0","group_name":"Simple ball and socket model","logo_file":"","short_description":"Creating a simple ball and socket joint model of the shoulder to reverse engineer a more complicated model and provide a baseline for future research.","long_description":"Creating a simple ball and socket joint model of the shoulder to reverse engineer a more complicated model and provide a baseline for future research.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ethan Erusha","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2002","unix_group_name":"histology","modified":"1602280829","downloads":"0","group_name":"Human Knee Cartilage Histopathology Assessment","logo_file":"","short_description":"91 patients with varus knees scheduled for total knee arthroplasty were recruited after getting their informed consent. Inclusion criteria required patients with a diagnosis of idiopathic OA (primarily medial compartment and/or patellofemoral disease) exh","long_description":"91 patients with varus knees scheduled for total knee arthroplasty were recruited after getting their informed consent. Inclusion criteria required patients with a diagnosis of idiopathic OA (primarily medial compartment and/or patellofemoral disease) exhibiting a relatively preserved lateral compartment (JSW: 2–10 mm, median: 6 mm in the lateral compartment) based on preoperative weight-bearing anterior-posterior (AP) radio- graphs taken in full extension and 30° of flexion. During TKA, the Lateral Femoral Condyle (LFC) was collected and the AP orientation was noted. All included LFC specimens presented with Grade 0 , I , or II macroscopic Outerbridge classifications. Two osteochondral specimens (4 × 4 × 8 mm) were obtained from each LFC by placing the condyle in an in-house fabricated miter box in AP orientation and cartilage arches were cut using a razor blade from the weight-bearing center portion of the LFC; 1 was located medial and 1 lateral to the LFC midline. The centers of these two samples were separated by 10 mm.\n\nA total of 182 osteochondral specimens were processed from 91 patients. Immediately after surgical retrieval, specimens were fixed for 48 h at 4 °C. 5µ thick paraffin sections were cut and stained with freshly prepared HE or SafraninO and fast green (SafO). Two adjacent sections per stain were digitally imaged under brightfield at 5x and used for scoring using HHGS and advanced OARSI systems. The HHGS and OARSI scores were provided by 3 reviewers, 3 times (separated by at least 3 months). Unstained sections were digitally imaged under polarized light microscopy (PLM) at 1.25X and scored using PLM scoring system.\n\nIn addition to the digital high quality tiff images of SafO, HE and PLM sections of cartilage and the respective scores, metadata would also include patient's age, gender, surgery-side.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"venkata mantripragada","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2004","unix_group_name":"cslimimages","modified":"1602484465","downloads":"0","group_name":"cSLIM myelin micrographs","logo_file":"","short_description":"Micrographs of piglet brain tissue captured with cSLIM","long_description":"Micrographs of piglet brain tissue captured with cSLIM","has_downloads":false,"keywords":"","ontologies":"","projMembers":"michael fanous","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2009","unix_group_name":"genvssubspec","modified":"1627642356","downloads":"26","group_name":"Generic versus subject-specific models","logo_file":"","short_description":"The aim of this project is to compare results from musculoskeletal simulations using various models ranging from linear scaled to highly subject-specific, i.e., including the participant's musculoskeletal geometry and electromyography data.","long_description":"The aim of this project is to compare results from musculoskeletal simulations using various models ranging from linear scaled to highly subject-specific, i.e., including the participant's musculoskeletal geometry and electromyography data.\n\nMore details can be found in our publication (please also cite our paper if you re-use the model, etc):\n\nKainz H., Wesseling M., Jonkers I. (2021). Generic scaled versus subject-specific models for the calculation of musculoskeletal loading in cerebral palsy gait: Effect of personalized musculoskeletal geometry outweighs the effect of personalized neural control. Clinical Biomechanics, 87, 105402, 1-9. \n\nhttps://doi.org/10.1016/j.clinbiomech.2021.105402 \n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"hans kainz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2011","unix_group_name":"projo","modified":"1603895566","downloads":"0","group_name":"Tranfemoral prosthetic and gait cycle analysis","logo_file":"","short_description":"Analysing solidworks designed limb.","long_description":"Analysing solidworks designed limb.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yaseer Abdullahi","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2013","unix_group_name":"ccivsjointstiff","modified":"1647543077","downloads":"43","group_name":"CCIvsJointStiffness","logo_file":"ccivsjointstiff","short_description":"Compare trends of common muscle co-contraction indices with joint stiffness","long_description":"Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCI’s to approximate lower limb joint stiffness trends during gait.\n\nPlease cite the following paper:\nG. Li, M.S. Shourijeh, D. Ao, C. Patten, B.J. Fregly, How Well Do Commonly Used Co-Contraction Indices Approximate Lower Limb Joint Stiffness Trends during Gait?, Frontiers in Bioengineering and Biotechnology, 2020, DOI: 10.3389/fbioe.2020.588908","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Mohammad S. Shourijeh,B.J. Fregly","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"312","fullname":"Developer Tools"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"}],"is_toolkit":true,"is_model":true,"is_application":true,"is_data":true},{"group_id":"2014","unix_group_name":"renalartery","modified":"1604168134","downloads":"0","group_name":"Renal Artery 1","logo_file":"","short_description":"Renal artery stenosis study","long_description":"Renal artery stenosis study","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Carla Flores ","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2015","unix_group_name":"oi-tfp-model","modified":"1700578793","downloads":"40","group_name":"Musculoskeletal Model of an Osseointegrated Transfemoral Amputee in OpenSim","logo_file":"oi-tfp-model","short_description":"This paper focuses on the development and validation of a generic musculoskeletal model of an osseointegrated transfemoral amputee. The model has been developed using OpenSim with the final goal of obtaining a competent tool to study and better understand","long_description":"This paper focuses on the development and validation of a generic musculoskeletal model of an osseointegrated transfemoral amputee. The model has been developed using OpenSim with the final goal of obtaining a competent tool to study and better understand the biomechanics of osseointegrated transfemoral amputees. The model has been validated on the experimental data obtained on one osseointegrated transfemoral amputee during level ground walking.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Raffaella Carloni,Vishal R","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2016","unix_group_name":"metab-timeprofl","modified":"1604677278","downloads":"0","group_name":"Estimating metabolic cost time profiles","logo_file":"metab-timeprofl","short_description":"This project contains links to experimental data and a manuscript on the comparison of methods for estimating the time profile of metabolic cost within the gait cycle. ","long_description":"Respiratory oxygen consumption measurements allow recording of the average metabolic cost of walking, but the slow rate of these measurements prevents assessing which part of the gait cycle has the highest metabolic cost. Simulation methods allow estimation of the time profile of metabolic cost within the gait cycle. We compared estimations of the metabolic cost of walking using a method that is based on kinematic and electromyography recordings from participants in conjunction with muscle metabolic rate equations and a method based on joint kinematics and kinetics. While both methods are able to accurately estimate large changes in the average metabolic cost from walking on different inclinations, the time profiles estimated by the two methods are different, indicating that estimations of the time profile of metabolic cost are dependent on the estimation method. Both estimation methods matched well with the results from experimental perturbation studies that suggest that the metabolic cost of the swing phase is approximately 10% to 20% of the metabolic cost of the entire gait cycle. \n\nAs a follow-up to this project we aim to develop slightly improved estimations of metabolic time profiles by adjusting the estimations to fit rich experimental datasets. We are using a robotic tether that can assist specific parts of the gait cycle with high repeatability and generate very large changes in metabolic cost. Advances in estimations of the time profile of metabolic cost could lead to practical applications such as designing rehabilitation robots that assist specifically during the phase with the highest metabolic cost.\n\nProject manuscript:\nMohammadzadeh Gonabadi, A., Antonellis, P., &amp; Malcolm, P. (2020). Differences between joint-space and musculoskeletal estimations of metabolic rate time profiles. PLOS Computational Biology, 16(10), e1008280.\nhttps://doi.org/10.1371/journal.pcbi.1008280\n\nSupporting MATLAB data:\nhttps://doi.org/10.1371/journal.pcbi.1008280.s008\n\nThe 3D musculoskeletal model that was used (Rajagopal2015):\nhttps://simtk.org/frs/?group_id=773\n\nThe manuscript describing experimental dataset: \nAntonellis, P., Frederick, C. M., Gonabadi, A. M., &amp; Malcolm, P. (2020). Modular footwear that partially offsets downhill or uphill grades minimizes the metabolic cost of human walking. Royal Society open science, 7(2), 191527.\nhttps://royalsocietypublishing.org/doi/full/10.1098/rsos.191527\n\nThe manuscript describing the robotic system for generating rich perturbation data:\nGonabadi, A. M., Antonellis, P., &amp; Malcolm, P. (2020). A system for simple robotic walking assistance with linear impulses at the center of mass. IEEE Transactions on Neural Systems and Rehabilitation Engineering.\nhttps://ieeexplore.ieee.org/document/9078836","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Arash Mohammadzadeh Gonabadi,philippe malcolm,Prokopios Antonellis","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2019","unix_group_name":"synx","modified":"1671759178","downloads":"64","group_name":"Synergy Extrapolation","logo_file":"synx","short_description":"Synergy extrapolation or SynX uses muscle synergy structure to estimate missing muscle excitations.","long_description":"The experimental data, OpenSim model, and Matlab code used to perform SynX are provided.\n\nFor more details about this study and project, read the following paper:\nD Ao, MS Shourijeh, C Patten, and BJ Fregly, Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies. Frontiers in Computational Neuroscience, DOI: 10.3389/fncom.2020.588943 (2020)","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Mohammad S. Shourijeh,B.J. Fregly,Di Ao","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2022","unix_group_name":"fsi","modified":"1605210012","downloads":"0","group_name":"Heart valve artery","logo_file":"","short_description":"Heart valve artery (fluid-solid inferface)","long_description":"Heart valve artery (fluid-solid inferface)","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ramiro Rebolledo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2025","unix_group_name":"coordretraining","modified":"1657226015","downloads":"91","group_name":"Muscle Coordination Retraining to Reduce Knee Loading","logo_file":"coordretraining","short_description":"Experimental data and simulation-estimated knee contact forces from ten healthy individuals walking on an instrumented treadmill. Participants were provided with real-time biofeedback instructing them to walk with differing gastrocnemius and soleus activation.","long_description":"Experimental data and simulation results from ten healthy individuals walking on an instrumented treadmill. Participants were provided with real-time biofeedback instructing them to walk with greater soleus muscle activation and less gastrocnemius activation, with the goal of reducing knee contact force. A custom static optimization code that incorporated EMG into the muscle redundancy solution was used to estimate the effect of the altered coordination pattern on knee contact force.\n\nThe manuscript using these data and simulations can be found here:\nUhlrich, S.D., Jackson, R.W., Seth, A. Kolesar, J.A., Delp, S.L. Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force. Sci Rep 12, 9842 (2022). https://doi.org/10.1038/s41598-022-13386-9","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Uhlrich","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2026","unix_group_name":"auto-label","modified":"1605731484","downloads":"0","group_name":"Automatic Labelling of Motion Capture Markers","logo_file":"","short_description":"An machine learning-based algorithm and GUI for automatically labelling motion capture markers. The algorithm can be trained on existed motion capture data, or on simulated trajectories that are generated based on an OpenSim marker set and the body kinema","long_description":"An machine learning-based algorithm and GUI for automatically labelling motion capture markers. The algorithm can be trained on existed motion capture data, or on simulated trajectories that are generated based on an OpenSim marker set and the body kinematics of a set of 100 participants performing athletic movements.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Allison Clouthier","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2032","unix_group_name":"dogmodel","modified":"1649074287","downloads":"677","group_name":"A three-dimensional musculoskeletal model of the dog","logo_file":"dogmodel","short_description":"We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a beagle at walk.","long_description":"The domestic dog is interesting to investigate because of their wide range of body size, body mass, and physique. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. Collecting this data through in vivo measurements/records of joint forces and loads on deep/small muscles is complex, invasive, and sometimes unethical. The use of detailed musculoskeletal models may help fill the knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a beagle at walk. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. The activation patterns predicted from the model exhibit good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence neuronal control and joint loading in dog locomotion.","has_downloads":true,"keywords":"Forelimb,Dog (canis lupus familiaris),Animal model,Musculoskeletal simulation,Dynamic simulation,Hindlimb,Hierarchical clustering of muscle activation","ontologies":"Multibody_Dynamics,Modeling_and_Simulation,Mechanical_Simulation,Dynamic_Model","projMembers":"Heiko Stark","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"2033","unix_group_name":"metatarsals","modified":"1606765334","downloads":"0","group_name":"Bone Stress Injury","logo_file":"","short_description":"Understanding bone stress injury - effect of cyclic loading magnitude and direction: \n\n· Design fixturing and test protocols to compare the effect of bending vs axial loading on whole bone fatigue failure / damage accumulation.\n·  Use imaging and eng","long_description":"Understanding bone stress injury - effect of cyclic loading magnitude and direction: \n\n· Design fixturing and test protocols to compare the effect of bending vs axial loading on whole bone fatigue failure / damage accumulation.\n·  Use imaging and engineering methods to calculate and then assign stress and strain for each specimen.\n· Perform fatigue testing on avian bone specimens loaded in different directions (axial vs. axial+bending).","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sofia Orrico","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2034","unix_group_name":"exohri","modified":"1607598075","downloads":"0","group_name":"Human Robot Interaction in Assistive Exoskeleton","logo_file":"","short_description":"Investigating human-robot interaction in assistive exoskeletons using the Matlab API for OpenSim","long_description":"Investigating human-robot interaction in assistive exoskeletons using the Matlab API for OpenSim.\n\nStage 1 - Adding Exo Geometry to Model\nGetting Solidworks files in the correct format\nhttp://cbee.oregonstate.edu/sites/cbee.oregonstate.edu/files/sites/cbee.oregonstate.edu/Documents/StudentProjects/2015/expoposter_artificalnetworkfortendontransfersurgery_varin.pdf\n\nImporting of STL files into Matlab environment to aid the building of the model.\nhttps://simtk.org/plugins/phpBB/viewtopicPhpbb.php?f=91&amp;t=12690&amp;p=0&amp;start=0&amp;view=&amp;sid=4ca249cdfeb6264152207c26539185cb\n\nStage 2\nBuild the exoskeleton model model\n\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Rory Turnbull","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2035","unix_group_name":"simlabmee230","modified":"1606887946","downloads":"0","group_name":"Simulation Lab MEE 230","logo_file":"","short_description":"Simulation Lab for Biomechanics","long_description":"Simulation Lab for Biomechanics","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Allison Coburn","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2037","unix_group_name":"finalproj","modified":"1607110139","downloads":"0","group_name":"Biomechanics Final","logo_file":"","short_description":"Biomechanics Final","long_description":"Biomechanics Final","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Abigail Teixeira","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2042","unix_group_name":"vp-integration","modified":"1613255640","downloads":"0","group_name":"Integration subgroup - Multiscale Modeling and Viral Pandemics","logo_file":"","short_description":"This page is for the Integration subgroup for the <a href=\"https://www.imagwiki.nibib.nih.gov/working-groups/multiscale-modeling-and-viral-pandemics\" target=\"_blank\" > Multiscale Modeling and Viral Pandemics working group</a> within MSM/IMAG community","long_description":"This group deals with integration and includes:\n- Integration Between Within-host and Population Scales\n- Integration Within and Across Scales\n- Ensemble modeling ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yaling Liu,Robin Thompson,Ruth Bowness,Jacob Barhak","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2044","unix_group_name":"fbmodpassivecal","modified":"1669849496","downloads":"266","group_name":"Full-body musculoskeletal model with calibrated passive muscle forces","logo_file":"","short_description":"A full-body musculoskeletal model based on the model described by Rajagopal et al. (2016) with calibrated passive muscle force curves and improved hip abductor muscle paths.","long_description":"A full-body musculoskeletal model based on the model described by Rajagopal et al. (2016) with calibrated passive muscle force curves and improved hip abductor muscle paths. Changes to the Rajagopal model are described in our manuscript Uhlrich et al. (2022). The code for calibrating the forces is here: https://github.com/stanfordnmbl/PassiveMuscleForceCalibration.\n\nPassive muscle force curves were calibrated to match experimental data published by Silder et al. (2007). Hip abductor muscle paths were adapted to more closely align with MRI and experimental data.\n\nRajagopal A, et al. &quot;Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait.&quot; IEEE Transactions on Biomedical Engineering 63.10 (2016): 2068-2079. (2016)\n\nUhlrich SD, et al. Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force. Sci Rep 12, 9842. (2022) https://doi.org/10.1038/s41598-022-13386-9\n\nSilder A, et al. &quot;Identification of Passive Elastic Joint Moment-Angle Relationships in the Lower Extremity.&quot; Journal of Biomechanics 40.12: 2628-2635. (2007)","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Uhlrich","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2045","unix_group_name":"vpcomorbidities","modified":"1612991054","downloads":"0","group_name":"Comorbidities subgroup - Multiscale Modeling and Viral Pandemics","logo_file":"","short_description":"This page is for the comorbidities subgroup for the <a href=\"https://www.imagwiki.nibib.nih.gov/working-groups/multiscale-modeling-and-viral-pandemics\" target=\"_blank\" > Multiscale Modeling and Viral Pandemics working group</a> within MSM/IMAG community.","long_description":"This group deals with comorbidities such as:\n- Diabetes\n- Obesity\n- Immune suppression","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jacob Barhak,Gilberto Gonzalez-Parra","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2046","unix_group_name":"vp-reproduce","modified":"1608440938","downloads":"0","group_name":"Model Reproducibility, Credibility and Standardization Subgroup - MSMVP","logo_file":"","short_description":"This page is for the Model Reproducibility, Credibility and Standardization subgroup for the Multiscale Modeling and Viral Pandemics working group within MSM/IMAG community.\n","long_description":"This group deals with Model Reproducibility, Credibility and Standardization and includes:\n- Repeatability and Reproducibility of models in publications and repositories\n- Credibility of models\n- Standardization efforts &amp; specifications for:  \n&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 1) Models and modeling technologies.  \n&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 2) Modeling data\n- Model annotation ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jacob Barhak,Rahuman  Sheriff","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2052","unix_group_name":"predictkam","modified":"1610502661","downloads":"28","group_name":"A neural network to predict the knee adduction moment with anatomical landmarks","logo_file":"","short_description":"The dataset to train a neural network to predict the knee adduction moment using the positions of anatomical landmarks only.","long_description":"The dataset to train a neural network to predict the knee adduction moment using the positions of anatomical landmarks only. Download this dataset and train models in the following github repository: https://github.com/stanfordnmbl/predictKAM\n\nDetails about the dataset on github as well as in Boswell and Uhlrich, et al., 2021. ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Scott Uhlrich,Melissa Boswell","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2054","unix_group_name":"acl","modified":"1610651962","downloads":"0","group_name":"ACL Injury","logo_file":"","short_description":"To look into preventative measures for ACL injury","long_description":"To look into preventative measures for ACL injury","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Branden Perry","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2055","unix_group_name":"marker_reg","modified":"1619726847","downloads":"85","group_name":"Marker registration method informed by anatomical reference frame orientations","logo_file":"","short_description":"A systematic method for marker registration, informed by anatomical reference frame orientations, reduces subjective user input and can make marker registration more accurate and repeatable","long_description":"Accurate computation of joint angles from optical marker data using inverse kinematics methods requires that the locations of markers on a model match the locations of experimental markers on participants. Marker registration is the process of positioning the model markers so that they match the locations of the experimental markers. Markers are typically registered using a graphical user interface (GUI), but this method is subjective and may introduce errors and uncertainty to the calculated joint angles and moments. In this investigation, we use OpenSim to isolate and quantify marker registration–based error from other sources of error by analyzing the gait of a bipedal humanoid robot for which segment geometry, mass properties, and joint angles are known. We then propose a marker registration method that is informed by the orientation of anatomical reference frames derived from surface-mounted optical markers as an alternative to user registration using a GUI. The proposed orientation registration method reduced errors in joint angles and moments compared to the user registration method, and eliminated variability among users. Our results show that a systematic method for marker registration that reduces subjective user input can make marker registration more accurate and repeatable.","has_downloads":true,"keywords":"OpenSim,biomechanics,inverse kinematics,motion capture,musculoskeletal model,registration","ontologies":"Modeling_and_Simulation","projMembers":"Thomas Uchida,jimmy d,Thor Besier,Scott Delp,Ajay Seth","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"},{"id":"1001","fullname":"OpenSim"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2057","unix_group_name":"cfd_in_chd","modified":"1611000211","downloads":"0","group_name":"Investigation into the use CFD in CHD - Focus on ToF and CoA","logo_file":"","short_description":"Modelling geometries for Tetralogy of Fallot and Coarctation of the Aorta to assess the possible benefits of CFD in CHD.","long_description":"Modelling geometries for Tetralogy of Fallot and Coarctation of the Aorta to assess the possible benefits of CFD in CHD.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"doireann shaffrey","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2058","unix_group_name":"pedaling_dc","modified":"1611127233","downloads":"0","group_name":"Direct collocation framework to simulate pedaling in OpenSim","logo_file":"","short_description":"This project aims to develop a direct collocation (DC) framework to solve optimal control pedaling problem within the OpenSim modeling environment. This page includes the OpenSim model, m files, and experimental data that were used in the paper titled as ","long_description":"This project aims to develop a direct collocation (DC) framework to solve optimal control pedaling problem within the OpenSim modeling environment. This page includes the OpenSim model, m files, and experimental data that were used in the paper titled as 'A direct collocation framework for optimal control simulation of pedaling using OpenSim'. The optimal results at 31 nodes shown in the paper can be reproduced using the example code.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sangsoo Park","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2065","unix_group_name":"real_time","modified":"1615001449","downloads":"0","group_name":"Real-time kinematics and dynamics analysis using marker- and IMU-based solutions","logo_file":"real_time","short_description":"Real-time musculoskeletal kinematics and dynamics analysis using marker- and IMU-based solutions in rehabilitation","long_description":"&lt;p align=&quot;center&quot;&gt;&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://mitkof6.gitlab.io/personal-site/publications/sensors2021/real_time_framework_video.mp4&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;&lt;/p&gt;\n\n\nThis study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim's offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.\n\n\nThe source code of the project can be found at https://github.com/mitkof6/OpenSimRT","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dimitar Stanev,Konstantinos Filip,Konstantinos Moustakas,George Giarmatzis,Dimitrios Tsaopoulos","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2074","unix_group_name":"exscjumphigher","modified":"1612559910","downloads":"0","group_name":"EXSC471LAB","logo_file":"","short_description":"Jump Higher Labe","long_description":"Jump Higher Labe","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Chase Capiti","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2075","unix_group_name":"jumplab","modified":"1612562344","downloads":"0","group_name":"Jump Lab","logo_file":"","short_description":"This project is a biomechanical model for a jump squat. By applying biomechanical principles, a jump squat can be observed.","long_description":"This project is a biomechanical model for a jump squat. By applying biomechanical principles, a jump squat can be observed.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Anthony Vargas","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2076","unix_group_name":"imag_evolution","modified":"1612745318","downloads":"0","group_name":"IMAG Working Group: Evolution Subgroup","logo_file":"","short_description":"We are developing models of viral evolution that will help us prepare for the next pandemic.  These models will integrate experimental data on the phenotypes of viral variants with physiological knowledge of how viruses interact with host cells and the im","long_description":"We are developing models of viral evolution that will help us prepare for the next pandemic.  These models will integrate experimental data on the phenotypes of viral variants with physiological knowledge of how viruses interact with host cells and the immune system.  Mathematical models will integrate these predictions with evolutionary theory, including the roles of drift and migration, to forecast how viral evolution will affect the course of symptoms, treatment and vaccination.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Frederick Adler","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2077","unix_group_name":"hw1","modified":"1612745419","downloads":"0","group_name":"tutorial1","logo_file":"","short_description":"Homework for biomechanics","long_description":"Homework for biomechanics","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ushna Usman","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2078","unix_group_name":"ymantra","modified":"1612817982","downloads":"0","group_name":"ptMantra","logo_file":"","short_description":"We are developing a motion capture solution for remote physiotherapy. Before doing an exercise, this solution provides patients with rotatable 3D models of the ideal movement in addition to normal video to help them understand proper form (enhanced educat","long_description":"We are developing a motion capture solution for remote physiotherapy. Before doing an exercise, this solution provides patients with rotatable 3D models of the ideal movement in addition to normal video to help them understand proper form (enhanced education). After doing an exercise, this solution provides therapists with rotatable 3D models of the observed movement in addition to normal video to help them understand the patient's form (enhanced examination).","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mikhail Lenko","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2079","unix_group_name":"sphinxsys","modified":"1623417341","downloads":"487","group_name":"SPHinXsys: a multi-physics library based on SPH method","logo_file":"sphinxsys","short_description":"SPHinXsys (pronunciation: s'finksis)  provides C++ APIs for physical accurate simulation and aims to model coupled industrial dynamic systems including fluid, solid, multi-body dynamics (with Simbody) and beyond with SPH (smoothed particle hydrodynamics), a meshless computational method using particle discretization.","long_description":"SPHinXsys (pronunciation: s'finksis) is an acronym from Smoothed Particle Hydrodynamics for industrial compleX systems. It provides C++ APIs for physical accurate simulation and aims to model coupled industrial dynamic systems including fluid, solid, multi-body dynamics and beyond with SPH (smoothed particle hydrodynamics), a meshless computational method using particle discretization.\n\nIncluded physics\nFluid dynamics, solid dynamics, fluid-structure interactions (FSI), and their coupling to multi-body dynamics (with SIMBody library)\n\nSPH method and algorithms\nSPH is a fully Lagrangian particle method, in which the continuum media is discretized into Lagrangian particles and the mechanics is approximated as the interaction between them with the help of a kernel, usually a Gaussian-like function. SPH is a mesh free method, which does not require a mesh to define the neighboring configuration of particles, but construct of update it according to the distance between particles. A remarkable feature of this method is that its computational algorithm involves a large number of common abstractions which link to many physical systems inherently. Due to such unique feature, SPH have been used here for unified modeling of both fluid and solid mechanics.\n\nThe SPH algorithms are based on the published work of the authors. The algorithms for the discretization of the fluid dynamics equations are based on a weakly compressible fluid formulation, which is suitable for the problems with incompressible flows, and compressible flows with low Mach number (less than 0.3). The solid dynamics equations are discretized by a total Lagrangian formulation, which is suitable to study the problems involving linear and non-linear elastic materials. The FSI coupling algorithm is implemented in a kinematic-force fashion, in which the solid structure surface describes the phase-interface and, at the same time, experiences the surface forces imposed by the fluid pressure and friction.\n\nGeometric models\n2D models can be built using basic shapes (polygon and circle) and full version of binary operations. 3D models can be generated by simple shapes (brick and sphere), imported from external STL files and processed by applying simple binary operations, e.g. add and substract.\nMaterial models\nNewtonian fluids with isothermal linear equation of state. Non-newtonian fluids with Oldroyd-B model. Linear elastic solid, non-linear elastic solid with Neo-Hookian model and anisotropic muscle model.\n\nMulti-resolution modeling\nUniform resolution is used within each fluid or solid bodies. However, it is allowed to use different resolutions for different bodies. For example, one is able to using higher resolution for a solid body which is interacting with a fluid body with lower resolution.\n\nParallel Computing\nIntel Threading Building Blocks (TBB) is used for the multi-core parallelism.\n\nAuthors\nXiangyu Hu, Luhui Han, Chi Zhang, Shuoguo Zhang, Massoud Rezavand, Yongchuan Yu\n\nProject Principle Investigator\nXiangyu Hu (xiangyu.hu@tum.de), Department of Mechanical Engineering, Technical University of Munich\n\nAcknowledgements\nGerman Research Foundation (Deutsche Forschungsgemeinschaft) DFG HU1527/6-1, HU1527/10-1 and HU1527/12-1.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Xiangyu Hu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2080","unix_group_name":"bone_deformity","modified":"1707350400","downloads":"148","group_name":"Bone deformation tool for OpenSim models","logo_file":"bone_deformity","short_description":"This project page is used for sharing a MATLAB toolbox that enables researcher in musculoskeletal models to modify generic musculoskeletal models by applying arbitrary rotational/torsional profiles to the long axis of the bone model. The bone-deformation toolbox works for OpenSim v4.1 and following (although compatibility with OpenSim v3.3 is also maintained). A package to reproduce all results and figures presented in the associated publication is also shared.\n\nLuca gave a talk at the 26th Congress of the European Society of Biomechanics presenting this work and the bone deformation tool. The recorded presentation is available at the following link:\n\n&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/jq2S2tRGsm0&quot; title=&quot;YouTube video player&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;","long_description":"Using the current version of the bone deformation toolbox is possible to apply the following personalization to generic OpenSim models, such as gait2392 or the Rajagopal model:\n&lt;ul&gt;\n  &lt;li&gt;modifying a femoral version angle leaving the kinematic and kinetic model unaltered.&lt;/li&gt;\n  &lt;li&gt;applying a torsional profile to the femur that modifies the knee joint alignment&lt;/li&gt;\n  &lt;li&gt;applying a torsional profile to the tibia that modifies the ankle joint alignment&lt;/li&gt;\n&lt;/ul&gt;\nThe tool is shared together with the scripts required to reproduce the results and figures of the associated publication. The following links are also related:\n&lt;ul&gt;\n&lt;li&gt; detailed documentation on how to use the package downloadable from this page is available at: https://github.com/modenaxe/femoral-anteversion-paper.\n\n&lt;li&gt; The bone-deformation tool is openly developed at https://github.com/modenaxe/msk-bone-deformation\n\n&lt;li&gt; the publication associated with this project is open access and available at https://doi.org/10.1016/j.gaitpost.2021.06.014\n&lt;/ul&gt;\n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Luca Modenese,Martina Barzan","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2081","unix_group_name":"sist","modified":"1613069753","downloads":"0","group_name":"Sit to stand model","logo_file":"","short_description":"Sit to stand model comprising of a lower limb model\nUsed to correlate electrical stimulation to force generated and movement","long_description":"Sit to stand model comprising of a lower limb model\nUsed to correlate electrical stimulation to force generated and movement","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Alex Noakes","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2083","unix_group_name":"insilico_ladder","modified":"1713455528","downloads":"0","group_name":"In-silico LADDER: Lung Aerosol Dosimetry for Drug and Environmental Research","logo_file":"","short_description":"The in-silico LADDER project aims at developing aerosol dosimetry multiscale models through a  modular integration of 3D computational fluid particle dynamic CT-based lung models of the upper and large airways with each most distal 3D airway bi-directionally coupled with lower dimensional airflow, aerosol transport, and tissue mechanics models.","long_description":"The development of predictive aerosol dosimetry models has been a major focus of environmental toxicology and pharmaceutical health research for decades. Simplified compartmental and one-dimensional models have been successful in predicting overall deposition but fail to accurately predict local deposition. Computational fluid-particle dynamics (CFPD) has been extensively used to study flow patterns and aerosol transport in idealistic, physiologically realistic and more recently patient-specific models of lung airways. To date, the challenge of predicting the deposition of inhaled aerosols under disease conditions is largely unmet. While 3D CFPD models provide the capability of including subject-specific lung abnormalities resulting from respiratory diseases, they typically only include a sub-region of the lung because of the prohibitive computational costs compared to simplified models. \n\nThe in-silico LADDER project aims at developing aerosol dosimetry multiscale models through a step-wise, modular integration of 3D computational fluid dynamic airflow and aerosol tracking CT-based lung models that extend from the nose and mouth to several generations of the conducting airways with each most distal 3D pulmonary airway bi-directionally coupled with lower dimensional airflow, aerosol transport, and tissue mechanics models to describe aerosol transport and deposition over the full respiratory system. The expected deliverables will be a suite of modular, multiscale models and standardized approaches for new model development that can be used by researchers, risk assessors, or clinicians to predict aerosol deposition in the lungs under healthy and disease conditions in addition to the underlying algorithms and framework for effective linking of user-defined, personalized aerosol dosimetry models in the future. As modules are fully validated and published, they will be made available to the research community on this platform. \n\nA repository of geometrical models of lung airways developed and used by the in-silico LADDER team of investigators is also available under the Documents tab (Lung geometries database folder).\n\nThis project is funded by grant U01-ES028669 from the National Institutes of Health (NIH). The curation of lung models and meshes is funded by the Environmental Protection Agency (EPA) through service agreement 68HE0B22P0365.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Richard Corley,bahman Asgharian,Azadeh Akhavan Taheri Borojeni,Sean Colby,Rajesh Singh,Andrew Kuprat,Chantal Darquenne,Owen Price,Wanjun Gu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2084","unix_group_name":"torsiontool","modified":"1709799649","downloads":"164","group_name":"Torsion Tool","logo_file":"torsiontool","short_description":"This Matlab tool can be used to generate OpenSim models with personalised femoral neck-shaft and anteversion angles, as well as tibial torsion angles.","long_description":"This Matlab tool can be used to generate OpenSim musculoskeletal models with personalised femoral neck-shaft and anteversion angles, as well as tibial torsion angles. \n\nMore info about the tool can be found in the paper published in Journal of Biomechanics: https://doi.org/10.1016/j.jbiomech.2021.110589. \n&quot;Torsion Tool: An automated tool for personalising femoral and tibial geometries in OpenSim musculoskeletal models&quot; by Kirsten Veerkamp, Hans Kainz, Bryce A. Killen, Hulda Jónasdóttir, Marjolein M. van der Krogt\n\nThe tool has been updated and should now be compatible with most OpenSim models. If you use the updated tool, please cite the following additional paper alongside the one mentioned above.\nhttps://doi.org/10.1038/s41598-024-53857-9\n&quot;A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining&quot; by Hans Kainz, Willi Koller, Elias Wallnöfer, Till R Bader, Gabriel T Mindler, Andreas Kranzl\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Marjolein van der Krogt,Willi Koller,Elias Wallnöfer,Bryce Killen,Hulda Jónasdóttir,Kirsten Veerkamp,hans kainz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2085","unix_group_name":"ski-analysis","modified":"1613614888","downloads":"0","group_name":"Ski Analysis","logo_file":"","short_description":"differences between retraction and extension turns\n- joint angles\n- muscle stretch shortening cycle- lengths for each turn","long_description":"differences between retraction and extension turns\n- joint angles\n- muscle stretch shortening cycle- lengths for each turn","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ashley Lyons","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2087","unix_group_name":"imukinetics","modified":"1689811200","downloads":"67","group_name":"IMU and Smartphone Camera Fusion for Estimation of Kinetic Outcomes","logo_file":"imukinetics","short_description":"This project provides modular deep learning models that estimate knee joint moments from inertial measurement units, smartphone cameras, or both.","long_description":"\nWearable sensing and computer vision could move biomechanics from specialized laboratories to natural environments, but better algorithms are needed to extract meaningful outcomes from these emerging modalities. We present here new models for estimating the knee adduction moment (KAM) and knee flexion moment (KFM) from  smartphone cameras and wearable inertial measurement units (IMUs).\n\nCODE: https://github.com/TheOne-1/KAM_and_KFM_Estimation\n\nDATA: The data of this project are stored in &quot;all_17_subjects.h5&quot;. Each subject's data are in a 3-dimensional matrix. The first dimension is for walking steps. The second dimension is for samples (collected at 100 Hz) from heel-strike - 20 samples to toe-off + 20 samples. Both heel-strike and toe-off are detected using right foot IMU data. The length of the second dimension is 152, which is the length of the longest step. Zeros were appended in the end of shorter steps. The third dimension is for 256 data fields, whose name is stored as an attribute named &quot;columns&quot; in the h5 file. An example Python script is provided for loading the data.\n\nTo cite this work:\n@article{tan2022imu,\n       title={IMU and Smartphone Camera Fusion for Knee Adduction and Knee Flexion Moment Estimation During Walking},\n       author={Tan, Tian and Wang, Dianxin and Shull, Peter B and Halilaj, Eni},\n       journal={IEEE Transactions on Industrial Informatics}, \n       year={2022},\n}\n\nT. Tan, D. Wang, P. B. Shull and E. Halilaj, &quot;IMU and Smartphone Camera Fusion for Knee Adduction and Knee Flexion Moment Estimation During Walking,&quot; in IEEE Transactions on Industrial Informatics, 2022, doi: 10.1109/TII.2022.3189648.\n\nLink to paper: https://ieeexplore.ieee.org/abstract/document/9826418","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Alan Tan","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2088","unix_group_name":"hmie","modified":"1613866398","downloads":"0","group_name":"HMIE","logo_file":"","short_description":"studies of posture to led recommendations for establishing safe working conditions to reduce the risk of musculoskeletal injuries","long_description":"studies of posture to led recommendations for establishing safe working conditions to reduce the risk of musculoskeletal injuries","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Maria Accini","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2089","unix_group_name":"twa-bvr","modified":"1614026657","downloads":"0","group_name":"Total Wrist Arthroplasty Biomechanics","logo_file":"twa-bvr","short_description":"<div align=\"justify\">This database will include biplane videoradiographs (BVR), implant models, and contact modeling of the total wrist arthroplasty (TWA) components along with visualization codes.</div>","long_description":"Total Wrist Arthroplasty (TWA) biomechanics was assessed using a biplane videoradiography (BVR) system at the &lt;a href=&quot;https://www.xromm.org/&quot;&gt;XROMM facility&lt;/a&gt;, at Brown University.\n\nThis database will include:\n1) Videoradiographs from 2 X-ray Sources\n2) Tracked Implants in Radiographs\n3) Matlab Codes for Processing the Tracked Data\n4) Contact Calculation for Implants\n\n","has_downloads":false,"keywords":"wrist,dataset,biplanar videoradiography,total wrist arthroplasty","ontologies":"","projMembers":"Bardiya Akhbari","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"2090","unix_group_name":"adr","modified":"1614241383","downloads":"0","group_name":"ADR PROJECT-1","logo_file":"adr","short_description":"Learning the mechanism of Adverse Drug Reactions through computational simulation ","long_description":"The project is initiated for Final year students of Doctor of Pharmacy in the discipline of Applied Clinical Pharmacy. The objectives are to help the students in regard to Adverse drug reactions  observed during their clinical clerkship at various hospitals in Pakistan. The students will learn and utilize Advanced Simulation Software, Tools, Apps and Packages. This research based project may play significant role in identification, targeting and control of Adverse drug reactions associated with pharmacotherapeutic agents, prior to prescription or post prescribed medication ( Forensic studies). The project needs Bioengineered simulation tools and software packages for the possible prediction of ADR that can produce 3D modeling of the invitro biological effects of drugs and medicines. This may have the advantage for safe use of medication in Clinical Pharmacy.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Anwar Ul Haq","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2092","unix_group_name":"footankle_model","modified":"1661123106","downloads":"5825","group_name":"Multi-segment Foot and ankle model validated using biplanar videoradiography","logo_file":"footankle_model","short_description":"A multi-segment foot and ankle model consisting of the tibia, talus, calcaneus, midfoot, forefoot and toes, with a total of 7 degrees of freedom. Motion between segments were constrained such that all joints except for the ankle were modelled as a revolut","long_description":"The kinematic outputs were validated using biplanar videoradiography in seven healthy participants during walking and running. ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jayishni Maharaj","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2094","unix_group_name":"invdyn","modified":"1614371219","downloads":"0","group_name":"Inverse Dynamics Project","logo_file":"","short_description":"Solve for joint torques about hip, knee, and ankle","long_description":"Solve for joint torques about hip, knee, and ankle","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mackenzie Mattone","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2096","unix_group_name":"ensemble","modified":"1615313056","downloads":"0","group_name":"Ensembling Improves Protein Contact Prediction","logo_file":"","short_description":"Long Term Accessible data for preprint: DOI: 10.22541/au.160317361.15075213/v1","long_description":"The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, can increase the accuracy of protein contact predictions by combining the outputs of different neural network models.  We show that ensembling the predictions made by different groups at the recent Critical Assessment of Protein Structure Prediction (CASP13) outperforms all individual groups. Further, we show that contacts derived from the distance predictions of three additional deep neural networks – AlphaFold, trRosetta, and ProSPr – can be substantially improved by ensembling all three networks. We also show that ensembling these recent deep neural networks with the best CASP13 group creates a superior contact prediction tool. Finally, we demonstrate that two ensembled networks can successfully differentiate between the folds of two highly homologous sequences. In order to build further on these findings, we propose the creation of a better protein contact benchmark set and additional open-source contact prediction methods.  ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dennis Della Corte","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2097","unix_group_name":"elderly_fall","modified":"1614899848","downloads":"0","group_name":"Study of elder fall","logo_file":"","short_description":"the study of the movement and behaviour of the human on a falling trajectory","long_description":"the study of the movement and behaviour of the human on a falling trajectory","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mário Luís Escobar Gonzalez","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2098","unix_group_name":"bl-1d","modified":"1614928324","downloads":"0","group_name":"intro","logo_file":"","short_description":"1d blood flow","long_description":"1d blood flow","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Avradip Ghosh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2102","unix_group_name":"ass3neuro","modified":"1615401847","downloads":"0","group_name":"Ass3 Neuromechanics","logo_file":"","short_description":"SCONE will be used for assignment 3 of the neuromechanics course at the tu delft. As TA, I need to assess the students working with SCONE.","long_description":"SCONE will be used for assignment 3 of the neuromechanics course at the tu delft. As TA, I need to assess the students working with SCONE.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thom van Rooijen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2103","unix_group_name":"anatomicalknee","modified":"1616464525","downloads":"85","group_name":"Anatomical Knee","logo_file":"anatomicalknee","short_description":"This project provides the community with a statistical shape model (SSM) of the knee, code for generating the SSM, and the training data of knee geometries. \n\nCurrently, the structures that are represented include the femur, tibia, patella, femoral cart","long_description":"This project provides the community with a statistical shape model (SSM) of the knee, code for generating the SSM, and the training data of knee geometries. \n\nCurrently, the structures that are represented include the femur, tibia, patella, femoral cartilage, tibial cartilages, and patellar cartilage. \n\nThe bones are represented as point distribution models (PDMs) and the cartilage as scalar fields of the cartilage thickness. This allows for more general use as the point clouds can be easily meshed to obtain the desired mesh topology. \n\nThe long term goal of this project is to characterise and embed as many of the knee structures as possible to allow for the creation of biomechanics models.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Marco Schneider","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2108","unix_group_name":"handloadinterac","modified":"1701128533","downloads":"351","group_name":"Enhancing Accuracy and Reliability of Spinal Load Estimation in Lifting/Lowering","logo_file":"handloadinterac","short_description":"This project involves two studies:\n\n1) We compared five approaches to model external hand forces and moments (EHF&amp;M) in dynamic two-handed lifting tasks using the Lifting Full-Body model.\n\n2) We improved the Fully Articulated Thoracolumbar Spine model and validated it during 9 dynamic lifting/lowering tasks using the five EHF&amp;M modeling approaches .","long_description":"1) For details on the first study (EHF&amp;M modeling approaches), please refer to our paper:\nAkhavanfar, M., Uchida, T. K., Clouthier, A. L., &amp; Graham, R. B. (2022). Sharing the Load: Modeling Loads in OpenSim to Simulate Two-Handed Lifting. Multibody System Dynamics, 54(2), 213–234. [DOI: 10.1007/s11044-021-09808-7]\n\nThe goal of this study was to compare five modeling approaches for simulating the interaction between external loads and hands. These introduced modeling approaches possess varying complexities and are tested for various two-handed lifting tasks. The accuracy of each approach is assessed by comparing the resulting residual forces and moments. You can download sample model files and data to evaluate the EHF&amp;M approaches from previous releases in the Downloads section.\n\n\n2) For information about the second study (validating spinal forces estimated by our new model), please refer to our paper:\nAkhavanfar, M., Mir-Orefice, A., Uchida, T. K., &amp; Graham, R. B. (2023). An Enhanced Spine Model Validated for Simulating Dynamic Lifting Tasks in OpenSim. Annals of Biomedical Engineering. [DOI: 10.1007/s10439-023-03368-x]\n\nIn this study, we developed a new spine model and validated the intervertebral spinal forces estimated by our model during a variety of dynamic lifting/lowering tasks. We also investigated which EHF&amp;M modeling approach resulted in the most accurate spinal load estimates. This new release includes the newly developed spine model for OpenSim, along with sample setup files and MATLAB scripts designed to automatically generate the necessary models and motion files required for kinematic and dynamic analysis of EHF&amp;M Approaches 3–5. Please carefully read the release notes and the README.pdf in the &quot;NewFATLSModelValidation.zip&quot; folder.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Thomas Uchida,Allison Clouthier,Alexandre Mir-Orefice,Ryan Graham,Mohammadhossein Akhavanfar","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2110","unix_group_name":"me563","modified":"1617461159","downloads":"0","group_name":"ME563 - Spring 2021","logo_file":"","short_description":"Biomechanics of knee and ankle - lacrosse move","long_description":"Biomechanics of knee and ankle - lacrosse move","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Holly Berns","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2111","unix_group_name":"coupled-exo-sim","modified":"1665420091","downloads":"1085","group_name":"Simulations of Walking with Coupled Exoskeleton Assistance","logo_file":"coupled-exo-sim","short_description":"Simulations of exoskeleton devices, where torque controls have the same timing between joints, or &quot;coupled&quot; control.","long_description":"In this study, we simulated exoskeleton devices that used one optimized control signal to provide torque assistance at multiple lower-limb joints, or “coupled” assistance. We found that coupled multi-joint devices could provide 50% greater metabolic savings than single joint devices. Further, coupled multi-joint devices were able to achieve similar metabolic savings to more complex multi-joint devices that controlled torques at each joint independently. Our results indicate that device designers could simplify multi-joint exoskeleton designs by reducing the number of torque control parameters through coupling, while still maintaining large reductions in metabolic cost.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Nicholas Bianco","trove_cats":[{"id":"1001","fullname":"OpenSim"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2113","unix_group_name":"heartgrowthpreg","modified":"1706822977","downloads":"24","group_name":"Multiscale model of heart growth during pregnancy","logo_file":"heartgrowthpreg","short_description":"This is a multiscale cardiac growth model for pregnancy designed to understand how mechanical and hormonal cues interact to drive heart growth during pregnancy. ","long_description":"This project focuses on building a multiscale cardiac growth model for pregnancy to understand how mechanical and hormonal cues interact to drive heart growth during pregnancy. This multiscale model couples a cell signaling network model that predicts cell-level hypertrophy in response to hormones and stretch, to a compartmental model of the rat heart and circulation that predicts organ-level growth in response to hemodynamic changes.\n\nThe companion paper can be found here: https://www.biorxiv.org/content/10.1101/2020.09.18.302067v1","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Molly Kaissar,Kyoko Yoshida","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2116","unix_group_name":"bicycle-model","modified":"1618257116","downloads":"0","group_name":"OpenSim Bicycle Model","logo_file":"","short_description":"The goal of this project is to build a 6-DoF model of a bicycle with accurate handling dynamics that can be used for forward analyses of standing cycling in OpenSim. The model will include a DoF at each wheel, the cranks, and the steering tube with contac","long_description":"The goal of this project is to build a 6-DoF model of a bicycle with accurate handling dynamics that can be used for forward analyses of standing cycling in OpenSim. The model will include a DoF at each wheel, the cranks, and the steering tube with contact geometries at the saddle, pedals, handlebar, and wheels.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ross Wilkinson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2120","unix_group_name":"quads_mom_arm","modified":"1684222789","downloads":"21","group_name":"Modified quadriceps moment arm for better estimation of the knee contact forces","logo_file":"","short_description":"The project presents a modified version of the model provided by Catelli et al. with improved knee extensor muscle moment arms for better prediction of knee contact forces.","long_description":"We modified the musculoskeletal model developed by Catelli et al. (1) by including the knee mechanism introduced by Lerner et al. (2) for separately estimating the medial and lateral tibiofemoral joint contact forces. We also replaced the wrapping surface for the knee extensor muscles with a separate surface for each of the muscles for improving the estimation of the knee joint contact forces. This modification was executed to better replicate the moment arm proposed by Bakenecker et al. (3), which was based on different moment arm functions presented in the current literature and validated using in vivo measurements. Further details regarding the model can be found in the supplementary material (4).\n\n(1) Catelli DS, Wesseling M, Jonkers I, Lamontagne M. A musculoskeletal model customized for squatting task. Computer Methods in Biomechanics and Biomedical Engineering. 2019.\n(2) Lerner ZF, DeMers MS, Delp SL, Browning RC. How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces. Journal of Biomechanics. 2015.\n(3) Bakenecker P, Raiteri B, Hahn D. Patella tendon moment arm function considerations for human vastus lateralis force estimates. Journal of Biomechanics. 2019.\n(4) Bosch Will, Esrafilian Amir, Vartiainen Paavo, Arokoski Jari, Korhonen Rami K., Stenroth Lauri. Alterations in the Functional Knee Alignment Are Not an Effective Strategy to Modify the Mediolateral Distribution of Knee Forces During Closed Kinetic Chain Exercises. Journal of Applied Biomechanics. 2022.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Will Bosch-Vuononen,Lauri Stenroth","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2123","unix_group_name":"mford10","modified":"1618856662","downloads":"0","group_name":"Vertical Jump Case Study Mark Ford","logo_file":"","short_description":"Analysis (estimation of forces, moments applied) of a knee joint when performing a vertical jump. I am only looking into the take off phase of the jump.","long_description":"Analysis (estimation of forces, moments applied) of a knee joint when performing a vertical jump. I am only looking into the take off phase of the jump.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mark Ford","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2127","unix_group_name":"spr","modified":"1619029083","downloads":"0","group_name":"Sprint start","logo_file":"","short_description":"Biomechanics analysis","long_description":"Biomechanics analysis","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Clayton McVay","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2131","unix_group_name":"yatheer23","modified":"1619215866","downloads":"0","group_name":"Prosthesis","logo_file":"","short_description":"Optimized design for a leg prosthesis.","long_description":"Optimized design for a leg prosthesis.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yaseer Abdullahi","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2134","unix_group_name":"casestudy","modified":"1619583290","downloads":"0","group_name":"Case Study Perri Meeks","logo_file":"","short_description":"Semester end squat modeling.","long_description":"Semester end squat modeling.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Perri Meeks","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2135","unix_group_name":"histone_ddtasep","modified":"1636415848","downloads":"7","group_name":"Dynamic Defect TASEP (ddTASEP) model of RNAPII transcription through nucleosomes","logo_file":"","short_description":"A dynamic defect Totally Asymmetric Simple Exclusion Process (ddTASEP) model of transcription with nucleosome induced pausing.","long_description":"Nucleosomes are recognized as key regulators of transcription. However, the relationship between slow nucleosome unwrapping dynamics and bulk transcriptional properties has not been thoroughly explored. Here, an agent-based model that we call the dynamic defect Totally Asymmetric Simple Exclusion Process (ddTASEP) was constructed to investigate the effects of nucleosome-induced pausing on transcriptional dynamics. Pausing due to slow nucleosome dynamics induced RNAPII convoy formation, which would cooperatively prevent nucleosome rebinding leading to bursts of transcription. The mean first passage time (MFPT) and the variance of first passage time (VFPT) were analytically expressed in terms of the nucleosome rate constants, allowing for the direct quantification of the effects of nucleosome-induced pausing on pioneering polymerase dynamics. The mean first passage elongation rate γ(h_c,h_o ) is inversely proportional to the MFPT and can be considered to be a new axis of the ddTASEP phase diagram, orthogonal to the classical αβ-plane (where α and β are the initiation and termination rates). Subsequently, we showed that, for β=1, there is a novel jamming transition in the αγ-plane that separates the ddTASEP dynamics into initiation-limited and nucleosome pausing-limited regions. We propose analytical estimates for the RNAPII density ρ, average elongation rate v, and transcription flux J in these regions that converge to the classical TASEP behavior in the limit γ→1 and verified them numerically. Finally, we demonstrate that the intra-burst RNAPII waiting times t_in follow the time-headway distribution of a max flux limit TASEP, that the average inter-burst interval (t_IBI ) correlates with the index of dispersion D_e and is inversely proportional to γ. In the limit γ→0, the average burst size reaches a maximum set by the closing rate h_c. Last, for cases with α≪1, the burst sizes are geometrically distributed, allowing large bursts even while the average burst size (N_B ) is small.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Robert Mines","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2144","unix_group_name":"predhealthygait","modified":"1621943262","downloads":"43","group_name":"Evaluating cost function criteria in predicting healthy gait","logo_file":"","short_description":"This project contains the framework described in \"Evaluating cost function criteria in predicting healthy gait\" by Veerkamp, Waterval, Geijtenbeek, Carty, Lloyd, Harlaar and van der Krogt (2021) in Journal of Biomechanics \nhttps://doi.org/10.1016/j.jbiomech.2021.110530","long_description":"This project contains the framework described in &quot;Evaluating cost function criteria in predicting healthy gait&quot; by Veerkamp, Waterval, Geijtenbeek, Carty, Lloyd, Harlaar and van der Krogt (2021) in Journal of Biomechanics \nhttps://doi.org/10.1016/j.jbiomech.2021.110530\n\nWe combined cost function criteria in a stepwise approach in order to best predict healthy gait. This project provides the developed framework (i.e., musculoskeletal OpenSim model, controller, cost function, and results with combined cost function) to be used in SCONE (https://simtk.org/projects/scone). ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Kirsten Veerkamp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2145","unix_group_name":"bloodvessel","modified":"1621485977","downloads":"0","group_name":"bloodvessel","logo_file":"","short_description":"bloodvessel","long_description":"bloodvessel","has_downloads":false,"keywords":"","ontologies":"","projMembers":"John Loh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2146","unix_group_name":"energy-est","modified":"1626196855","downloads":"763","group_name":"Wearable sensing for estimating energy expediture","logo_file":"","short_description":"This project includes supplementary data and code to estimate energy expenditure for the paper titled &quot;Sensing leg movement enhances wearable monitoring of energy expenditure&quot;.\n\nPlease cite the corresponding paper if you use these materials in your work: Slade, P., Kochenderfer, M.J., Delp, S.L. et al. Sensing leg movement enhances wearable monitoring of energy expenditure. Nat Communications 12, 4312 (2021).","long_description":"This project includes supplementary data and code to estimate energy expenditure for the paper titled &quot;Sensing leg movement enhances wearable monitoring of energy expenditure&quot;.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Patrick Slade","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2147","unix_group_name":"digitaltwins","modified":"1621708226","downloads":"0","group_name":"Digital twins of robots &amp; patients to classify movement disorders","logo_file":"","short_description":"The reliability and validity of a digital twin end effector robotic therapy intervention, to assess and classify adult upper limb pathological characteristics by comparing a digital twin human patient with spasticity to a digital twin human patient with t","long_description":"The reliability and validity of a digital twin end effector robotic therapy intervention, to assess and classify adult upper limb pathological characteristics by comparing a digital twin human patient with spasticity to a digital twin human patient with typical movement characteristics.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Simon Turnbull","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2148","unix_group_name":"mlu_zheng","modified":"1635573051","downloads":"0","group_name":"An optimization model for predicting manual lifting and unloading","logo_file":"mlu_zheng","short_description":"This model will be available months later.","long_description":"An optimization model was created in OpenSim Moco 0.4.0 to simulate manual lifting and unloading. A subtask-based multi-objective function method was used. The physical model developed in OpenSim 4.2 includes a foot, shank, thigh, pelvis, torso, upper arm, forearm and hand.\n\nThe corresponding paper is under review. More detailed information and source code will be available months later.","has_downloads":false,"keywords":"manual material handling,optimization,OpenSim,OpenSim Moco","ontologies":"","projMembers":"Simon Jeng","trove_cats":[{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2149","unix_group_name":"realtimekin","modified":"1630033552","downloads":"801","group_name":"Estimating 3D joint kinematics in real-time","logo_file":"","short_description":"This project houses the Raspberry Pi image to replicate the OpenSenseRT, a real-time and wearable system for motion capture.","long_description":"This project houses the Raspberry Pi image to replicate the OpenSenseRT, a real-time and wearable system for motion capture.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Patrick Slade","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2150","unix_group_name":"pm3_ffrct","modified":"1622133374","downloads":"0","group_name":"3D FFR CT from CT stacks","logo_file":"","short_description":"calculating FFR from CT driven 3D models of coronary vasculature","long_description":"calculating FFR from CT driven 3D models of coronary vasculature","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jermiah Joseph","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2152","unix_group_name":"resirole","modified":"1622224272","downloads":"0","group_name":"ResiRole","logo_file":"","short_description":"Use FEATURE predictions to compare structure models to reference structures. The similarity in the predicted probabilities of the SeqFEATURE models for the structure models versus the target structures are used to assess the quality of the structure model","long_description":"Use FEATURE predictions to compare structure models to reference structures. The similarity in the predicted probabilities of the SeqFEATURE models for the structure models versus the target structures are used to assess the quality of the structure models. A full description of the methodology is described in the following reference.  \n\nToth, Joshua M., et al. &quot;ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques.&quot; Bioinformatics 37.3 (2021): 351-359.\n\nThe URL for the ResiRole tool is available at the URL http://protein.som.geisinger.edu/ResiRole/","has_downloads":false,"keywords":"","ontologies":"","projMembers":"William McLaughlin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2154","unix_group_name":"emg_opt_tool","modified":"1624133703","downloads":"198","group_name":"EMG Optimization Tool","logo_file":"emg_opt_tool","short_description":"A MATLAB OpenSim (>=4.0) API that utilizes Cholewicki's  EMG optimization approach to solve muscle redundancies (Cholewicki & McGill, 1994; Cholewicki et al., 1995; Gagnon et al., 2011).\n","long_description":"We developed the framework for defining muscular contributions in OpenSim using the EMG optimization approach (Cholewicki &amp; McGill, 1994).  The attached tool and accompanying sample data/model/setup files provide an (hopefully) easy to follow working example.  Individual projects will undoubtedly require some minor adjustments to the function/example but project members will gladly assist with any issues one may have (please post such questions or initiate contact via the forum). \n\nWe are in the process publishing an evaluation study for the API and a lower back gait model.  A link to our paper will be provided at the appropriate time.  Meanwhile, we have provided a link to the dissertation this tool was designed for as well as a TGCS conference abstract (both can be found in 'Publications').\n\nIn addition to the 'EMGopt_Tool.zip' that contains the aforementioned API, 'Downloads' also includes a 'Base_Models.zip' that has both a top-down and bottom-up approach model for solving inverse dynamics.  Note: evaluation was done with the top-down model, with reference to the bottom-up approach in the supplemental material(s).\n \nSubscribe to this project for updates etc. and post forum questions.  ","has_downloads":true,"keywords":"EMG-assisted simulations,EMG-informed,EMG-driven simulations,EMG,EMG Optimization","ontologies":"","projMembers":"Brian Umberger,Graham Caldwell,Jacob J. Banks","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2155","unix_group_name":"referentaccdata","modified":"1672757902","downloads":"0","group_name":"Accelerometry Data From Daily Life","logo_file":"referentaccdata","short_description":"Accelerometry data from neurologically-intact, community-dwelling adults and adults with stroke, collected across multiple cycles of NIH R01HD068290.  ","long_description":"The first set of accelerometry data are from a cohort of neurologically-intact, community-dwelling adults, age 40-80.  Participants wore Actigraph accelerometers on all four limbs for 25 hours.  The first hour was in the lab (supervised), where participants completed 10 activities of daily living in a random order.  The remaining 24 hrs of recording the participants went about their day in the real world (unsupervised).  Data provide a referent sample of middle-aged and older adults for comparison with neurologic populations.\n\nThe second set of accelerometry data are from persons with stroke who participated in a clinical trial.  Participants followed a similar protocol as above, with accelerometer data coming from the baseline assessment, weekly during the intervention, and then post-intervention.   \n\nThe third set of accelerometery data are from a longitudinal, prospective cohort of persons with upper limb paresis post stroke, followed from 2 weeks to 6 months after stroke.  \n\nThe fourth set of accelerometry data are from a longitudinal, prospective cohort of persons with stroke or Parkinson Disease undergoing outpatient rehabilitation services.  Persons with stroke have either upper limb or walking data, while persons with Parkinson disease have walking data.   ","has_downloads":false,"keywords":"human, movement, accelerometry, activity in daily life","ontologies":"","projMembers":"Catherine Lang,Kayla  Thuet","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"2159","unix_group_name":"clavicleloading","modified":"1641306314","downloads":"0","group_name":"Musculoskeletal model for the assessment of clavicle loading","logo_file":"","short_description":"A musculoskeletal model that included the clavicle muscles and scapulohumeral rhythm was defined based on previously published models (Vasavada and Holzbaur). The standard OpenSim workflow (inverse kinematics, inverse dynamics, static optimisation, joint ","long_description":"A musculoskeletal model that included the clavicle muscles and scapulohumeral rhythm was defined based on previously published models (Vasavada and Holzbaur). The standard OpenSim workflow (inverse kinematics, inverse dynamics, static optimisation, joint reaction analysis) was used to calculate muscle and joint reaction forces based on 3D Marker data collected in three subjects during seven ADL. \nModel, experimental data and simulation results are shared.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Sanne Vancleef","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2161","unix_group_name":"acl_surgery","modified":"1652802597","downloads":"0","group_name":"Evaluation of Anterior Cruciate Ligament Surgical Reconstruction Through Finite","logo_file":"acl_surgery","short_description":"Evaluation of Anterior Cruciate Ligament Surgical Reconstruction Through Finite Element Analysis","long_description":"&lt;p align=&quot;center&quot;&gt;&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://mitkof6.gitlab.io/personal-site/publications/nature2022/risvas-nature-2022.mp4&quot; frameborder=&quot;0&quot; allow=&quot;autoplay; encrypted-media&quot; allowfullscreen&gt;&lt;/iframe&gt;&lt;/p&gt;\n\nAnterior Cruciate Ligament (ACL) tear is one of the most common knee injuries. The ACL reconstruction surgery aims to restore healthy knee function by replacing the injured ligament with a graft. Proper selection of the optimal surgery parameters is a complex task. To this end, we developed an automated modeling framework that accepts subject-specific geometries and produces finite element knee models incorporating different surgical techniques. Initially, we developed a reference model of the intact knee, validated with data provided by the OpenKnee project. This helped us evaluate the effectiveness of estimating ligament stiffness directly from MRI. Next, we performed a plethora of &quot;what-if&quot; simulations, comparing responses with the reference model. We found that a) increasing graft pretension and radius reduces relative knee displacement, b) the correlation of graft radius and tension should not be neglected, c) graft fixation angle of 20 degrees can reduce knee laxity, and d) single- versus double-bundle techniques demonstrate comparable performance in restraining knee translation. In most cases, these findings confirm reported values from comparative clinical studies. The numerical models are made publicly available, allowing for experimental reuse and lowering the barriers for meta-studies. The modeling approach proposed here can complement orthopedic surgeons in their decision-making.\n\nA link with data containing, models, simulation, and results: https://gitlab.com/knee_modeling_tools/acl_reconstruction_data","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dimitar Stanev,Kostas Risvas,Konstantinos Filip,Konstantinos Moustakas,Lefteris  Benos,Dimitrios Tsaopoulos","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2162","unix_group_name":"fes_amputee","modified":"1624968192","downloads":"0","group_name":"Functional Electrical Stimulation Design for Lower Limb Amputees","logo_file":"","short_description":"A predictive, neuromusculoskeletal model for unilateral transtibial amputees will be developed and applied to design the optimal rehabilitation protocol using functional electrical stimulation. ","long_description":"There are more than one million annual amputations globally as a result of vascular diseases, and cancer. Due to the increasing rate of diabetes and the population ageing, a growth of amputation is expected with the prediction that the amputee population will double by 2050. A prosthesis allows a certain restoration of functional mobility after an amputation. However, neither passive nor active prostheses can directly address the fundamental problems of chronic pain trauma and muscle atrophy in millions of amputees worldwide. Chronic amputation-related pain impairs function. In addition, the early decline in the use of the affected limb results in progressive muscle atrophy with strength loss. Concurrently, a mechanical adaption occurs in order to compensate for the collective effects due to limb loss. A common compensation strategy is to overload the intact limbs in terms of time and intensity, which will cause secondary musculoskeletal disorders, further compromising their health-related quality of life. \n\nIn the project, we will work towards a new generation of therapies for patients with lower limb amputations using a combination of functional electrical stimulation (FES) and musculoskeletal modelling techniques. The computational design of the FES rehabilitation protocol has the potential to improve the pain management, muscle strength and mobility for lower limb amputees by tailoring the FES prescription to the unique needs of each patient. \n\nThe project will deliver:\n1.\tA detailed musculoskeletal model of lower limb amputees based on the experimental gait data, including motion, ground reaction force, muscle EMG and high-resolution magnetic resonance imaging.\n2.\tA computational optimisation framework to design the FES rehabilitation protocol, which will address the clinical questions such as which muscles to stimulate and when to simulate them logically and non-intuitively.\n3.\tA feasibility study to evaluate the effectiveness and reliability of model-based FES protocol design, potentially resulting in a future clinical tool. \n\nThe project is supported by UK EPSRC (EP/V057138/1)","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ziyun Ding","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2164","unix_group_name":"opensense_val","modified":"1635483047","downloads":"0","group_name":"OpenSense: Validation of IMU-based estimates of kinematics over long durations","logo_file":"","short_description":"We validated an open-source workflow to measure 3D lower extremity joint kinematics over long durations using inertial measurement units (IMUs) for healthy subjects as they performed two 10-minute trials of common lower-extremity tasks.","long_description":"The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate, and capable of assessing and mitigating drift. \n\nWe computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-minute trials: walking and a repeated sequence of varied lower-extremity movements. We share these data openly as well as the scripts to complete our analyses.\n\nLink to our data in the DataShare tab above. \n\nLink to download OpenSim 4.2 with OpenSense: https://simtk.org/frs/?group_id=91\n\nMore information on OpenSense: https://simtk-confluence.stanford.edu/display/OpenSim/OpenSense+-+Kinematics+with+IMU+Data","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ajay Seth,Ayman Habib,Jennifer Hicks,Mazen Al Borno,Scott Uhlrich,Scott Delp,Carmichael Ong,Johanna O'Day,Mazen Al Borno","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2166","unix_group_name":"ssm_tibia","modified":"1681713895","downloads":"407","group_name":"Statistical Shape Model of the Tibia","logo_file":"ssm_tibia","short_description":"This project provides a freely accessible three-dimensional statistical shape model (SSM) of the tibia, the MATLAB scripts for generating a SSM and the segmented surface models of the cortical and trabecular bone. It also provides three example applications for the models. ","long_description":"This project provides a freely accessible three-dimensional statistical shape model (SSM) of the tibia, the MATLAB scripts for generating a SSM and the segmented surface models of the cortical and trabecular bone. Information on the use of code and data can be found in the read-me file contained within the download.\n\nFurther, this dataset and associated statistical shape models can be used in several ways to assist with skeletal focused research of the tibia-fibula. We do not have the scope to highlight each and every potential application, however have provided a series of example cases of where and how the shape models may be used. Our hope is that these examples can be directly used, or assist in guiding other uses. \n\nCase 1: Generating Surface Samples — this example case demonstrates how to use the shape model data to reconstruct a randomly sampled 'population' of surfaces.\n\nCase 2: Predicting and Generating Trabecular Volumes — this example case demonstrates how to combine the tibia and trabecular shape models to predict and generate the trabecular volume from a tibial surface.\n\nCase 3: Generating Tibia-Fibula Surfaces from Landmarks — this example case demonstrates how to use the tibia-fibula shape model to estimate and reconstruct surfaces from palpable landmarks on the tibia and fibula.\n\nPlease cite our work if you use this code or data. \n\n&lt;iframe src=&quot;https://widgets.figshare.com/articles/20454462/embed?show_title=1&quot; width=&quot;568&quot; height=&quot;351&quot; allowfullscreen frameborder=&quot;0&quot;&gt;&lt;/iframe&gt;","has_downloads":true,"keywords":"tibia-fibula,statistical shape modelling,lower limb,modelling","ontologies":"","projMembers":"Meghan Keast,Aaron Fox","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"2168","unix_group_name":"ferrochelatase","modified":"1625681680","downloads":"0","group_name":"Interpolation between ferrochelatase crystal structures","logo_file":"","short_description":"Interpolation between two (identical sequence) crystal structures of the human enzyme ferrochelatase. The main structural differences are centered around a pi-helix that has been proposed to be important for the enzyme to acquire the substrate iron and de","long_description":"Interpolation between two (identical sequence) crystal structures of the human enzyme ferrochelatase. The main structural differences are centered around a pi-helix that has been proposed to be important for the enzyme to acquire the substrate iron and deliver it to the active site in the non-oxidized ferrous form so that it can be used to produce heme.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Greg Hunter","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2173","unix_group_name":"imc-gp","modified":"1682467200","downloads":"41","group_name":"IMU- and EMG-driven simulation of muscle contraction during gait","logo_file":"","short_description":"We developed an algorithm for simulating muscle contraction during gait using only wearable sensors. It was developed to enable continuous monitoring of knee joint mechanics and the associated muscles during free-living conditions.","long_description":"Continuous monitoring of human movement is necessary to adapt personalized interventions, evaluate intervention efficacy, and facilitate research in cumulative-load dependent phenomena (e.g., muscle hypertrophy, osteoarthritis). Wearable sensors provide the hardware solution, but a minimal sensor set is required for practical deployment. This presents an analytical hurdle for use of physics-based simulation to calculate the biomechanical variables of interest; a minimal sensor set provides insufficient information. Machine learning techniques have been proposed as a potential solution but at the expense of generalizability and interpretability. Thus, we developed a hybrid approach that utilizes the best of both worlds: machine learning is used only to provide the missing information necessary to drive a physics-based simulation.\n\nWe developed an algorithm for simulating muscle contraction during gait using only wearable sensors. To facilitate practical deployment, our method uses a reduced sensor array: two IMUs (one each on the thigh and the shank) and three surface electrodes to measure surface electromyograms of the lateral and medial gastrocnemius and vastus medialis. The musculoskeletal kinematics are computed using the IMU data and optimal state estimation. Machine learning is used only to estimate the excitation of the non-instrumented muscles. Muscle contraction is then simulated using EMG-driven techniques.\n\nOur validation study (https://ieeexplore.ieee.org/document/9507535) demonstrated that our algorithm performed similarly to state-of-art techniques (both physics- and data-driven approaches) in characterizing muscle and joint dynamics in walking gait.\n\nCode and an example dataset is publicly available and maintained at this GitHub repo: https://github.com/gurchiek/nms-dyn\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Reed Gurchiek","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2174","unix_group_name":"armrobotmodel","modified":"1628475359","downloads":"136","group_name":"Simulation of Coupled Arm-Robot Motion to Design Rehabilitation Interventions","logo_file":"armrobotmodel","short_description":"This project contains custom Matlab code, experimental robot motor torque and angle data, and OpenSim models of a rehabilitation robot and coupled arm-robot model.","long_description":"This project contains custom Matlab code, experimental robot motor torque and angle data, and OpenSim models of a rehabilitation robot and coupled arm-robot model. The rehabilitation robot used in this study was developed by Kinarm Corporation (Kingston, Ontario, Canada), while the upper extremity model used was developed by Saul et al. (2015) and is available on Simtk.org (https://simtk.org/projects/upexdyn). The study seeks to verify and validate whether the OpenSim models are able to reproduce experimental measurements made with the robot under four conditions: 1) active robot, no arm, 2) active robot, passive arm, 3) passive robot, active arm, and 4) active robot, active arm.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"B.J. Fregly","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2176","unix_group_name":"ps-202101","modified":"1626714644","downloads":"0","group_name":"Cycling motion and muscle recruitment cycling on barefoot pedals","logo_file":"","short_description":"Since cycling exists, serious cyclist are placing their foot on the cycling pedal near the front of the foot. While developing a more ergonomic pedal first aimed at more cycling comfort I discovered by practice that there was no major difference in perfor","long_description":"Since cycling exists, serious cyclist are placing their foot on the cycling pedal near the front of the foot. While developing a more ergonomic pedal first aimed at more cycling comfort I discovered by practice that there was no major difference in performance when performing an effort longer than 10 seconds. Cyclists riding on barefoot pedals report they are feeling less fatigued on these pedals and have less back and knee pain. I am trying to find out what the difference in muscle recruitment is and what effects are really worth mentioning.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mischa Nieuwboer","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2179","unix_group_name":"shooting","modified":"1627670895","downloads":"0","group_name":"analysis of arm movement during shooting action","logo_file":"","short_description":"analysis of arm movement during shooting action","long_description":"analysis of arm movement during shooting action","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jose Bravo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2183","unix_group_name":"topographylift","modified":"1690204012","downloads":"0","group_name":"Joint loading topography during occupational tasks","logo_file":"","short_description":"To improve ergonomic advice and to design an optimal job rotation schedule for decreasing WMSDs, a thorough documentation of full-body musculoskeletal loading topography during occupational tasks is needed. The dataset used to publish the paper will be made available here in the near future. ","long_description":"full paper: https://doi.org/10.1016/j.ergon.2023.103451\nMotion capture data containing all occupational tasks described in the paper in .c3d, .trc and .mot: https://doi.org/10.48804/XES6PY \n\nBackground\nTo improve ergonomic recommendations and decrease work-related musculoskeletal disorders, thorough documentation of full-body, joint loading topography during occupational tasks is needed. Therefore, the purpose of this study was to document full-body internal joint loading topography in terms of estimated joint contact forces during occupational tasks. In addition, this internal loading topography was also compared to loading proxies (e.g., external joint moments) commonly used to assess injury risk during occupational tasks.\n\nMethods\n3D motion capture and ground reaction forces were measured while 20 participants performed ten occupational tasks. A musculoskeletal modeling workflow with a detailed spine was used to calculate internal joint loading in terms of contact forces, and their association with external joint moments was evaluated.\n\nFindings\nLifting 10 kg from the ground imposed the highest full-body internal joint loading compared to all other lifting tasks, while lifting 10 kg from hip height to shoulder height imposed the lowest internal joint loading. Only during occupational tasks involving standing upright posture, loading proxies did correlate well with internal joint loading.\n\nInterpretation\nThe modeling workflow and the internal joint loading topography could inform ergonomic recommendations on optimized load distribution across different anatomical regions.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Arthur van der Have","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2187","unix_group_name":"cbbxix_opensim","modified":"1631995449","downloads":"10","group_name":"How to get started using modeling and simulation with OpenSim?","logo_file":"cbbxix_opensim","short_description":"(En) This workshop will be held during the XIX Brazilian Congress of Biomechanics (XIX CBB), on September 13, 14 and 16, 2021.\nThe objective is to train participants to use the basic tools available in OpenSim, aimed at analyzing and simulating the dynamics of human movement.","long_description":"(En) This workshop will be held during the XIX Brazilian Congress of Biomechanics (XIX CBB), on September 13, 14 and 16, 2021.\nThe objective is to train participants to use the basic tools available in OpenSim, aimed at analyzing and simulating the dynamics of human movement, as well as reviewing theoretical concepts fundamental to understanding the use of these tools. We will cover theoretical principles of the use of computer modeling and simulation, we will present OpenSim and the elements of a neuromusculoskeletal model. We will explore the files that configure the model. We will work on the creation, editing and loading of marker sets to describe the movement and the scaling of models from experimental data. We will apply the Inverse Kinematics tool and work on creating configuration files to be used in inverse dynamics.\n\nComo começar a usar modelagem e simulação com o OpenSim?\n(Pt) Este workshop será ministrado durante o XIX Congresso Brasileiro de Biomecânica  (XIX CBB), nos dias 13, 14 e 16 de setembro de 2021.\nA atividade tem como objetivo capacitar aos participantes para o uso das ferramentas básicas disponíveis no OpenSim, voltadas para análise e simulação da dinâmica do movimento humano, assim como revisar conceitos teóricos fundamentais à compreensão do uso dessas ferramentas. Abordaremos princípios teóricos do uso de modelagem e simulação computacional, apresentaremos o OpenSim e os elementos de um modelo neuromusculoesquelético. Exploraremos os arquivos que configuram o modelo. Trabalharemos a criação, edição e carregamento de um conjunto de marcadores (marker set) para descrição do movimento e o escalonamento de modelos a partir de dados experimentais. Aplicaremos a ferramenta de Cinemática Inversa e trabalharemos a criação de arquivos para serem utilizados na dinâmica inversa.\nhttps://www.cbb2021.com.br/pagina/324/workshop%201/\n\n(Es) Este workshop se realizará durante el XIX Congreso Brasileño de Biomecánica (XIX CBB), los días 13, 14 y 16 de septiembre de 2021.\nLa actividad tiene como objetivo capacitar a los participantes en el uso de las herramientas básicas disponibles en OpenSim, orientadas a analizar y simular la dinámica del movimiento humano, así como a revisar conceptos teóricos fundamentales para comprender el uso de estas herramientas. Cubriremos los principios teóricos del uso del modelado y simulación por computadora, presentaremos OpenSim y los elementos de un modelo neuromusculoesquelético. Exploraremos los archivos que configuran el modelo. Trabajaremos en la creación, edición y carga de un conjunto de marcadores (marker set) para describir el movimiento y el escalado de modelos a partir de datos experimentales. Aplicaremos la herramienta de Cinemática Inversa y trabajaremos en la creación de archivos para ser utilizados en dinámica inversa.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Maria Isabel Orselli,Kristy Godoy","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2191","unix_group_name":"barbarus","modified":"1638288679","downloads":"18","group_name":"Messor barbarus ant biomechanics","logo_file":"barbarus","short_description":"This project is about the develpment of a musculoskeleetal model of the messor barbarus ant.  ","long_description":"The model was build from 3D scans coming from X-ray micro-computed tomography. Joint geometrical parameters were estimated from the articular surfaces of the exoskeleton. Kinematic data of a free walking ant was acquired using high-speed synchronized video cameras. Spatial coordinates of 49 virtual markers were used to run inverse kinematics simulations","has_downloads":true,"keywords":"inverse kinematics,ant,insects","ontologies":"","projMembers":"Santiago Arroyave-Tobon","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2194","unix_group_name":"sprain-sim","modified":"1637255972","downloads":"88","group_name":"Simulating Ankle Sprain Prevention","logo_file":"sprain-sim","short_description":"A package for probabilistic, virtual testing of ankle sprain prevention, including a multibody model and a FE brace for optimizing brace form, fit, and function.\n","long_description":"This project is composed of complementary tools aimed to create a multiscale platform for rapid virtual testing of new ankle sprain prevention technologies:\n\n1) Extends a deterministic, subject-specific forward simulation of single-limb drop landing with probabilistic inputs, automation, and analysis\n\nhttps://github.com/ajyoder/ankle-sprain-prob\n\n2) An FEA module to generate wearable brace designs optimized to morphology constraints, and virtually test efficacy with probabilistic simulation","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Adam Yoder,Anthony Petrella","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2197","unix_group_name":"gatestudy","modified":"1630612929","downloads":"0","group_name":"EBME 329 Gate Simulation","logo_file":"","short_description":"Study the gate of patients with prosthetics compared to patients without prosthetics and the overall outcomes","long_description":"Study the gate of patients with prosthetics compared to patients without prosthetics and the overall outcomes","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kelly Moton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2201","unix_group_name":"predict-kam","modified":"1677683622","downloads":"8","group_name":"Predicting KAM Response to Gait Retraining","logo_file":"predict-kam","short_description":"We present a model, trained on synthetic data, to predict the extent of first peak KAM reduction after toe-in gait retraining. ","long_description":"About: Although foot progression angle gait retraining is overall beneficial as a conservative intervention for knee osteoarthritis, knee adduction moment (KAM) reductions are not consistent across patients. Moreover, customized gait interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a model that uses minimal clinical data to predict the extent of first peak KAM reduction after toe-in gait retraining. Given the lack of large public datasets that contain different gaits for the same patient, we present a method to generate toe-in gait data synthetically, and share the resultant trained model.\n\nData are available under Downloads &gt; Data Share\nCode and trained models are available on GitHub: https://github.com/CMU-MBL/predictKAMreduction  \n\nCitation: Rokhmanova N, Kuchenbecker KJ, Shull PB, Ferber R, Halilaj E (2022) Predicting knee adduction moment response to gait retraining with minimal clinical data. PLoS Comput Biol 18(5): e1009500. https://doi.org/10.1371/journal.pcbi.1009500","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Nataliya Rokhmanova","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2205","unix_group_name":"pers_fbm_spine","modified":"1656945216","downloads":"276","group_name":"Personalizable full-body models with a detailed thoracolumbar spine","logo_file":"pers_fbm_spine","short_description":"This project provides adult male and female musculoskeletal full-body models with a detailed thoracolumbar spine as well as the code for personalizing the models, including the adjustment of spinal alignment based on skin markers.","long_description":"Full-body base models with detailed thoracolumbar spine already available. Full description and MATLAB code for personalization of spinal alignment coming soon.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Stefan Schmid,Lukas Connolly,Marco Senteler,Greta Moschini","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2206","unix_group_name":"bailey2021_imu","modified":"1637332538","downloads":"150","group_name":"OpenSense model of motor variability for gait","logo_file":"","short_description":"Dataset for IMU-driven (OpenSense) and optoelectronic-driven (OpenSim) kinematic models for several 7-minute conditions of continuous treadmill gait. ","long_description":"Here we include IMU-driven (OpenSense) and optoelectronic-driven (OpenSim) inverse kinematics from 14 healthy young adults (7 males and 7 females) who performed five 7-minute trials of walking on a treadmill: (i) at preferred speed and with preferred arm swing, (ii) at 70% preferred speed and with preferred arm swing, (iii) at 130% preferred speed and with preferred arm swing, (iv) at preferred speed and with active arm swing, and (v) at preferred speed and with the arms bound to the sides. Models were based on the Rajagopal 2015 full-body model and were modified to lock the toe joints.\n\nOur manuscript (https://www.mdpi.com/1424-8220/21/22/7690) reports on the concurrent validity and sensitivity of the OpenSense model for joint angle timeseries, ranges of motion, and several (stride-to-stride) motor variability features from these angles: (a) magnitude of variability, (b) local dynamic stability, (c) persistence of range-of-motion fluctuations, and (d) regularity.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Thomas Uchida,Christopher Bailey,Ryan Graham,Julie Nantel","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2208","unix_group_name":"bucknell","modified":"1632081925","downloads":"0","group_name":"MECH 476","logo_file":"","short_description":"Biomechanics class exercise","long_description":"Biomechanics class exercise","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jenna Cohen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2210","unix_group_name":"paed_ssm","modified":"1712618361","downloads":"58","group_name":"Personalised Lower Limb Bone Models in a Paediatric Population","logo_file":"","short_description":"Statistical shape model for the pelvis, femur, and tibia/fibula of children aged 4-18 years. The dataset consisted of 333 CT scans. From PCA weights, partial least squares regression can be used to predict bone shapes using demographic inputs such as age,","long_description":"Statistical shape model for the pelvis, femur, and tibia/fibula of children aged 4-18 years. The dataset consisted of 333 CT scans. From PCA weights, partial least squares regression can be used to predict bone shapes using demographic inputs such as age, height, and weight. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91±0.99mm in the pelvis, 2.01±0.62mm in the femur, and 1.85±0.54mm in the tibia/fibula.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Laura Carman","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2212","unix_group_name":"genedive","modified":"1645840353","downloads":"27","group_name":"GeneDive","logo_file":"","short_description":"GeneDive is a powerful but easy-to-use application that can search, sort, group, filter, highlight, and visualize interactions between drugs, genes, and diseases (DGR). GeneDive also facilitates topology discovery through the various search modes that rev","long_description":"GeneDive is a powerful but easy-to-use application that can search, sort, group, filter, highlight, and visualize interactions between drugs, genes, and diseases (DGR). GeneDive also facilitates topology discovery through the various search modes that reveal direct and indirect interactions between DGR. The search results, in textual and graphical form, can be downloaded along with the search settings to easily restart the session at later time. Refer to &lt;a href=&quot;https://pubmed.ncbi.nlm.nih.gov/33737208/&quot;&gt;Wong et al. 2021&lt;/a&gt; for more details.\n\nGeneDive is a joint project between the Computer Science Department at San Francisco State University, and the Bioengineering Department at Stanford University.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Mike Wong,Russ Altman,Anagha Kulkarni","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2217","unix_group_name":"orangecat","modified":"1632857376","downloads":"0","group_name":"PRP-Lexity","logo_file":"","short_description":"modelling blood thru microfluidic device","long_description":"modelling blood thru microfluidic device","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mallory Box","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2219","unix_group_name":"simulation1","modified":"1633210047","downloads":"0","group_name":"MECHENG21","logo_file":"","short_description":"simulation for class\nBecome familiar with OpenSim’s graphical user interface (GUI) \nPage 2of 11•Discover some limitations of musculoskeletal models •Explore differences between “1-joint” (uni-articular) and “2-joint” (bi-articular) muscles","long_description":"simulation for class\nBecome familiar with OpenSim’s graphical user interface (GUI) \nPage 2of 11•Discover some limitations of musculoskeletal models •Explore differences between “1-joint” (uni-articular) and “2-joint” (bi-articular) muscles •Use OpenSim to address an important clinical problem","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jamie Kronenberg","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2221","unix_group_name":"bme315","modified":"1633378651","downloads":"0","group_name":"BME 315","logo_file":"","short_description":"Biomechanics","long_description":"Biomechanics","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jack Maher","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2222","unix_group_name":"lab2","modified":"1633380934","downloads":"0","group_name":"BME Lab 2: Muscle Modeling","logo_file":"","short_description":"BME 315 - Biomechanics Lab 2 Muscle Modeling \nBy: Katie McGovern and Sam Bardwell","long_description":"BME 315 - Biomechanics Lab 2 Muscle Modeling \nBy: Katie McGovern and Sam Bardwell","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Katie McGovern","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2226","unix_group_name":"swathi_1","modified":"1633975517","downloads":"0","group_name":"biomechatronics","logo_file":"","short_description":"simulation","long_description":"simulation","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Swathilakshmi P R K","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2227","unix_group_name":"upper-limb","modified":"1633975430","downloads":"0","group_name":"EMG-driven upper limb movement simulation","logo_file":"","short_description":"This project contains EMG and motion data for 10 subjects during isokinetic movement.","long_description":"This project contains EMG and motion data for 10 subjects during isokinetic movement.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yixuan Sheng","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2229","unix_group_name":"knee-cipd-kletu","modified":"1635320624","downloads":"0","group_name":"Analytical Method for contact Mechanics of Biological Joints- Knee Joint MBD","logo_file":"knee-cipd-kletu","short_description":"The long-term aim of this research is the MBD simulation of musculoskeletal systems through minor computational requirements. The research methodology could be framed into 3 phases as follows,","long_description":"The long-term aim of this research is the MBD simulation of musculoskeletal systems through minor computational requirements. The research methodology could be framed into 3 phases as follows,\n\n1.\tDeveloping an analytical model of biological joints,\n2.\tVerifying  using numerical methods\n3.\tMBD through co-simulation.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"SACHIN KHOT,Ravi Guttal,Subhramanya Doddamani","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2230","unix_group_name":"20_eulerdofhand","modified":"1639526794","downloads":"327","group_name":"Kinematic Arm Model with Articulated Hand","logo_file":"","short_description":"Expanded Saul et al. (2015) arm model with 20 degrees of freedom of the elbow, wrist, and hand. The added degrees of freedom follow cardan Euler angles. The original anatomical degrees of freedom are locked. The dimensions and inertial parameters of segme","long_description":"Expanded Saul et al. (2015) arm model with 20 degrees of freedom of the elbow, wrist, and hand. The added degrees of freedom follow cardan Euler angles. The original anatomical degrees of freedom are locked. The dimensions and inertial parameters of segments are based on published anthropometric data for an average human (Winter 2009 and Kodak 2007). The model was used for testing a segmented forearm model of hand pronation-supination (Yough et al. 2021).\n\nContributors to the model:\nMatthew Yough\nRussell Hardesty\nMatthew Boots\nSergiy Yakovenko\nValeriya Gritsenko\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Matthew Yough","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2232","unix_group_name":"raja-dhm","modified":"1663206535","downloads":"37","group_name":"Full Body Model adjusted - all hip spanning muscles","logo_file":"","short_description":"The Full Body Model of Rajagopal et al. (2016) was adjusted to contain all 22 hip spanning muscles. ","long_description":"The Full Body Model (https://simtk.org/projects/full_body) of Rajagopal et al. (2016) compatible with OpenSim v4.1 was adjusted to contain all 22 hip spanning muscles. Six hip spanning muscles (obturator internus, obturator externus, quadratus femoris, gemellus inferior, and gemellus superior, pectineus) were added and two muscle paths (posterior gluteus medius and piriformis) were adjusted. As per Rajagopals study (Rajagopal et al., 2016), muscle volumes of the added muscles were calculated for a 75kg, 170cm tall male via Hansfields equations (Handsfield et al., 2014) using a specific muscle tension of 60N/cm2. \n\nFor more details regarding the model adjustments, see: https://doi.org/10.1109/tbme.2021.3114717","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Evy Meinders,Basilio Gonçalves,David John Saxby,David Lloyd,Laura Diamond,Claudio Pizzolato","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2234","unix_group_name":"cadaver1","modified":"1636407705","downloads":"0","group_name":"Cadaver Study Force Application System","logo_file":"cadaver1","short_description":"Cad Design, labview code and fabrication files for a universal testing system tailored toward cadaver studies of the lower extremity.","long_description":"This system was developed to overcome limitations of using other universal testing systems such as Instrons in cadaver studies.  The limitations it overcomes include access to these systems in wet labs, trans-portability, ease of sanitization of the system post study, cadaver surgical access from all angles, ease of mounting cadavers, manipulation of limb angles and positioning and ease of customizability of tests. This system has been sussessfully tested in cadaver studies and can be easily replicated by sending the custom fabrication files to a machinist and sheetmetal company.  Other components such as bearings, rod ends, Velmex linear stage, induction hardened shaft and data acquisition components will need to be individually sourced. Further to this, the design can be modified for cadaver studies for other bodily components such as the upper extremity or spine. See 'documents' folder for access to these files.  The first piece of literature linked to this system will be linked soon.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Angus Malcolm","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2237","unix_group_name":"z-anatomy","modified":"1635787313","downloads":"0","group_name":"Z-anatomy: an open 3D atlas of human anatomy","logo_file":"","short_description":"This project began in february 2021 and is shared on this page:\nhttps://www.z-anatomy.com/\n\nBlender, the open source 3D modelling software is used to navigate through the hundreds of .obj 3D files of anatomical structures produced ten years ago by 'Bod","long_description":"This project began in february 2021 and is shared on this page:\nhttps://www.z-anatomy.com/\n\nBlender, the open source 3D modelling software is used to navigate through the hundreds of .obj 3D files of anatomical structures produced ten years ago by 'BodyParts3D'.\n\nThese objects have been gathered, renamed, organized, re-meshed (retopo), instanced, labeled and completed.\n\nA python script now allows:\n-to easily add labels, \n-import definitions, \n-to automatically display the labels and definition of the active object\n-to translate all the structures at once\n-to create cross sections\n-to reach all the object's collections in two clicks\n-to show/hide/isolate only the parts of interest\n\nThis project is free and meant to be collaborative.\n\nPlease feel free to jump in if you want to contribute.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Gauthier Kervyn","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2241","unix_group_name":"fpmetfixedspeed","modified":"1637582312","downloads":"0","group_name":"Propulsive Force Metabolics during Fixed Speed Walking","logo_file":"fpmetfixedspeed","short_description":"Download biomechanical data of young adults walking at fixed speeds in response to targeted biofeedback of propulsive forces in a series of five-minute trials. ","long_description":"This dataset contains biomechanical data of young adults walking at fixed speeds in response to targeted biofeedback of propulsive forces in a series of five-minute trials. We also provide metabolic data, participant demographics, as well as static &amp; functional hip joint center trials to accurately scale and normalize relevant outcomes. \n\nWe also provide opensim outputs for model scaling, inverse kinematics, RRA, and CMC for multiple trials within each subject folder. \n\nThis dataset yielded the following published manuscripts: https://doi.org/10.1080/10255842.2021.1900134 ; https://doi.org/10.1016/j.jbiomech.2021.110447","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ricky Pimentel","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2246","unix_group_name":"swallowing","modified":"1647942140","downloads":"0","group_name":"swallowing musculoskeletal model","logo_file":"swallowing","short_description":"we made a musculoskeletal model to \nunderstand the mechanism of swallowing.\nThis project is unfinished. Please wait for a while.","long_description":"we made the musculoskeletal model to understand the mechanism of swallowing.\nthis model has 25 muscles and springs.\nyou can simulate the activation of muscle related to swallowing by input trajectory of hyoid and thyroid bone.\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yuriko Iyama,Koichiro Shizuya","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2248","unix_group_name":"exercisemindset","modified":"1674514955","downloads":"0","group_name":"Mindset and Physical Activity Survey in Individuals with Knee Osteoarthritis","logo_file":"","short_description":"The project provides the code and data associated with the paper:\n\nBoswell M, et al. 2021. Mindset is Associated with Physical Activity and Management Strategies in Individuals with Knee Osteoarthritis: A Repeated Cross-Sectional Survey. Annals of Physi","long_description":"The project provides the code and data associated with the paper:\n\nBoswell M, et al. 2021. Mindset is Associated with Physical Activity and Management Strategies in Individuals with Knee Osteoarthritis: A Repeated Cross-Sectional Survey. Annals of Physical Medicine and Rehabilitation.\n\nThe key findings/highlights of the paper are:\n• In individuals with knee osteoarthritis, mindset is associated with physical activity.\n• Those who manage symptoms with exercise have a more appeal-focused exercise mindset.\n• MPH-Physical Activity is short (7 items), reliable, and related to health measures.\n• Mindset interventions may provide a new tool for increasing activity participation.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Melissa Boswell,Kris Evans","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2253","unix_group_name":"caren2opensim","modified":"1637976096","downloads":"0","group_name":"CAREN to OpenSim (Matlab Scripts)","logo_file":"","short_description":"This project aims to bring data collected in the CAREN system (Motek Medical B.V.) into OpenSim for further biomechanical analyses. \nhttps://www.motekmedical.com/\n\nHere is the GitHub link: https://github.com/hmok/CAREN\n\nThis project is built upon some of the work performed by the OpenSim team on Inverse Problem in Biomechanics in Matlab API.","long_description":"This project aims to bring data collected in the CAREN system (Motek Medical B.V.) into OpenSim for further biomechanical analyses. \nhttps://www.motekmedical.com/\n\nHere is the GitHub link: https://github.com/hmok/CAREN\n\nThis project is built upon some of the work performed by the OpenSim team on Inverse Problem in Biomechanics in Matlab API.\n\nSince the CAREN system integrates several systems such as MoCap, Treadmills, Robotics platform, Auditory and Visual systems, we require a comprehensive approach to analyze data collected from these separate systems. \n\nStay tuned please as we are finalizing the codes. These codes can be used for data analyses from any other MoCap lab if proper modifications are done. But this whole process was simplified for the CAREN lab at Melbourne University.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Hossein Mokhtarzadeh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2255","unix_group_name":"bit","modified":"1675485441","downloads":"141","group_name":"A bilateral upper extremity trunk model for cross-country sit-skiing.","logo_file":"bit","short_description":"This project provides a bilateral upper extremity trunk model established for the study of the propulsion technique of the two poles of the cross-country sit-skiing. The bilateral upper extremity trunk model was developed by combining three  OpenSim models.\n\nThis project is related to the the paper: \n\nChen, X., Huang, Y., Jiang, L. et al. Bilateral upper extremity trunk model for cross-country sit-skiing double poling propulsion: model development and validation. Med Biol Eng Comput 61, 445–455 (2023). https://doi.org/10.1007/s11517-022-02724-8 \n\nPlease cite our work if you use this code or data.","long_description":"This project provides a bilateral upper extremity trunk model established for the study of the propulsion technique of the two poles of the cross-country sit-skiing. The bilateral upper extremity trunk model was developed by combining three previously built OpenSim models: full-body lumbar spine for the base model (Raabe and Chaudhari 2016), das3 model (Blana, Hincapie et al. 2008) for the rotator cuff muscles and spanning elbow joint muscles, human shoulder model (Seth, Dong et al. 2019) for the body properties of scapula and clavicle. ","has_downloads":true,"keywords":"opensim model","ontologies":"","projMembers":"Xue Chen","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2259","unix_group_name":"exotendon","modified":"1692814624","downloads":"0","group_name":"Connecting the legs with a spring improves human running economy","logo_file":"","short_description":"In this study we tested a simple passive elastic assistive device's ability to improve human running economy in a speed controlled trial. We collected motion, kinetic, and EMG data in addition to energy expenditure to verify the savings from the device. ","long_description":"Human running is inefficient. For every 10 calories burned, less than 1 is needed to maintain a constant forward velocity – the remaining energy is, in a sense, wasted. The majority of this wasted energy is expended to support the bodyweight and redirect the center of mass during the stance phase of gait. An order of magnitude less energy is expended to brake and accelerate the swinging leg. Accordingly, most devices designed to increase running efficiency have targeted the costlier stance phase of gait. An alternative approach is seen in nature: spring-like tissues in some animals and humans are believed to assist leg swing. While it has been assumed that such a spring simply offloads the muscles that swing the legs, thus saving energy, this mechanism has not been experimentally investigated. Here, we show that a spring, or ‘exotendon’, connecting the legs of a human reduces the energy required for running by 6.4±2.8%, and does so through a complex mechanism that produces savings beyond those associated with leg swing. The exotendon applies assistive forces to the swinging legs, increasing the energy optimal stride frequency. Runners then adopt this frequency, taking faster and shorter strides, and reduce the joint mechanical work to redirect their center of mass. Our study shows how a simple spring improves running economy through a complex interaction between the changing dynamics of the body and the adaptive strategies of the runner, highlighting the importance of considering each when designing systems that couple human and machine.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jon Stingel,Joy Ku,Cara Welker,Scott Delp,Scott Uhlrich,Elliot Hawkes,JESSICA SELINGER,Sean Sketch,Cole Simpson,Rachel Jackson,Steve Collins","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2260","unix_group_name":"insilico-rehab","modified":"1638349790","downloads":"0","group_name":"In silico Rehabilitation","logo_file":"","short_description":"This project aims at providing optimal kinematics advice for rehabilitation and/or osteoarthritis mitigation.\n\nIt combines both SPM1D-based MovementRx visualization system of areas of deviation from normative reference with opensim-moco based gait traje","long_description":"This project aims at providing optimal kinematics advice for rehabilitation and/or osteoarthritis mitigation.\n\nIt combines both SPM1D-based MovementRx visualization system of areas of deviation from normative reference with opensim-moco based gait trajectory optimization.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Amr ALHOSSARY","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2266","unix_group_name":"abinay-1","modified":"1639118809","downloads":"0","group_name":"exosuit","logo_file":"","short_description":"softexosuit . load limitations and calculations","long_description":"softexosuit . load limitations and calculations","has_downloads":false,"keywords":"","ontologies":"","projMembers":"ABINAYA SRI","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2269","unix_group_name":"primseq","modified":"1652992763","downloads":"0","group_name":"PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training","logo_file":"","short_description":"This site serves for dissemination of upper body motion data from stroke patients during rehabilitation. The data is captured using wearable inertial measurement units and cameras. The data are fully labeled by trained annotators. A link to the code for the deep learning model is also provided. ","long_description":"We present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. PrimSeq encompasses three main steps: (1) the capture of upper body motion during rehabilitation with wearable inertial measurement units (IMUs) and video, (2) the generation of primitive sequences from IMU data with the trained deep learning model, and (3) the tallying of primitives with a counting algorithm. \n\nTo build this approach, we collected a large realistic dataset of stroke patients and healthy controls undergoing rehabilitation training activities. We labeled the dataset and developed a new deep learning method to process it.  \n\nUsing a previously established functional motion taxonomy (Schambra et al., 2019), we identified five classes of functional primitives, which are elemental units of functional motion. These classes are reach, reposition, transport, stabilize, and idle.  A reach is a UE motion to move into contact with a target object; a reposition is a UE motion to move proximate to a target object; a transport is a UE motion to convey a target object; a stabilize is a minimal-motion to keep a target object still; and an idle is a minimal-motion to stand at the ready near target object. \n\nData include: \n- Subject demographic and clinical characteristics\n- Kinematic data from 9 IMUs affixed to the upper body: a 77-dimensional dataset every 10 ms consisting of 27 dimensions of accelerations (9 IMUs × 3D accelerations per IMU), 27 dimensions of quaternions (9 IMUs × 3D quaternions per IMU), 22 joint angles, and side of the patient’s affected upper extremity (left or right).\n- Video features from 2 orthogonal cameras:\n\nWe invite you to download the data and code.\n\nReferences:\n- Schambra HM, Parnandi A, Pandit NG, Uddin J, Wirtanen A, Nilsen DM. A Taxonomy of Functional Upper Extremity Motion. Front Neurol. 2019 Aug 20;10:857. doi: 10.3389/fneur.2019.00857. PMID: 31481922; PMCID: PMC6710387.\n\n- Parnandi, A., Kaku, A., Venkatesan, A., Pandit, N., Wirtanen, A., Rajamohan, H., Venkataramanan, K., Nilsen, D., Fernandez-Granda, C. and Schambra, H., 2021. PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training. arXiv preprint arXiv:2112.11330. (https://arxiv.org/abs/2112.11330)\n\n- Kaku, A., Liu, K., Parnandi, A., Rajamohan, H.R., Venkataramanan, K., Venkatesan, A., Wirtanen, A., Pandit, N., Schambra, H. and Fernandez-Granda, C., 2021. Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution. arXiv preprint arXiv:2111.02521. (https://arxiv.org/abs/2111.02521)\n\nAcknowledgement: \nThis work was funded by the American Heart Association/Amazon Web Service postdoctoral fellowship 19AMTG35210398 (A.P.), NIH R01 LM013316 (C.F.G., H.S.), NIH K02 NS104207 (H.S.), NIH NCATS UL1TR001445 (H.S.), and NSF NRT-HDR 1922658 (A.K., C.F.G.)\n","has_downloads":false,"keywords":"Deep learning,Machine Learning,inertial sensors,Motion Data,stroke rehabilitation","ontologies":"Time_Series_Analysis,Data_Repository,Algorithm,Source_Code","projMembers":"Heidi Schambra,Aakash Kaku,Avinash Parnandi,Kangning Liu","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":true},{"group_id":"2270","unix_group_name":"abi_knee_models","modified":"1648520088","downloads":"0","group_name":"ABI Knee Joint Modelling","logo_file":"","short_description":"Knee Joint Modelling at the Auckland Bioengineering Institute.","long_description":"Knee Joint Modelling at the Auckland Bioengineering Institute.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Nynke Rooks","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2271","unix_group_name":"sts_reh-device","modified":"1639718975","downloads":"0","group_name":"Design and development of Sit to Stand and rehabilitation assistive device","logo_file":"","short_description":"We have plan to design and development of Sit to Stand and rehabilitation assistive device to reduce burden on caregiver","long_description":"We have plan to design and development of Sit to Stand and rehabilitation assistive device to reduce burden on caregiver","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Subodh Suman","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2272","unix_group_name":"post-stroke-sym","modified":"1641509601","downloads":"92","group_name":"Effects of simulated neuromuscular impairments post-stroke on gait asymmetry","logo_file":"","short_description":"Our goal is to better understand how neuromuscular impairments in people post-stroke affect their gait performance by using simulation to predict the optimal gait patterns for musculoskeletal models with simulated impairments. ","long_description":"Several neuromuscular impairments (e.g., hemiparesis) occur after an individual has a stroke, and these impairments primarily affect one side of the body more than the other. Predictive musculoskeletal modeling presents an opportunity to investigate how a specific impairment affects gait performance post-stroke. Therefore, the aims of our project are to use to predictive simulation to quantify the spatiotemporal asymmetries and changes to metabolic cost that emerge when muscle strength is unilaterally reduced. We used OpenSim Moco with modified sagittal-plane musculoskeletal models to better understand the relationship between unilateral muscle weakness, gait asymmetry and metabolic cost.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Russell Johnson,James Finley,Nicholas Bianco","trove_cats":[{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"2275","unix_group_name":"arms_hand_model","modified":"1640899730","downloads":"546","group_name":"ARMS Lab hand and wrist model","logo_file":"arms_hand_model","short_description":"","long_description":"The project releases the ARMS Lab dynamic musculoskeletal model of the human hand and wrist, implemented in OpenSIM. Please see the model summary for details of the new model and its use. We include tutorials to perform simulations of maximal grip strength, maximal pinch strength, active hand opening, and passive hand opening with this model. In order to respect the time and effort put in by the original developers please carefully read accompanying publications and cite appropriate references in future work. The links to the left contain all the files (Downloads) and documentation (Documents) related to the model.\n\nPlease cite the following paper:\n- The accompanying publication is currently under review for publication.\n- A pre-print of the manuscript is available on biorxiv, citation information will be updated as the peer review process is completed.\n\nIn the meantime, please cite the biorxiv pre-print: \nD. C. McFarland, B. I. Binder-Markey, J. A. Nichols, S. J. Wohlman, M. de Bruin, and W. M. Murray, &quot;A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks,&quot; bioRxiv, p. 2021.12.28.474357, 2021, doi: 10.1101/2021.12.28.474357.\n\n\nAbout the model:\nThis model of the hand and wrist includes 23 independent degrees of freedom (DOF) including a flexion/extension DOF for each interphalangeal joint of the four fingers and thumb, flexion/extension and ab-adduction DOFs for each metacarpophalangeal joint of the fingers, a flexion/extension DOF for the metacarpophalangeal joint of the thumb, flexion/extension and ab-adduction DOFs for the carpometacarpal thumb joint, a coupled flexion DOF for the carpometacarpal  joints of the ring and little finger, and flexion/extension and radial/ulnar deviation DOFs for the wrist. The model includes passive joint properties for all flexion/extension DOFs of the phalanges and thumb, for carpometacarpal ab-adduction of the thumb, and for wrist flexion and deviation DOFs. Forty-three Hill-type muscle-tendon actuators representing the intrinsic muscles of the hand, the extrinsic muscles of the hand, and the primary wrist muscles are included in the model. The kinematics of each joint and the force-generating parameters for each muscle were derived from experimental data. We include tutorials to perform simulations of maximal grip strength, maximal pinch strength, active hand opening, and passive hand opening. An optimal control theory framework that combines forward-dynamics simulations with a simulated-annealing optimization is used to simulate maximum grip and pinch force.\n\nThe model’s maximum grip force production match experimental measures of grip force, force distribution amongst the digits, and displays sensitivity to wrist flexion. Simulated lateral pinch strength falls within variability of in vivo palmar pinch strength data. The active and passive hand opening simulations predict reasonable activations and demonstrated passive motion mimicking tenodesis, respectively.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Daniel McFarland,Wendy Murray,Jennifer Nichols,Benjamin Binder-Markey","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2277","unix_group_name":"3link","modified":"1641236139","downloads":"0","group_name":"3 link planar arm","logo_file":"","short_description":"its a 3 link planar arm that will help us in a physiotherapy project","long_description":"its a 3 link planar arm that will help us in a physiotherapy project","has_downloads":false,"keywords":"","ontologies":"","projMembers":"DARSHANA NAGALE","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2278","unix_group_name":"estm_acl_force","modified":"1693460068","downloads":"0","group_name":"Estimation of forces on anterior cruciate ligament in dynamic activities","logo_file":"","short_description":"In this work, a nonlinear strain rate dependent plugin developed for the OpenSim® platform was used to estimate the instantaneous strain rate (ISR) and the forces on the ACL’s anteromedial (aACL) and posterolateral (pACL) bundles during walking and sud","long_description":"In this work, a nonlinear strain rate dependent plugin developed for the OpenSim® platform was used to estimate the instantaneous strain rate (ISR) and the forces on the ACL’s anteromedial (aACL) and posterolateral (pACL) bundles during walking and sudden change of direction of running termed as ‘plant-and-cut’ (PC). The authors obtained the kinematics data for walking via optical motion capture. PC movements, along with running kinematics, were obtained from the literature. A nonlinear plugin developed for ligaments was interfaced with the OpenSim® platform to simulate walking and PC motions with a flexed knee and an extended knee. PC phase is sandwiched between an approach phase and take-off phase and was studied at various event velocities (1.8, 3, and 4.2 m s−1), and angles of PC (23°, 34°, and 45°) as encountered in adult ball games. In both cases of PC-with-extended knee and PC-with-flexed-knee, the maximum forces on both the ACL bundles were observed after the take-off phase. A maximum force of ~ 35 N kg−1 of body weight (BW) was observed on aACL after the take-off phase for an event velocity of 4.2 m s−1. In the posterolateral bundle (pACL), the maximum forces (~ 40 N kg−1 of BW) were observed towards the end of the mid-swing phase (after the take-off phase) for the various combinations of the parameters studied. The forces observed in the simulation of PC-with-flexed-knee and PC-with-extended-knee have resulted in magnitude higher than sustainable by the adults. This study is novel in attempting to incorporate differing rates of strain that have been shown to alter soft tissue properties into the OpenSim® musculoskeletal model. The proposed model can be used by researchers to predict the forces during various kinematic activities for other soft tissues.\nKeywords","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Arnab Sikidar","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2279","unix_group_name":"knee-oa-age-imu","modified":"1642016280","downloads":"60","group_name":"Differences in gait patterns across age and knee OA status using IMU data","logo_file":"","short_description":"","long_description":"Common in-lab, marker-based gait analyses may not represent daily, real-world gait. Real-world gait analyses are feasible using inertial measurement units (IMUs), but estimating traditional gait kinematics (e.g., joint angles) from IMU data is challenging. Recent advancements in open-source methods (e.g., OpenSense) may enable reliable and repeatable estimation of joint angles. Before using OpenSense to study real-world gait, we must determine whether these methods: (1) estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and (2) differentiate groups with clinically different gait mechanics. In this project, we compared inverse kinematics calculated using IMU- and marker-based data across young adults, healthy older adults, and older adults with knee osteoarthritis.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jocelyn Hafer,Russell Johnson,Andrew Hunt,Julien Mihy","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2280","unix_group_name":"predictsim_mtp","modified":"1642038391","downloads":"46","group_name":"3D Predictive Simulations of Walking - Impact of Modeling the Toes","logo_file":"","short_description":"This project contains code and data to perform 3D muscle-driven predictive simulations of walking.","long_description":"This repository contains code and data to generate 3D muscle-driven predictive simulations of human walking as described in: Falisse et al., 'Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking', PLOS One (2022).\n\nThis repository can also be found on GitHub (https://github.com/antoinefalisse/predictsim_mtp), where we recommend users to ask questions and report bugs.\n\nPlease note that this code was mainly developed on Windows and will fail on other platforms. It is nevertheless minimal change to make it work on Linux or Mac, so please let us know if this can be useful for you.\n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Friedl De Groote,Maarten Afschrift","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2282","unix_group_name":"xosoftamiapi","modified":"1641925337","downloads":"0","group_name":"AssistanceEvaluation","logo_file":"","short_description":"Gait analysis and evaluation of a quasi-passive exosuit","long_description":"Gait analysis and evaluation of a quasi-passive exosuit","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Vasco Fanti","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2283","unix_group_name":"3dvideotrans","modified":"1643035745","downloads":"0","group_name":"Tool for obtaining OpenSim kinematics based on 3D video analysis","logo_file":"","short_description":"The purpose of this project is to develop a tool for obtaining input OpenSim kinematics based on 3D video analysis.","long_description":"The purpose of this project is to develop a tool for obtaining input OpenSim kinematics based on 3D video analysis.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ting Long,YULIN ZHOU","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2285","unix_group_name":"mech2020","modified":"1698180996","downloads":"0","group_name":"MSK model validation dataset: Muscle mechanics and energetics of hopping","logo_file":"","short_description":"Dataset for OpenSim model and tool testing. Includes experimental motion capture, fascicle length, electromyography, and indirect calorimetry data.","long_description":"Dataset for OpenSim model and tool testing. Data from 8 participants who each performed up to 19 hopping trials to different frequency and height constraints. Go to Downloads &gt; Data Share &gt; MSK model validation dataset\n\nContains: \n- experimental marker (.trc / .c3d) and force (.mot / .c3d) data\n- experimental indirect calorimetry data (.xml / .mat)\n- experimental fascicle length data from soleus, lateral gastrocnemius and vastus lateralis \n(.mat)\n- experimental electromyography data from soleus, lateral gastrocnemius, medial gastrocnemius, tibialis anterior, vastus lateralis, rectus femoris and biceps femoris (.mat)\n- base scaling, IK and ID setup files and base model (https://simtk.org/projects/model-high-flex) which have been adapted to complement the experimental marker set and force data\n* data that are in .mat format are readable files in MATLAB or in Python via 'scipy.'\n\nJessup LN, Kelly LA, Cresswell AG, Lichtwark GA. 2023 Validation of a musculoskeletal model for simulating muscle mechanics and energetics during diverse human hopping tasks. R. Soc. Open Sci. https://doi.org/10.1098/rsos.230393","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Luke Jessup,Glen Lichtwark","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2287","unix_group_name":"kinematic_mri","modified":"1643042880","downloads":"0","group_name":"Kinematic Tracking of Wrist Carpal Bones Using 4D MRI","logo_file":"","short_description":"In this project, heavily under-sampled and fat-saturated 3D Cartesian MRI acquisition were used to capture temporal frames of the unconstrained moving wrist of 5 healthy subjects. ","long_description":"MRI data was collected on a GE HealthCare Signa Premier 3T MRI scanner using a 16-channel large flex coil and using 3D LAVA Flex sequence. Healthy subjects with no prior reported wrist pathology were placed in the MRI bore in a prone &quot;superman&quot; position. No motion-restriction constraints were utilized. Instead, visual cues were used to pace the radial-ulnar motion during the 103 seconds acquisition duration. \n4D dynamic MRI was utilized to analyze individual carpal bone dynamic trajectories within an asymptomatic subject cohort.  Static images were acquired with 0.9×0.9×1 mm3 voxel size and an acquisition matrix size of 224×224×60.  40 dynamic sub-volumes with a temporal resolution of 2.57s were acquired using multi-phase 3D LAVA Flex series with 1.6×1.6×2.5 mm3 voxel size with 128×128×12 acquisition matrix size. \n\n","has_downloads":false,"keywords":"MRI-based modeling,carpal database, kinematics model,Image registration,wrist motion,Musculoskeletal","ontologies":"","projMembers":"Mohammad Zarenia","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2292","unix_group_name":"amputee_gait","modified":"1643235995","downloads":"0","group_name":"BME4504","logo_file":"","short_description":"Amputee gait analysis","long_description":"Amputee gait analysis","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emma Burkhardt","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2294","unix_group_name":"sitting","modified":"1643317227","downloads":"0","group_name":"Modeling and evaluating sitting-on-the-floor postures","logo_file":"","short_description":"In Asia, people tend to sit on the floor while doing their daily work. In this project, it will look into the physical mechanic of every posture uses while sitting on the floor.","long_description":"In Asia, people tend to sit on the floor while doing their daily work. In this project, it will look into the physical mechanic of every posture uses while sitting on the floor.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"NOORAZIAH AHMAD","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2297","unix_group_name":"bos","modified":"1643480660","downloads":"0","group_name":"Biomechanics of Sports","logo_file":"","short_description":"Biomechanics of Sports","long_description":"Biomechanics of Sports","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Uljan S","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2298","unix_group_name":"spinekinematics","modified":"1643703594","downloads":"0","group_name":"A Dynamic Optimization Approach for Solving Spine Kinematics","logo_file":"","short_description":"This study aims to propose a new optimization framework for solving spine kinematics based on skin-mounted markers and estimate subject-specific mechanical properties of the intervertebral joints.","long_description":"The proposed dynamic optimization framework aimed to solve spine kinematics based on 3D skin-marker positions while simultaneously calibrating spine stiffness. Previously used methods and models for solving spine kinematics are commonly static-based and are constrained with strict kinematics bounds. Our proposed optimization approach enforces dynamic consistency in the entire skeletal system and over the entire time-trajectories, as well as the subject-specific nonlinear properties of joint stiffness. Thereby the approach prevents unrealistic joint motions and kinematic inconsistencies caused by uncertainties in body segment parameters and experimental measurement errors.\n\nMore details can be found here:\nhttps://link.springer.com/article/10.1007/s10439-021-02774-3","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Wei Wang","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2299","unix_group_name":"zoon_mehshi","modified":"1643607902","downloads":"0","group_name":"design of a exoskeleton.","logo_file":"","short_description":"This report outlines the need of assistance for a child with dysfunctional knee\njoint. We present the design, fabrication and control of knee exoskeleton to enhance\nthe strength and endurance to impaired knee joint.\nWhen human knee is viewed from a mod","long_description":"This report outlines the need of assistance for a child with dysfunctional knee\njoint. We present the design, fabrication and control of knee exoskeleton to enhance\nthe strength and endurance to impaired knee joint.\nWhen human knee is viewed from a modelling standpoint, the lower limb\nmotion is essentially a rocking motion. This report documents the details of selection,\nsynthesis and analysis of the mechanism. In particular, a slider crank mechanism is\nselected to imitate the dominant motion of knee joint, extension and flexion.\nA CAD model of the knee exoskeleton is developed later which incorporated\nthe housing for lead screw, DC motor, microcontroller and the motor driver. An\naccelerometer is placed on the chest of a child to record the gait cycle.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mahshida hamid","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2300","unix_group_name":"seans-thesis","modified":"1676505600","downloads":"0","group_name":"Prediction of Mechanics from Ultrasound Image","logo_file":"","short_description":"Location for storage and development of CSU master thesis. \nStudent: Sean Doherty\nAdvisor: Ahmet Erdemir","long_description":"Location for storage and development of CSU master thesis, focusing on prediction of tissue mechanics and deformation based on ultrasound image analysis. ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ahmet Erdemir,Sean  Doherty","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2302","unix_group_name":"quant_uncertain","modified":"1653852265","downloads":"53","group_name":"Quantifying uncertainty in inverse analyses from marker-based motion capture","logo_file":"quant_uncertain","short_description":"Scripts for quantifying uncertainty in inverse analyses from marker-based motion capture due to errors in marker registration and model scaling.","long_description":"Estimating kinematics from optical motion capture with skin-mounted markers, referred to as an inverse kinematic (IK) calculation, is the most common experimental technique in human motion analysis. Kinematics are often used to diagnose movement disorders and plan treatment strategies. In many such applications, small differences in joint angles can be clinically significant. Kinematics are also used to estimate joint powers, muscle forces, and other quantities of interest that cannot typically be measured directly. Thus, the accuracy and reproducibility of IK calculations are critical. In this work, we isolate and quantify the uncertainty in joint angles, moments, and powers due to two sources of error during IK analyses: errors in the placement of markers on the model (marker registration) and errors in the dimensions of the model's body segments (model scaling). We demonstrate that IK solutions are best presented as a distribution of equally probable trajectories when these sources of modeling uncertainty are considered. Notably, a substantial amount of uncertainty exists in the computed kinematics and kinetics even if low marker tracking errors are achieved. For example, considering only 2 cm of marker registration uncertainty, peak ankle plantarflexion angle varied by 15.9°, peak ankle plantarflexion moment varied by 26.6 N·m, and peak ankle power at push off varied by 75.9 W during healthy gait. This uncertainty can directly impact the classification of patient movements and the evaluation of training or device effectiveness, such as calculations of push-off power. We provide scripts in OpenSim so that others can reproduce our results and quantify the effect of modeling uncertainty in their own studies.\n\nPlease cite the following publication:\n\nUchida TK*, Seth A*. Conclusion or Illusion: Quantifying uncertainty in inverse analyses from marker-based motion capture due to errors in marker registration and model scaling. Frontiers in Bioengineering and Biotechnology 10: 874725, 2022 (*co-first authors). https://doi.org/10.3389/fbioe.2022.874725","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Thomas Uchida,Ajay Seth","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2304","unix_group_name":"gwea","modified":"1644862034","downloads":"0","group_name":"Garbage worker ergonomic assessment","logo_file":"","short_description":"Ergonomic assessment of the task of handling a trash can","long_description":"Ergonomic assessment of the task of handling a trash can","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thomas Geier","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2311","unix_group_name":"tms_toolbox","modified":"1645737015","downloads":"0","group_name":"Transcranial Magnetic Stimulation (TMS) Analysis ToolBox","logo_file":"tms_toolbox","short_description":"TMS Analysis ToolBox is user friendly matlab based toolbox with a graphical user interface that can perform basic and advanced analyses of common TMS related outcomes on individual or averaged signal TMS trials.","long_description":"TMS Analysis ToolBox is user friendly matlab based toolbox with a graphical user interface that can perform basic and advanced analyses of common TMS related outcomes on individual or averaged signal TMS trials (e.g. MEP latency/amplitudes, silent periods (duration and % decrease), input/output curves (sigmoidal fitting and area under the curve), paired-pulse ratios, and EMG onset detection. The toolbox imports whole multi-channel files and time-locks the data based on comments, data blocks, or thresholds (e.g. TTL). Further, the toolbox allows for easy organization of data and allows interactive analysis for data reduction and outcome detection for immediate visualization and exporting of results for second level analyses. TMS analysis toolbox currently supports file exports from: LabChart, Brain Vision, AcqKnowledge, Signal, Spike and Brainsight. \n\nFor more information, basic tutorial and/or to provide data from alternate data acquisition software for inclusion, please contact: David Cunningham, PhD (dxc536@case.edu). The software is also available from our github page: https://github.com/CunninghamLab/TMSAnalysisToolBox","has_downloads":false,"keywords":"Transcranial Magnetic Stimulation","ontologies":"","projMembers":"David Cunningham","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":true,"is_data":false},{"group_id":"2312","unix_group_name":"gait-hip-exo","modified":"1678136194","downloads":"152","group_name":"Gait Model with Hip Exoskeleton","logo_file":"gait-hip-exo","short_description":"The goal of this project is to develop a gait model that accounts for human-exoskeleton interface dynamics and can form a base for analysis of motion capture studies with hip exoskeletons.","long_description":"Human experiments with hip exoskeletons often depend on on-board sensing to obtain estimates of joint angle, which may be affected by relative motion between the exoskeleton and the wearer. This model adapts a musculoskeletal gait model to include a hip exoskeleton which can be tracked separately from the human wearer with marker-based motion capture. The modeled exoskeleton represents a hip exoskeleton developed at the Human Robot Systems Laboratory at UMass Amherst under the direction of Dr. Meghan Huber. \n\nThe modeled exoskeleton has two internal degrees of freedom common to many current hip exoskeletons: 1) The actuated motor angle, corresponding with hip flexion, and 2) A pin joint connecting the motor to the waist harness which passively allows hip ab/adduction. This model was defined so that the rigid body dynamics are generalizable to exoskeletons in use by other research groups.\n\nWe have also included a sample marker set used in our IROS 2022 and ICRA 2023 studies, which allows for the human kinematics and exoskeleton kinematics to be computed separately, as well as the relative motion between them.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Banu  Abdikadirova,jonaz Moreno ,Mark Price,Meghan Huber","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2313","unix_group_name":"split-belt-gait","modified":"1678135942","downloads":"49","group_name":"Minimum Effort Simulations of Split Belt Treadmill Walking","logo_file":"split-belt-gait","short_description":"This project is designed to provide a public resource for supporting simulation work with split belt treadmills or other asymmetric ground contact conditions such as variable surface stiffness. ","long_description":"This project is designed to provide a public resource for supporting simulation work with split belt treadmills or other asymmetric ground contact conditions such as variable surface stiffness. Walking on single- or dual-belt treadmills can be simulated by assigning ground contact geometry to moving bodies controlled by coordinate actuators and assigning tracking goals to these walking platforms. Project includes OpenSim models and Moco scripts to enable optimal control simulations of split belt treadmill walking.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Wouter Hoogkamer,Mark Price,Meghan Huber","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2320","unix_group_name":"treadmill_gait","modified":"1674923114","downloads":"146","group_name":"Framework for Predictive Simulation of Treadmill Gait","logo_file":"treadmill_gait","short_description":"Developed and validated a predictive simulation framework for treadmill gait using direct collocation methods via OpenSim Moco.","long_description":"This project was divided into two tasks:\n(1) We created a simple model of a block on a treadmill to understand how to develop a framework to track and predict motion between a moving platform and a body moving relative to it. We simulated the block falling, rotating, and translating to mimic heel strike, heel rocker, and translation of the foot posteriorly with respect to the treadmill.\n\n(2) Modified the example2DWalking musculoskeletal model and MATLAB code to track and predict treadmill gait at slow, comfortable, and fast belt speeds.\n\nWhat is included in the download:\n(1) Block Model\n- Model files (.osim) - note model file is the same for the translation &amp; falling simulations, \n   but slightly different for rotation, so there are 2 different model files\n- Manually generated reference coordinates data (.sto) for each tracking problem\n- MATLAB scripts (.m) written to track &amp; predict each block motion\n\n\n(2) Treadmill Gait Model\n- Model files (.osim) - note the treadmill speed is defined in the model so the model files \n   are different for each speed condition, so there are 3 different model files\n- Reference coordinates data for tracking problems (.sto)\n- One MATLAB script to track &amp; predict treadmill gait (.m)- note: this script asks the user to \n   select their model file from the current folder, so just be sure to select the desired speed \n   condition\n- Solutions generated from tracking &amp; predictive problems for all three speeds\n\nNote: To perform comparison with the overground gait simulation described in the manuscript run the example2DWalking code in the OpenSim Moco download.\n\n\n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Kayla Pariser,Jill Higginson","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"417","fullname":"Educational and Training Material"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":false},{"group_id":"2321","unix_group_name":"sport_kinematic","modified":"1646838464","downloads":"0","group_name":"Sport Kinematic","logo_file":"","short_description":"Human body kinematic during sport","long_description":"Human body kinematic during sport","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Stefan Grieder","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2322","unix_group_name":"run_energetics","modified":"1651129712","downloads":"101","group_name":"Running in the wild: energetics explain ecological running speeds","logo_file":"","short_description":"This is a data and code repository for the following manuscript (in Press):\n\nJC Selinger, JL Hicks, RW Jackson, CM Wall-Scheffler, D Chang, and SL Delp. Running in the Wild: Using large-scale wearable data to understand ecological running speed preferences. Current Biology (2022).","long_description":"Human runners have long been thought to have the ability to consume a near constant amount of energy per distance traveled regardless of speed, allowing speed to be adapted to particular task demands with minimal energetic consequence. However, recent and more precise laboratory measures indicate that humans may in fact have an energy-optimal running speed. Here we characterize runners’ speeds in a free-living environment and determine if preferred speed is consistent with task or energy dependent objectives. \n\nWe analyzed data from anonymized runners using the Lumo Run wearable device (Lumo Bodytech Inc.), in combination with pooled laboratory data of running energetics [1,2,3], to answer two questions. First, do runners adapt their preferred speed for different distance tasks? If minimizing cost of transport is not a dominant objective, and runners instead tailor their preferred speed to the task (for example minimizing time across run distance), we might expect faster paces for shorter distances and slower paces for longer distances. Second, are runners’ preferred speeds energy optimal? If minimizing cost of transport is a dominant objective, we expect preferred running speeds to be unaffected by the task (run distance) and also consistent with speeds that minimize cost of transport.  \n\nWe found that individual runners preferred a particular speed that did not change across commonly run distances. We compared data from lab experiments that measured participants’ energy-optimal running speeds to the free-living preferred speeds of age- and gender-matched runners in our dataset and found the speeds to be indistinguishable. Human runners prefer a particular running speed that is independent of task distance, and consistent with the objective of minimizing energy expenditure.\n\n[1] Steudel-Numbers KL, Wall-Scheffler CM. Optimal running speed and the evolution of hominin hunting strategies. Journal of Human Evolution 2009;56:355–60.\ndoi:10.1016/j.jhevol.2008.11.002.\n\n[2]\tRathkey JK, Wall-Scheffler CM. People choose to run at their optimal speed. Am J Phys Anthropol 2017:1–9. \ndoi:10.1002/ajpa.23187.\n\n[3]\tWillcockson MA, Wall-Scheffler CM. Reconsidering the effects of respiratory constraints on the optimal running speed. Med Sci Sports Exerc 2012;44:1344–50. doi:10.1249/MSS.0b013e318248d907.\n","has_downloads":true,"keywords":"running,energetics,wearable sensors,big data","ontologies":"","projMembers":"Jennifer Hicks,Scott Delp,JESSICA SELINGER","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"},{"id":"1006","fullname":"Biomechanics of Movement"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"2324","unix_group_name":"msense_ms_adls","modified":"1647445143","downloads":"0","group_name":"Daily Life Activities in Person with Multiple Sclerosis","logo_file":"","short_description":"A wearable sensor dataset featuring data collected from 38 persons with multiple sclerosis (PwMS), 21 of which are identified as fallers and 17 as non-fallers based on 6 month fall history. Both in lab and remote data are available.","long_description":"In order to explore fall risk and performance of daily life activities, we introduce a new open-source dataset featuring data collected from 38 persons with multiple sclerosis (PwMS), 21 of which are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from several body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month repeat assessment (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Brett Meyer","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2333","unix_group_name":"grad_proj","modified":"1648147783","downloads":"0","group_name":"Joint Angle and CGA position analysis","logo_file":"","short_description":"Analyzing kinematic data to detect alleviation of crouch gait symptoms in spastic diplegic patients.","long_description":"Analyzing kinematic data to detect alleviation of crouch gait symptoms in spastic diplegic patients.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jack Snodgrass","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2335","unix_group_name":"uw_cspine","modified":"1649169801","downloads":"50","group_name":"University of Waterloo Cervical Spine Model","logo_file":"","short_description":"A 24 degree-of-freedom cervical spine model:\n\n\nBarrett, J. M., McKinnon, C. D., Dickerson, C. R., & Callaghan, J. P. (2021). An Electromyographically Driven Cervical Spine Model in OpenSim. Journal of Applied Biomechanics, 37(5), 481-493.","long_description":"A 24 degree-of-freedom cervical spine model:\n\n\nBarrett, J. M., McKinnon, C. D., Dickerson, C. R., &amp; Callaghan, J. P. (2021). An Electromyographically Driven Cervical Spine Model in OpenSim. Journal of Applied Biomechanics, 37(5), 481-493.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Jeff Barrett","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2339","unix_group_name":"swing-gait","modified":"1649220004","downloads":"0","group_name":"Swing Phase","logo_file":"","short_description":"Swing phase of gait","long_description":"Swing phase of gait","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emily Mende","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2343","unix_group_name":"group_activity","modified":"1650152947","downloads":"0","group_name":"Muscoskeletal Model","logo_file":"","short_description":"Develop a 3D muscoskeletal model","long_description":"Develop a 3D muscoskeletal model","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ayushi Sharma","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2347","unix_group_name":"tka-model-vvuq","modified":"1650396074","downloads":"0","group_name":"tka: educational computational model and VVUQ","logo_file":"","short_description":"I would like to create an educational example of CM&S and VVUQ of Total Knee arthroplasty, in order to lower barriers for in silico medicine.","long_description":"I would like to create an educational example of CM&amp;S and VVUQ of Total Knee arthroplasty, in order to lower barriers for in silico medicine.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Els De Swerdt","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2349","unix_group_name":"multicriteria","modified":"1658092965","downloads":"45","group_name":"Simulation-based multi-criteria comparison of exoskeletons","logo_file":"multicriteria","short_description":"we present a simulation-based multi-criteria design approach to systematically study the effect of different device kinematics and corresponding optimal assistive torque profiles under actuator saturation on the metabolic cost, muscle activation, and joint reaction forces of subjects walking under different loading conditions.","long_description":"Wearable robotic assistive devices possess the potential to improve the metabolic efficiency of human locomotion. Developing exoskeletons that can reduce the metabolic cost of assisted subjects is challenging, since a systematic design approach is required to capture the effects of device dynamics and the assistance torques on human performance. Conducting such investigations through human subject experiments with physical devices is generally infeasible.\n\nOn the other hand, design studies that rely on musculoskeletal models hold high promise in providing effective design guidelines, as the effect of various devices and different assistance torque profiles on muscle recruitment and metabolic cost can be studied systematically.\n\nIn this paper, we present a simulation-based multi-criteria design approach to systematically study the effect of different device kinematics and corresponding optimal assistive torque profiles under actuator saturation on the metabolic cost, muscle activation, and joint reaction forces of subjects walking under different loading conditions. For the multi-criteria comparison of mono-articular and bi-articular exoskeletons, we introduce a Pareto optimization approach to simultaneously optimize the exoskeleton power consumption and the human metabolic rate reduction during walking, under different loading conditions. We further superpose the effects of device inertia and electrical regeneration on the metabolic rate and power consumption, respectively.\n\nOur simulation results explain the effects of heavy loads on the optimal assistance profiles of the exoskeletons and provide guidelines on choosing optimal device configurations under actuator torque limitations, device inertia, and regeneration effects. \n\nThe multi-criteria comparison of devices indicates that despite the similar assistance levels that can be provided by both types of exoskeletons, mono-articular exoskeletons demonstrate better performance on reducing the peak reaction forces, while the power consumption of bi-articular exoskeletons is less sensitive to the loading. Furthermore, for the bi-articular exoskeletons, the device inertia has lower detrimental effects on the metabolic cost of subjects and does not affect the Pareto-optimality of solutions, while non-dominated configurations are significantly affected by the device inertia for the mono-articular exoskeletons.\n\nBonab, A.K. and Patoglu, V., 2021. Simulation-based multi-criteria comparison of mono-articular and bi-articular exoskeletons during walking with and without load. arXiv preprint arXiv:2110.00062.","has_downloads":true,"keywords":"simulation-based assistive device design,Physical human-robot interaction,exoskeleton design,multi-criteria design optimization,musculoskeletal simulations","ontologies":"","projMembers":"Ali Khalilianmotamed Bonab","trove_cats":[{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"2367","unix_group_name":"pitching","modified":"1652237285","downloads":"0","group_name":"pitching","logo_file":"","short_description":"pitching mechanics efficiency","long_description":"pitching mechanics efficiency","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Brett Parker","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2371","unix_group_name":"mocointeraction","modified":"1653065683","downloads":"0","group_name":"Simulations with OpenSim Moco and human-exoskeleton interaction model","logo_file":"","short_description":"The purpose of this project is to perform predictive simulations using OpenSim Moco and models of interaction between humans and exoskeleton robots, in order to generate useful data for the design of interaction controls applied to robotic neurorehabilita","long_description":"The purpose of this project is to perform predictive simulations using OpenSim Moco and models of interaction between humans and exoskeleton robots, in order to generate useful data for the design of interaction controls applied to robotic neurorehabilitation of stroke victims.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Denis César Mosconi Pereira","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2372","unix_group_name":"manuscript1","modified":"1653065737","downloads":"0","group_name":"Fluid Mechanics of the Zebrafish Embryonic Heart Trabeculation","logo_file":"","short_description":"Images and Simulation files associated with publication","long_description":"Images and Simulation files associated with publication","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Choon Hwai Yap","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2373","unix_group_name":"foot-orthoses","modified":"1657230164","downloads":"45","group_name":"A Method for Quantifying Stiffness of Ankle-Foot Orthoses","logo_file":"foot-orthoses","short_description":"Adjustments of Ankle-Foot Orthosis (AFO) stiffness is commonly prescribed for people with neurologically impaired to improve walking. It is important to quantify AFO stiffness levels to provide consistent patient-specific settings. We propose the Ankle As","long_description":"Adjustments of Ankle-Foot Orthosis (AFO) stiffness is commonly prescribed for people with neurologically impaired to improve walking. It is important to quantify AFO stiffness levels to provide consistent patient-specific settings. We propose the Ankle Assistive Device Stiffness (AADS) test method, a simple design jig using motion capture system and musculoskeletal analysis software (OpenSim).  The collected marker trajectory data were imported to OpenSim to calculate AFO dorsiflexion angle using inverse kinematics. Then a static optimization algorithm was used to identify external forces from operators and the AFO torque best matching the experimentally collected ground reaction force data. Estimated AFO moments were compared within the operators’ trials and with theoretically calculated AFO moments to evaluate the accuracy of AADS tests. This Project will include the model of AFO and ADDS and the scaling, IK and SOP setup sample.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Sepehr Ramezani,Hwan Choi,Brian Brady","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2380","unix_group_name":"sb_force","modified":"1666635859","downloads":"44","group_name":"Tibial forces in independently ambulatory children with spina bifida","logo_file":"sb_force","short_description":"Bone strength data, kinematic and kinetic overground walking data, and simulation results from 16 independently ambulatory children with spina bifida and 16 age- and sex-matched children with typical development.","long_description":"Experimental motion capture and bone strength data and simulation results from 16 independently ambulatory children with spina bifida and 16 age- and sex-matched children with typical development. Additional motion capture and EMG data and simulation results for 6 independently ambulatory children with spina bifida and 1 child with typical development. Custom scripts were used to calculate joint kinematics, moments, and forces. Post-simulation analyses were conducted to compare these waveforms between the group with spina bifida and the group with typical development.\n\nThe manuscript using these data and simulations can be found here:\nLee MR, Hicks JL, Wren TAL, and Delp SL (2022). Independently ambulatory children with spina bifida experience near-typical knee and ankle joint moments and forces during walking. Gait and Posture, 99:1-8. https://doi.org/10.1016/j.gaitpost.2022.10.010","has_downloads":true,"keywords":"joint force,bone,joint moment,musculoskeletal simulation,myelomeningocele,spina bifida","ontologies":"","projMembers":"Jennifer Hicks,Scott Delp,Tishya Wren,Marissa Lee","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":false,"is_application":true,"is_data":true},{"group_id":"2381","unix_group_name":"synesttrunkact","modified":"1654716310","downloads":"0","group_name":"Estimation of Trunk Muscle Activations Using Lower Extremity Muscle Synergies","logo_file":"","short_description":"Estimation of Trunk Muscle Activations Using Lower Extremity Muscle Synergies","long_description":"Estimation of Trunk Muscle Activations Using Lower Extremity Muscle Synergies","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Geng Li","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2383","unix_group_name":"falling","modified":"1655230148","downloads":"0","group_name":"falling","logo_file":"","short_description":"I want to create a simulation of a human falling down","long_description":"I want to create a simulation of a human falling down","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Evelien Fleerakkers","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2384","unix_group_name":"csdataset","modified":"1710721033","downloads":"0","group_name":"Capacitive Sensing for Natural Environment Biomechanics Monitoring","logo_file":"csdataset","short_description":"Capacitive sensing data from 31 participants and code for validating capacitive measurements against traditional measures of gait and applying them for portable kinematics estimation.","long_description":"Link to Code: https://github.com/opearl-cmu/CapacitiveSensingKinematics\n\nThe included codebase illustrates how to use capacitive sensing data within two different \nwearable kinematics algorithms, CSInverseKinematics and CSOptimalControl. It shows how to load raw CS signals, process them, analyze them, learn from them, and predict kinematics with them on their own or in combination with other wearables.\n\nLink to Dataset: https://github.com/opearl-cmu/CapacitiveSensingDataset\n\nThe following dataset comprises data from two experiments. The first dataset includes time-synchronized measurements of (1) muscle bulging acquired via a wearble lower limb capacitive sensing sleeve at the shank, (2) neural excitation measurements from electromyography, and (3) inferred muscle moments from static optimization performed in OpenSim with optical motion capture and instrumented treadmill data. 20 participants were recorded walking normally and with a 5-degree toe-in foot progression angle, a therapeutic modification used to mitigate progression of knee osteoarthritis. Measurements for CS and EMG were taken both inside a traditional motion capture laboratory environment and outside in natural environments.\n\nThe second dataset includes measurements of (1) muscle bulging acquired via wearable lower limb capacitive sensing sleeves located at both the shank and thigh of both legs, (2) neural excitation measurements from electromyography, (3) optical motion capture and instrumented treadmill data, (4) XSens inertial measurement unit data, and (5) magnetic resonance imaging (MRI) body composition scan results. 10 healthy participants were recorded walking normally and with a mock impaired stiff-knee gait, along with 1 total knee arthroplasty patient. Measurements for CS, IMUs, and mocap were taking simultaneously, as well as measurements of EMG, IMUs, and mocap inside of the lab on an instrumented treadmill. The provided dataset enables the comparison of CS data with any biomarker in a consistent OpenSim/MATLAB ready formatting.\n\nPlease cite the following when using this code or data: \nOwen Pearl, Nataliya Rokhmanova, Louis Dankovich, Summer Faille, Sarah Bergbreiter, Eni Halilaj. (2022) Capacitive Sensing for Natural Environment Rehabilitation Monitoring, Nature (under review). https://doi.org/10.21203/rs.3.rs-1902381/v.","has_downloads":false,"keywords":"natural environments,muscle activity,capacitive sensing,biomechanics,wearable sensing,rehabilitation","ontologies":"","projMembers":"Eni Halilaj,Nataliya Rokhmanova,Sarah Bergbreiter,Owen Pearl","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"},{"id":"411","fullname":"Experimental Analysis"}],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":true},{"group_id":"2385","unix_group_name":"opencap","modified":"1711662383","downloads":"366","group_name":"OpenCap","logo_file":"opencap","short_description":"OpenCap is a software package to estimate 3D human movement dynamics from smartphone videos.","long_description":"OpenCap combines computer vision, deep learning, and musculoskeletal simulation to quantify human movement dynamics from smartphone videos. \n\nSee our preprint for more description of OpenCap and our validation experiments:\nUhlrich SD*, Falisse A*, Kidzinski L*, Ko M, Chaudhari AS, Hicks JL, Delp SL, 2022. OpenCap: 3D human movement dynamics from smartphone videos. biorxiv. https://doi.org/10.1101/2022.07.07.499061. *contributed equally\n\n- To start collecting data with OpenCap, visit https://app.opencap.ai.\n- To find more information about OpenCap, visit https://opencap.ai.\n- To find the source code for computing kinematics from videos, visit https://github.com/stanfordnmbl/opencap-core\n- To find code for post-processing OpenCap data and generating dynamic simulations, visit https://github.com/stanfordnmbl/opencap-processing\n\nOpenCap comprises an iOS application, a web application, and cloud computing. To collect data, users open an application on two or more iOS devices and pair them with the OpenCap web application. The web application enables users to record videos simultaneously on the iOS devices and to visualize the resulting 3-dimensional (3D) kinematics. In the cloud, 2D keypoints are extracted from multi-view videos using open-source pose estimation algorithms. The videos are time synchronized using cross-correlations of keypoint velocities, and 3D keypoints are computed by triangulating these synchronized 2D keypoints. These 3D keypoints are converted into a more comprehensive 3D anatomical marker set using a recurrent neural network (LSTM) trained on motion capture data. 3D kinematics are then computed from marker trajectories using inverse kinematics and a musculoskeletal model with biomechanical constraints. Finally, kinetic measures are estimated using muscle-driven dynamic simulations that track 3D kinematics.\n\nThis repository (see Downloads) contains the experimental data used in the validation study. More details on the participant population can be found in our preprint. More details about the specifics of the included data can also be found in the README included in the downloaded folders.\n\n1) Lab Validation Data: \nPopulation and activities: 10 individuals performing four activities (squats, sit-to-stand, drop vertical jump, and walking) with varied kinematic patterns. \nRaw data: Marker-based motion capture, ground reaction forces, electromyography from 10 lower-extremity muscles, RGB video from 5 cameras.\nProcessed data: OpenSim models, inverse kinematics, inverse dynamics, muscle driven simulations.\nWe provide this dataset with and without RGB videos, for file size considerations.\n\n2) Field Study Data:\nPopulation and activities: 100 individuals performing natural and asymmetric squats.\nProcessed data: OpenSim models, inverse kinematics, muscle driven simulations from OpenCap using two cameras. RGB videos are not provided with this dataset, due to the more restrictive IRB protocol that we used for this portion of the study.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Antoine Falisse,Scott Uhlrich,Jennifer Hicks,Scott Delp,Łukasz Kidziński,Matt Petrucci","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2386","unix_group_name":"musc_fe_approx","modified":"1700468286","downloads":"9","group_name":"Muscle constitutive model with a tangent modulus approximation","logo_file":"musc_fe_approx","short_description":"Ansys implementation of a muscle constitutive model that uses an approximation of the tangent modulus.","long_description":"Sophisticated muscle material models are required to perform detailed finite element simulations of soft tissue; however, state-of-the-art muscle models are not among the built-in materials in popular commercial finite element software packages. Implementing user-defined muscle material models is challenging for two reasons: deriving the tangent modulus tensor for a material with a complex strain energy function is tedious and programming the algorithm to compute it is error-prone. These challenges hinder widespread use of such models in software that employs implicit, nonlinear, Newton-type finite element methods. We implement a muscle material model in Ansys using an approximation of the tangent modulus, which simplifies its derivation and implementation. Three test models were constructed by revolving a rectangle (RR), a right trapezoid (RTR), and a generic obtuse trapezoid (RTO) around the muscle's centerline. A displacement was applied to one end of each muscle, holding the other end fixed. The results were validated against analogous simulations in FEBio, which uses the same muscle model but with the exact tangent modulus. Overall, good agreement was found between our Ansys and FEBio simulations, though some noticeable discrepancies were observed. For the elements along the muscle's centerline, the root-mean-square-percentage error in the Von Mises stress was 0.00%, 3.03%, and 6.75% for the RR, RTR, and RTO models, respectively; similar errors in longitudinal strain were observed. We provide our Ansys implementation so that others can reproduce and extend our results.\n\nPlease cite the following publication:\n\nSampaio de Oliveira ML, Uchida TK. Muscle constitutive model with a tangent modulus approximation: Ansys implementation and verification. 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Edited experimental marker trajectories, scaled models, and setup files are also included. All inputs needed to run our static optimization implementation (https://github.com/stanfordnmbl/MatlabStaticOptimization) and replicate our results are included with the download. A README file with more detail about the dataset structure is also included with the download.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Kevin Thomas,Scott Delp,Scott Uhlrich,Julie Kolesar,Janelle Kaneda,Kirsten Seagers","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2396","unix_group_name":"median_pfmodel","modified":"1668551124","downloads":"15","group_name":"Plantarflexor personalisation with group-specific median parameters for CP","logo_file":"","short_description":"The provided code allows creation of a model for typically developing children and children with cerebral palsy, with plantarflexor parameters that are based on group-specific median parameters. 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At this time, we ask that you wait to publish any work that uses the NMSM Pipeline until the journal article reference for the software is available. Please get in touch with us if you have any questions.\n\nIf you need help or want to start a discussion, please use the SimTK forum for this project.\n\nNote: This project is a living entity. Updates will be made available as the Pipeline, examples, and tutorials are developed further and improved.","has_downloads":true,"keywords":"EMG,Biomechanics,opensim,OpenSim,MATLAB,Matlab,matlab,musculoskeletal,neuromuscular,computational modeling,computation,personalization,EMG-driven simulations,neuromuscular,Neuromuscular,neuromuscular control,Neuromuscular control,neuromuscular model,neuromusculoskeletal modelling,NeuroMusculoSkeletal Modeling,neuromusculoskeletal simulation,biomechanics","ontologies":"Software_Distribution,Neuromuscular_Model,Multibody_Dynamics,Software,Mechanical_Simulation","projMembers":"Claire V. Hammond,B.J. 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Hosted by Stanford University.\n\nAvailable at https://addbiomechanics.org","long_description":"Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. 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Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.\n\nWerling, K., Bianco, N.A., Raitor, M., Stingel, J., Hicks, J. L., Collins, S., Delp, S., &amp; Liu, C. K. (2023). AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization. bioRxiv, https://www.biorxiv.org/content/10.1101/2023.06.15.545116v1.\n\nPublication data repository: https://github.com/stanfordnmbl/addbiomechanics-paper","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Keenon Werling,Nicholas Bianco","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2403","unix_group_name":"3d-vh-geometry","modified":"1701274277","downloads":"676","group_name":"3D Models of the Visible Human Male and Female","logo_file":"3d-vh-geometry","short_description":"Complete 3D musculoskeletal geometries were extracted from the National Libraries of Medicine Visible Human Female and Male cryosection images. Muscle, bone, cartilage, ligament, and fat from the pelvis to the ankle were digitized and exported in shareabl","long_description":"Complete 3D musculoskeletal geometries were extracted from the National Libraries of Medicine Visible Human Female and Male cryosection images. Muscle, bone, cartilage, ligament, and fat from the pelvis to the ankle were digitized and exported in shareable formats and made available for download. While a substantial amount of published work has been derived from the Visible Human Project, this is the first time a large number of musculoskeletal 3D geometries are being made available to the public including both male and female specimens. Currently, 260 geometries from the Visible Human Male and Female are available consisting of 76 muscles, 28 bones, 16 cartilages, 8 ligaments, and 2 fat geometries per subject. The library is available at multiple layers of processing and remarkably in a final form with no overlap between individual structures. This library is made available to motivate continued work in multi-scale, high-fidelity musculoskeletal modeling and promote reuse and continued development of the dataset.\n\nSUPPORT\nThis data was made possible by NIH grant U01 AR072989 with combined support from the National Institute for Arthritis, Musculoskeletal, and Skin Diseases (NIAMS), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and the National Institute of Child Health and Human Development (NICHD).\n\nCITATIONS\nAndreassen, T.E., Hume, D.R., Hamilton, L.D. et al. Three Dimensional Lower Extremity Musculoskeletal Geometry of the Visible Human Female and Male. Sci Data 10, 34 (2023). https://doi.org/10.1038/s41597-022-01905-2.\n\nAndreassen, T. E., Hume, D. R., Hamilton, L. D., Higinbotham, S. E. &amp; Shelburne, K. B. An Automated Process for 2D and 3D Finite Element Overclosure and Gap Adjustment using Radial Basis Function Networks. 1–13 (2022) doi: https://doi.org/10.48550/arXiv.2209.06948\n\nTE Andreassen, DR Hume, LD Hamilton, SE Higinbotham, KB Shelburne (in review) “An Automated Process for 2D and 3D Finite Element Overclosure and Gap Adjustment using Radial Basis Function Networks,” Computer Methods and Programs in Biomedicine Update, 2023.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Kevin Shelburne,Sean Higinbotham,Donald Hume,Thor Andreassen,Landon Hamilton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2405","unix_group_name":"fe-tmc-joint","modified":"1666847330","downloads":"5","group_name":"Finite Element Model of the Trapeziometacarpal Joint","logo_file":"","short_description":"We developed a finite element of the trapeziometacarpal joint in FEBio. This model takes user-defined ligament and muscle force properties to calculate the joint contact stress.","long_description":"We developed a finite element of the trapeziometacarpal joint in FEBio. This model takes user-defined ligament and muscle force properties to calculate the joint contact stress.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Meilin Dong","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2414","unix_group_name":"imuopt_no-int","modified":"1662439871","downloads":"1","group_name":"An Integration-free Optimization Method for IMU-based Human Motion Measurement","logo_file":"","short_description":"This study presents a novel integration-free optimization method for measuring human movement using inertial measurement units.","long_description":"Wearable inertial measurement units (IMUs) are a cheaper alternative to video motion capture systems and can measure human movement in any environment. However, the state estimation methods used to convert noisy IMU data into joint kinematic data typically require numerical integration, resulting in significant integration drift. This study presents a novel integration-free nonlinear optimization method for measuring human movement with IMUs. The method utilizes a physics-based kinematic model with joint constraints to provide theoretical relationships between IMU kinematics and joint kinematics and replaces numerical integration with differentiation. It does not require IMU magnetometer data, calculation of IMU orientation in the global reference frame from IMU gyroscope data, or subtraction of the acceleration due to gravity from IMU accelerometer data. The method was evaluated quantitatively using experimental IMU and video motion capture data collected from the pelvis and lower limbs of a healthy subject who performed walking, jogging, and jumping trials. The proposed integration-free optimization method produced average root-mean-square (RMS) errors on the order of 3 deg for walking, 6 deg for jogging, and 12 deg for jumping. With a machine learning enhancement, these errors were reduced to roughly 3 deg for all three movements. In contrast, a standard unscented filter method produced average RMS errors of 18 deg, 19 deg, and 16 deg for the same three movements, respectively. These findings suggest that the proposed integration-free optimization method for estimating joint kinematics from IMU data could potentially be used in place of a video motion capture system for patient assessment when real-time measurement capability is not required.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Anirudh Bhateja,Thor Besier,B.J. Fregly","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2423","unix_group_name":"lumbersupport","modified":"1662463467","downloads":"0","group_name":"Development of lumber and knee protection for mining workers","logo_file":"","short_description":"In mining a worker has to do repetitive task with carrying load. This increases the chances of knee and lumber related injuries in the mining workers. Moreover, during accidents in mining the lumber and knee joints are highly under risk for injuries.\nThe","long_description":"In mining a worker has to do repetitive task with carrying load. This increases the chances of knee and lumber related injuries in the mining workers. Moreover, during accidents in mining the lumber and knee joints are highly under risk for injuries.\nThe project aims to develop a lumber and knee support for the workers in the mines. The primary focus is to make these lumber and knee supports light weight using the fiber reinforced polymer composite laminates. Further, the developed lumber and knee supporters should increase the efficiency of the mining worker for repetitive tasks. Moreover, these protective supporters should reduce the muscoloskeletel risks associated with lumber and knee joints. In the initial stage, we would like see the reactive stresses generated in the muscles during lifting of a weight and repetitive tasks. Based on the results we would like proceed for further analysis and fabricate the lumber and knee supports.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mahesh Shindhe","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2425","unix_group_name":"activewalker","modified":"1662485964","downloads":"0","group_name":"Multiple Sclerosis Patient interacting with Walker","logo_file":"","short_description":"Modelling an \"active walking\" device used by a person with multiple sclerosis. Models force distribution of walker over differing elevations.","long_description":"Modelling an &quot;active walking&quot; device used by a person with multiple sclerosis. Models force distribution of walker over differing elevations.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Michelle Morris","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2434","unix_group_name":"discgolf","modified":"1663620345","downloads":"0","group_name":"Disc Golf Form Optimization","logo_file":"","short_description":"Identify the ideal form for a human to throw a disc golf disc for maximum distance","long_description":"Identify the ideal form for a human to throw a disc golf disc for maximum distance","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Tyler Huttenlocher","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2436","unix_group_name":"ml_sensors","modified":"1706134349","downloads":"52","group_name":"Surrogate modelling from wearable sensors to estimate gait time series","logo_file":"","short_description":"Lower limb joint angles, joint moments, and muscle forces during gait for 17 healthy volunteers (9F, 28±5 yrs) along with raw IMU and EMG data. This dataset can be used to build regression-based machine learning models for the prediction of intended targ","long_description":"This data set includes lower limb joint angles, joint moments, and muscle forces during gait for 17 healthy volunteers (9F, 28±5 yrs), along with raw IMU and EMG data. Joint angles and moments are related to pelvis tilt, pelvis obliquity, pelvis rotation, hip flexion/extension, hip adduction/abduction, hip rotation, knee flexion/extension, ankle dorsi/plantar flexion, and ankle inversion/eversion. EMG surface electrodes were recorded from lower limb muscles' activity on both legs (Gluteus maximus, Rectus femoris, Vastus lateralis, Biceps femoris, Semimembranosus, Medial gastrocnemius, Soleus, and Tibialis anterior). Three-dimensional acceleration (m/s^2) and angular velocity (rad/s) were recorded from 7 IMUs attached to each segment of the lower limbs (one on the pelvis, one on each foot, shank, and thigh). The dataset can be used to build regression-based machine learning models for the prediction of intended targets (Joint angles, joint moments, and muscle forces) by using wearable sensors' data. We built a multi-output random forest (RF) model to predict all targets simultaneously from wearable sensors' data with the aid of the Tsfresh python package for automatic feature extraction. The RF model can predict all targets with a reasonable accuracy that is comparable to the literature.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Shima Mohammadi Moghadam","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2437","unix_group_name":"imu2opensense","modified":"1674588739","downloads":"0","group_name":"IMU to OpenSense Toolbox","logo_file":"","short_description":"This toolbox covers kinematics data collected from different types of IMU sensors to the input data of OpenSense.","long_description":"This toolbox covers kinematics data collected from different types of IMU sensors to the input data of OpenSense.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Qingyao Bian,Ziyun Ding","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2442","unix_group_name":"soip","modified":"1664488758","downloads":"0","group_name":"Spaceflight Orthopedic Implant Project","logo_file":"","short_description":"Investigating the mechanical stress on a bone-plate-screw model during spaceflight to determine its safety.","long_description":"Investigating the mechanical stress on a bone-plate-screw model during spaceflight to determine its safety.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Chloe Jacquet","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2444","unix_group_name":"pastocovac_plus","modified":"1664746147","downloads":"0","group_name":"pastocovac plus® vaccine as a protein subunit booster","logo_file":"","short_description":"This study presents an excellent safety and immunogenicity profile of a subunit protein vaccine from Iran against the current pandemic. Considering the valuable data and significant impact on medical world. According to the recent SARS-CoV-2 variants, boo","long_description":"This study presents an excellent safety and immunogenicity profile of a subunit protein vaccine from Iran against the current pandemic. Considering the valuable data and significant impact on medical world. According to the recent SARS-CoV-2 variants, booster doses are widely needed globally. What is more, according to the new studies, prime-boost strategy is strongly suggested to prevent from COVID-19. Thus, this study will definitely contribute to the associated data as well.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mona Sadat Larijani","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2447","unix_group_name":"fyp","modified":"1665072859","downloads":"0","group_name":"Asymmetry analysis in stair ascent of transtibial amputees","logo_file":"","short_description":"Ascending stairs is a critical activity in maintaining independence in daily living. The loss of the ankle joint following the transtibial amputation inevitably causes the between-limb asymmetry and poses a great challenge of ascending stairs. The project","long_description":"Ascending stairs is a critical activity in maintaining independence in daily living. The loss of the ankle joint following the transtibial amputation inevitably causes the between-limb asymmetry and poses a great challenge of ascending stairs. The project will identify the asymmetric parameters in stair ascend based on MSK modelling and in addition, investigate the relationship between the temporal/spatial asymmetry and the loading asymmetry. The understanding of the biomechanics factors and their contributions to the stair asymmetry will help to focus on the rehabilitation targets for transtibial amputees.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Eva Knight","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2449","unix_group_name":"afo-predictions","modified":"1665592608","downloads":"59","group_name":"Predicting changes in gait mechanics over a range of AFO stiffnesses","logo_file":"","short_description":"Using computational musculoskeletal simulations in OpenSim and SCONE to predict and analyze the changes in gait mechanics over a range of ankle-foot orthosis stiffnesses.","long_description":"To uncover the optimal ankle-foot orthosis (AFO) stiffness, we generated simulations of an individual with calf muscle weakness walking with an AFO over a range of stiffnesses. Stable walking patterns were generated that minimized the energy demand for a given stiffness. \n\nThis project provides results from all of our simulations. We also provide MATLAB code for generating the Results and Supporting Information figures and tables, and SCONE setup files for others to reproduce our results.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Ajay Seth,Bernadett Kiss","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2450","unix_group_name":"kneesegment","modified":"1666537190","downloads":"0","group_name":"Automated Knee MRI Segmentation","logo_file":"kneesegment","short_description":"We introduce an open-source tool for automated subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning.","long_description":"Manual or semi-automated segmentation of cartilage in quantitative MRI scans is a necessary step in assessing early changes in cartilage health. The aim of this work was to develop a fully automated femoral cartilage segmentation model and to evaluate the model's ability to measure subregional T2 values longitudinally. \n\nWhen using the tool, please cite the following paper:\nThomas, Kevin A., Dominik Krzemiński, Łukasz Kidziński, Rohan Paul, Elka B. Rubin, Eni Halilaj, Marianne S. Black, Akshay Chaudhari, Garry E. Gold, and Scott L. Delp. &quot;Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning.&quot; Cartilage 13, no. 1_suppl (2021): 747S-756S.\n\nLink to paper: https://journals.sagepub.com/doi/abs/10.1177/19476035211042406?journalCode=cara\n\nLink to code: https://github.com/kathoma/AutomaticKneeMRISegmentation\n\nThis software provides the following automated functionality for multi-echo spin echo T2-weighted knee MRIs:\n\nSegmentation of femoral cartilage\nProjection of the femoral cartilage onto a 2D plane\nDivision of the projected cartilage into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries\nCalculation of the average T2 value in each subregion\nCalculation of the change in average T2 value over time for each subregion (if 2 imaging time points are available for a given person)\nComparison of results across different readers/models\nFullPipeline.ipynb walks through an example of how to use the full pipeline to analyze individual images, calculate changes in a patient over time, and compare results for segmentations from different readers.\n\nRequires CUDA Version 9.0.176. Tested with CUDA 9.0 and cudnn 7.3.0 in Ubuntu 18.04.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Kevin Thomas,Łukasz Kidziński,Scott Delp","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2453","unix_group_name":"mocap101222","modified":"1665623943","downloads":"0","group_name":"Motion Capture Test 10/12","logo_file":"","short_description":"mocap test for senior design","long_description":"mocap test for senior design","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Catherine Peluso","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2456","unix_group_name":"weav","modified":"1666108141","downloads":"0","group_name":"Developing a Walking Energy Audit from Video","logo_file":"","short_description":"The purpose of this research study is to test the feasibility of a video-based system to estimate the energetic cost of walking. ","long_description":"Various methods exist to estimate energy expenditure (heart rate monitors, accelerometers, and oxygen consumption), but all have drawbacks such as attaching devices, battery life, and long testing durations. We are interested in determining whether video-based movement analysis can reliably estimate the energetic cost of walking from only video input. This may improve the throughput, cost, accuracy, and/or fidelity of current energetic cost measurement systems. Additionally, we will test the accuracy of machine learning algorithms to detect body point locations across various skin tones.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ricky Pimentel","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2458","unix_group_name":"upper_extreme","modified":"1666376115","downloads":"0","group_name":"Upper Extremity - NEIL","logo_file":"","short_description":"For academic research to quantitatively identify if the tremors in stroke patients increase/decrease with physical therapy. We are utilizing IMU sensors and attaching it to a skeletal body (upper extremity) to limit its degree of freedoms.","long_description":"For academic research to quantitatively identify if the tremors in stroke patients increase/decrease with physical therapy. We are utilizing IMU sensors and attaching it to a skeletal body (upper extremity) to limit its degree of freedoms.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Karan Bhula","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2469","unix_group_name":"bme553hw8","modified":"1667414170","downloads":"0","group_name":"bme535biomechanics","logo_file":"","short_description":"grad class","long_description":"grad class","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Allison Smith","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2470","unix_group_name":"musculoskeletal","modified":"1667515987","downloads":"0","group_name":"Musculoskeletal Modeling","logo_file":"","short_description":"BIME 4040 assignment","long_description":"BIME 4040 assignment","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Taylor Everett","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2472","unix_group_name":"balance-exo-sim","modified":"1699900815","downloads":"402","group_name":"Simulating the effect of ankle exoskeleton torques on walking kinematics","logo_file":"balance-exo-sim","short_description":"Simulations to understand the effect of ankle exoskeleton torques on changes in center of mass kinematics during walking.\n\nGitHub repo: https://github.com/stanfordnmbl/balance-exo-sim.","long_description":"Walking balance is central to independent mobility, and falls due to loss of balance are a leading cause of death for people 65 years of age and older. Wearable robotic devices, or exoskeletons, could help people with reduced muscle strength and motor control avoid falls by providing stabilizing torques at lower-limb joints. However, it is currently unclear how exoskeleton torques change walking motions. In this study, we used computer simulation to investigate how exoskeleton torques applied to the ankle change the motion of the body’s center of mass. We first created realistic simulations of walking using a biomechanically accurate model. We then simulated the effect of exoskeleton torques applied to the model that plantarflexed (i.e., extended), inverted, or everted the ankle. We found that plantarflexion torques moved the center of mass backwards or forwards, depending on when the torque was applied during the walking cycle. Plantarflexion torques also moved the center of mass upwards. Eversion and inversion torques produced left-right motions of the center of mass. Finally, we found that the force-generating properties of muscles in our model reduced the center of mass changes from exoskeleton torques. Our results can help exoskeleton designers create devices that stabilize walking balance.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Nicholas Bianco","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2474","unix_group_name":"lit_con","modified":"1670279851","downloads":"0","group_name":"Associating Biological Context with PPIs through Text Mining at PubMed Scale","logo_file":"","short_description":"We demonstrate an approach for enriching text-derived knowledge bases with biological detail by incorporating cell type context into protein-protein interaction networks at PubMed scale","long_description":"Inferring knowledge from known relationships between drugs, proteins, genes, and diseases has great potential for clinical impact, such as predicting which existing drugs could be repurposed to treat rare diseases. Incorporating key biological context such as cell type or tissue of action into representations of extracted biomedical knowledge is essential for principled pharmacological discovery. Existing global knowledge graphs of interactions between drugs, proteins, genes, and diseases lack this essential information. In this study, we frame the task of associating biological context with protein-protein interactions extracted from text as a classification task using syntactic, semantic, and novel meta-discourse features. We introduce the Insider corpora, which are automatically generated PubMed-scale corpora for training classifiers for the context association task. These corpora are created by searching for precise syntactic cues of cell type and tissue relevancy to extracted regulatory relations. We report F1 scores of 0.955 and 0.862 for identifying relevant cell types and tissues, respectively, for our identified relations. By classifying with this framework, we demonstrate that the problem of context association can be addressed using intuitive, interpretable features. We demonstrate the potential of this approach to enrich text-derived knowledge bases with biological detail by incorporating cell type context into a protein-protein network for dengue fever.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Daniel Sosa","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2476","unix_group_name":"gross_vm","modified":"1668102082","downloads":"0","group_name":"Kin 535 Finally Project Gross","logo_file":"","short_description":"Kin 535 Final Project","long_description":"Kin 535 Final Project","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Aidan Gross","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2479","unix_group_name":"valve_replace","modified":"1668546996","downloads":"0","group_name":"Heart Valve Replacement","logo_file":"","short_description":"Understand the blood flow through the aortic valve in order to better design mechanical heart valve replacements.","long_description":"Understand the blood flow through the aortic valve in order to better design mechanical heart valve replacements.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Katherine Ohotin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2483","unix_group_name":"bone_gait_load","modified":"1701264752","downloads":"39","group_name":"How do bony geometries influence our gait and musculoskeletal loading?","logo_file":"","short_description":"The aim of this project is to comprehensively investigate the influence of bony geometries on muscle forces and joint loads during walking. Furthermore, we will investigate reasons for pathological gait and evaluate the influence of clinical interventions on the patient-specific gait pattern and musculoskeletal loading.","long_description":"Bony deformities, e.g. increased or decreased femoral anteversion and neck-shaft angle, can lead to pathological gait patterns, altered joint loads, and degenerative joint diseases. The mechanism how bony geometries influence muscle forces and joint load during walking is still not fully understood. The aim of this project is to comprehensively investigate the influence of bony geometries on muscle forces and joint loads during walking. Furthermore, we will investigate reasons for pathological gait and evaluate the influence of clinical interventions on the patient-specific gait pattern and musculoskeletal loading.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Willi Koller,hans kainz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2486","unix_group_name":"f-2","modified":"1669407890","downloads":"0","group_name":"Floriculture cutting tool analysis","logo_file":"","short_description":"Analysis of biomechanical aspects related to muscle fatigue and other biomechanical stress factors that take place in the cutting process is required in order to perform ergonomic tool intervention.","long_description":"Analysis of biomechanical aspects related to muscle fatigue and other biomechanical stress factors that take place in the cutting process is required in order to perform ergonomic tool intervention.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Juan Pablo Pulido Muñoz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2488","unix_group_name":"cost_fn_snstvty","modified":"1675612936","downloads":"58","group_name":"Cost function sensitivity in predictive simulations for assistive device design","logo_file":"cost_fn_snstvty","short_description":"Results for tracking simulations and predictive simulations of unassisted and assisted gait using different cost functions.","long_description":"Software packages that use optimization to predict the motion of dynamic systems are powerful tools for studying human movement. These &quot;predictive simulations&quot; are gaining popularity in parameter optimization studies for designing assistive devices such as exoskeletons. The cost function is a critical component of the optimization problem and can dramatically affect the solution. Many cost functions have been proposed that are biologically inspired and produce reasonable solutions, but which may lead to different conclusions in some contexts. We used OpenSim Moco to generate predictive simulations of human walking using several cost functions, each of which produced a reasonable trajectory of the human model. We then augmented the model with motors that generated hip flexion, knee flexion, or ankle plantarflexion torques, and repeated the predictive simulations to determine the optimal motor torques. The model was assumed to be planar and bilaterally symmetric to reduce computation time. Peak torques varied from 41.3 to 79.0 N·m for the hip flexion motors, from 48.0 to 94.2 N·m for the knee flexion motors, and from 42.6 to 79.8 N·m for the ankle plantarflexion motors, which could have important design consequences. This study highlights the importance of evaluating the robustness of results from predictive simulations.\n\nPlease cite the following publication:\n\nNikoo A, Uchida TK. Be careful what you wish for: Cost function sensitivity in predictive simulations for assistive device design. Symmetry 14(12): 2534, 2022. https://doi.org/10.3390/sym14122534","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Thomas Uchida,ali nikoo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2492","unix_group_name":"2jointmuslce","modified":"1670001457","downloads":"0","group_name":"two joint muscle function, locomotion demo","logo_file":"","short_description":"demonstrate the function of two-joint and one-joint muscles during locomotion for undergraduate students","long_description":"demonstrate the function of two-joint and one-joint muscles during locomotion for undergraduate students","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Brandi Row Lazzarini","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2498","unix_group_name":"trunk_muscle_py","modified":"1680018910","downloads":"0","group_name":"Prediction of trunk muscle size and position","logo_file":"trunk_muscle_py","short_description":"This project provides programs for predicting trunk muscle size and position values given sex, age, height, and weight. The predictions apply regressions developed based on CT measurements in a multi-ethnic sample of the Framingham Heart Study.  ","long_description":"This project provides programs for predicting trunk muscle size and position values given sex, age, height, and weight. The predictions apply regressions developed based on CT measurements in a multi-ethnic sample of the Framingham Heart Study.  This implements the regressions in Python code, specifically enabling users to run online via Google Colab notebooks.  This may be of interest for researchers creating musculoskeletal models or other studies needing estimates of muscle morphometry.\n\nThe code (available in downloads) provides estimations of trunk muscle cross-sectional areas and positions at the vertebral levels between T4 and L4. Copy the Google Colab notebook to your Google Colab account to run.  https://colab.research.google.com/\n\nThe form requires submission of a subject's sex, weight ( kg / lb ), height ( cm / in ), Age and ID (optional). After clicking the play button on the form an excel workbook will be downloaded. It contains four sheets: Cross-Sectional Area in  mm^2 , Distance in  mm^2 , Angle in degrees and an additional sheet describing your subject's inputs. Note that this calculator was developed with a dataset containing healthy adults between ages 40 and 90. Application outside this range may not be accurate, and this should not be used for children and adolescents. \n\nFAQ.\n1:  What sample pool was used to generate the regression used in this calculator?\n\nTable 1:  Mean (SD) [Range] characteristics of participants included in the sample.\n&lt;table border=&quot;1&quot;&gt; &lt;tr&gt; &lt;th style=&quot;background-color: gray&quot;&gt;   &lt;/th&gt; &lt;th style=&quot;background-color: gray&quot;&gt; Men (N=247) &lt;/th&gt; &lt;th style=&quot;background-color: gray&quot;&gt; Women (N=260 ) &lt;/th&gt; &lt;/tr&gt; &lt;tr&gt; &lt;th style=&quot;background-color: gray&quot;&gt;Age (years)&lt;/th&gt; &lt;td&gt;60.8 (14.1) [40-88]&lt;/td&gt;  &lt;td&gt;61.8 (12.6) [40-90]&lt;/td&gt; &lt;/tr&gt; &lt;tr&gt; &lt;th style=&quot;background-color: gray&quot;&gt;Height (cm)&lt;/th&gt; &lt;td&gt;173.8 (7.2) [155.5-193.7]        &lt;/td&gt; &lt;td&gt;159.9 (6.6) [139.7-175.9] &lt;/td&gt; &lt;/tr&gt; &lt;tr&gt; &lt;th style=&quot;background-color: gray&quot;&gt; Weight (kg)&lt;/th&gt; &lt;td&gt;86.0 (14.4) [47.2-122.9]&lt;/td&gt; &lt;td&gt;70.7 (15.3) [40.4-127.0]&lt;/td&gt; &lt;/tr&gt;\n&lt;/table&gt;\n\n\n\n2:  Can this calculator be used for anyone?\nThe calculator can be used for anyone who falls within the data ranges noted above (i.e age 40 – 90, 140cm-195cm (4ft-6.4ft) and 70kg -130kg (154lbs-286lbs). Outside these ranges, the calculator may still be used, but will generate a warning that predictions are being extrapolated. Prediction intervals will also increase as inputs move outside the range of the sample. If an age &lt; 40 is entered, the calculations will be performed for age = 40, as aging-related effects are likely not found in the same way for adults under 40.\n\n3: How were the muscle distance and angle measurements calculated?\nMeasurements were performed in transverse plane CT scans at the mid-level of the vertebral body. After segmenting a muscle, the CSA is defined as its area in this plane. The distance and angle refer to the transverse plane polar coordinates representing position of a muscle’s centroid in relation to the centroid of the vertebral body, where the posterior direction is 0°.\n\n4.  What are the prediction intervals?\nThe prediction intervals are calculated at each vertebral level along with the predicted value. The prediction intervals for an outcome (lower 95% and upper 95%) provide a likely range of values for an individual with the given input sex, age, height and weight. A hard lower limit of 0 is applied for CSA predictions and distance predictions, and lower and upper limits of 0° and 180°, respectively, for angle predictions.\n\n5. How do I run multiple individuals at once?\nUse the  MuscleCalculator_ForBulkUse. pynb code and fill in the arrays with your individuals' information, using commas for delineation and quotation marks for Sex, WeightUnits, HeightUnits and Names. Click run and your files will download. Your browser may ask you to allow multiple files to be downloaded. If you do not see a notification for the files being downloaded, check  your google drive folder as some browsers may automatically send it there.\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Dennis Anderson,Joanna James,Brett Allaire,Seyed Javad Mousavi","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"415","fullname":"Visualization"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"}],"is_toolkit":false,"is_model":false,"is_application":true,"is_data":true},{"group_id":"2505","unix_group_name":"exotendon_sims","modified":"1692814652","downloads":"0","group_name":"How connecting the legs with a spring improves human running economy","logo_file":"","short_description":"In this study we perform 3D muscle-driven simulations of running with and without a passive elastic device, called an exotendon, to understand how users are able to improve their running economy while wearing the device.  The study contains 3D motion, GRF, EMG, and energy expenditure data, as well as simulation code with OpenSim Moco. ","long_description":"Connecting the legs with a spring attached to the shoelaces reduces the energy cost of running, but how the spring reduces the energy burden of individual muscles remains unknown. We generated muscle-driven simulations of seven individuals running with and without the spring to discern whether savings occurred during the stance phase or the swing phase, and to identify which muscles contributed to energy savings. We computed differences in muscle-level energy consumption, muscle activations, and changes in muscle-fiber velocity and force between running with and without the spring. Across participants, running with the spring reduced the measured rate of energy expenditure by 0.9 W/kg (8.3%). Simulations predicted a 1.4 W/kg (12.0%) reduction in the average rate of energy expenditure and correctly identified that the spring reduced rates of energy expenditure for all participants. Simulations showed most of the savings occurred during stance (1.5 W/kg), though the rate of energy expenditure was also reduced during swing (0.3 W/kg). The energetic savings were distributed across the quadriceps, hip flexor, hip abductor, hamstring, hip adductor, and hip extensor muscle groups, whereas no changes in the rate of energy expenditure were observed in the plantarflexor or dorsiflexor muscles. Energetic savings were facilitated by reductions in the rate of mechanical work performed by muscles and their estimated rate of heat production. The simulations provide insight into muscle-level changes that occur when utilizing an assistive device and the mechanisms by which a spring connecting the legs improves running economy.\n\nSupporting Code in branch 'paperSubmission': https://github.com/stingjp/muscleEnergyModel/tree/paperSubmission\n\nAll data and results are available in the 'Downloads&gt;Data Share' tab of this project.\n\nA collection of additional figures that may be useful in analysis of exotendon running is available on the 'Documents' Tab. ","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jon Stingel,Jennifer Hicks,Scott Delp,Scott Uhlrich","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2506","unix_group_name":"ankle-foot","modified":"1674210572","downloads":"148","group_name":"OpenSim® ankle-foot musculoskeletal model for assessment of strain and Force","logo_file":"","short_description":"Background\nThe ankle and foot are among the most critical load-bearing joints in the human anatomy. Anatomically accurate human body models are imperative to understanding the mechanics of injury and musculoskeletal disorders. A typical human ankle-foot ","long_description":"Background\nThe ankle and foot are among the most critical load-bearing joints in the human anatomy. Anatomically accurate human body models are imperative to understanding the mechanics of injury and musculoskeletal disorders. A typical human ankle-foot anatomy consists of 25 DOFs, 112 dense connective tissues (DCTs) (92 ligaments, one capsule and 19 fasciae), 30 tendons, and 65 muscles. Existing models possess less than half of the DOFs and physiological elements. In this work, we have developed an ankle-foot joint complex musculoskeletal model for the OpenSim® platform by incorporating 24 degrees of freedom (DOF) comprising of 66 DCTs (46 ligaments, one 1 capsule and 19 fasciae), 30 tendons, and 65 muscles.\n\nMethods\nComputed tomography (CT) data of human ankle joint-foot complex was segmented using Mimics ® (Version 17.0, Materialise, Belgium) to obtain models of the cartilages and bones of the ankle joint-foot complex. The position and resting lengths of the DCTs were attained from the MRI data and literature. Five joints, namely, tibiotalar, subtalar, chopart, tarsometatarsal (TMT), and metatarsophalangeal (MTP) joints and their joint axes were formulated to yield 24 DOFs. A forward simulation was carried out at each joint of the ankle-foot complex within their respective range of motions. The strains, instantaneous strain rates, and forces developed in the ligaments during the simulation were studied.\nResults\nDuring plantar-dorsiflexion of the tibiotalar joint, the anterior tibio-talar ligament (aTTL) yielded the maximum strain compared to all other ligaments. Anterior tibio-fibular ligament (aTFL) experienced extreme strain during subtalar inversion. Hence, the coupled kinematics of subtalar inversion and plantar flexion are failure-prone activities for aTFL. The chopart, TMT, and MTP joints yielded maximum strains or forces for several bundles at the extremes of the range of motion. This signifies that rotations of these joints to their extreme range of motion are prone to failure for the bundles attached to the joint complex. \n\nConclusion\nThe results illustrate the potential application of the proposed OpenSim® ankle-foot model in understanding the ligament injury mechanism during sports activity and its prevention. Researchers can use the proposed model or customise it to study complex kinematics, understanding injury mechanisms, testing fixtures, orthosis or prosthesis, and many more in the domain of musculoskeletal research.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Arnab Sikidar,Dinesh Kalyanasundaram,Dinesh Kalyanasundaram","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2511","unix_group_name":"acltearsfemale","modified":"1673983109","downloads":"0","group_name":"ACLresearchtear","logo_file":"","short_description":"Researching how an ACL ligament tears. Forces on the leg. How strong they are and how they impact the ligament. Use for senior project at Columbia University.","long_description":"Researching how an ACL ligament tears. Forces on the leg. How strong they are and how they impact the ligament. Use for senior project at Columbia University.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ashley Gigon","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2514","unix_group_name":"awais1575","modified":"1674071775","downloads":"0","group_name":"Identification of binding residues of FMDV with TLRs receptors using simulation","logo_file":"","short_description":"This research project aims to identify the binding residues of foot-and-mouth disease virus (FMDV) with Toll-like receptors (TLRs) using molecular dynamics (MD) simulation. The goal is to understand how FMDV binds to TLRs, which play a critical role in th","long_description":"This research project aims to identify the binding residues of foot-and-mouth disease virus (FMDV) with Toll-like receptors (TLRs) using molecular dynamics (MD) simulation. The goal is to understand how FMDV binds to TLRs, which play a critical role in the host innate immune response to viral infections.\n\nThe project will involve the use of bioinformatics tools to predict the 3D structure of FMDV and TLRs, as well as the small molecule ligands that bind to them. The MD simulations will be performed using a suitable force field (such as OPLS, CHARMM, AMBER etc.) to investigate the structural changes in the FMDV and TLRs caused by ligand binding.\n\nThe project will also involve the use of computational chemistry techniques such as free energy calculations and molecular dynamics simulations to analyze the interactions between FMDV and TLRs and to identify the specific amino acids that are involved in the binding process.\n\nThe project will use high-performance computing resources to perform large-scale simulations and analyze the results. The data generated by the simulations will be stored and managed using data storage and management systems.\n\nThe results of this project will provide a detailed understanding of the mechanisms of FMDV binding to TLRs and will identify key residues that are involved in this process. This knowledge could ultimately be used to develop new strategies for controlling FMDV infections.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Muhammad Awais","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2520","unix_group_name":"sync-hand-stim","modified":"1674694010","downloads":"0","group_name":"Synchronization of hand tasks with electrocorticography and cortical stimulation","logo_file":"","short_description":"A repository of kinematic hand data of different hand tasks while undergoing varied frequency cortical stimulation during an awake craniotomy. Electrocorticography is synchronized to kinematic and kinetic force data through a TTL signal.","long_description":"A repository of kinematic hand data of different hand tasks while undergoing varied frequency cortical stimulation during an awake craniotomy. Electrocorticography is synchronized to kinematic and kinetic force data through a TTL signal.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Leon Taquet","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2524","unix_group_name":"cycling_sim","modified":"1703686702","downloads":"178","group_name":"Muscle-Driven Simulations and Experimental Data of Cycling","logo_file":"cycling_sim","short_description":"Muscle-driven simulations of cycling using optimal control methods and experimental data for 16 participants.","long_description":"The aim of this work was to develop and validate a set of muscle-driven simulations of cycling using optimal control methods. We used direct collocation to generate simulations of 16 participants cycling over a range of powers (40-216 W) and cadences (75-99 RPM) using two optimization objectives: a baseline objective that minimized muscle effort and a second objective that additionally minimized tibiofemoral joint forces. Adding a term in the objective function to minimize tibiofemoral forces preserved cycling power and kinematics, improved similarity between active muscle force timing and experimental electromyography, and decreased tibiofemoral joint reaction forces, which better matched previously reported in vivo measurements.\n\n•Read our paper: https://doi.org/10.1038/s41598-023-47945-5\n•Download the data, models, and code:\n--Motion capture, external force, kinematic, and joint states data for 3 full minutes of cycling\n--OpenSim models\n--Moco simulation code with example scripts to run the code\n--Example results files\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Caitlin Clancy,Scott Delp,Anthony Gatti,Carmichael Ong","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2533","unix_group_name":"me481","modified":"1676309503","downloads":"0","group_name":"Dynamic Walking Model Simulation","logo_file":"","short_description":"In this exercise you will be given several Passive Dynamic Walker Models and an arena with obstacles. The goal of the exercise is to maximize the distance the walkers can travel on increasingly challenging terrain by adjusting the model’s parameters. Yo","long_description":"In this exercise you will be given several Passive Dynamic Walker Models and an arena with obstacles. The goal of the exercise is to maximize the distance the walkers can travel on increasingly challenging terrain by adjusting the model’s parameters. You will use the OpenSim graphical user interface (GUI) to adjust component properties, visualize dynamic\nsimulations, and make plots of your simulation results.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jenna Altahhan","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2540","unix_group_name":"biomechanics232","modified":"1676860479","downloads":"0","group_name":"Biomechanics Course Projects","logo_file":"","short_description":"Students in a biomechanics course will be using OpenSim to work on projects relating to bones, tendons, ligaments, muscles, cardiovascular system, and gait. This page will be utilized to showcase their work and documents.","long_description":"Students in a biomechanics course will be using OpenSim to work on projects relating to bones, tendons, ligaments, muscles, cardiovascular system, and gait. This page will be utilized to showcase their work and documents.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Adel Alhalawani","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2541","unix_group_name":"theia3d_to_osim","modified":"1677090822","downloads":"0","group_name":"Theia3D to OpenSim: Utilities to fit an OpenSim model from markerless mocap data","logo_file":"","short_description":"Theia3D to OpenSim: Utilities to fit an OpenSim model from markerless mocap data","long_description":"Theia3D to OpenSim: Utilities to fit an OpenSim model from markerless mocap data","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thomas Uchida","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2543","unix_group_name":"fibextrot","modified":"1677178083","downloads":"0","group_name":"Mechanical Testing of Syndesmosis repair devices in 3D printed ankles","logo_file":"","short_description":"To calculate the average external rotation force on the fibula during gait to be used in mechanical testing of syndesmosis devices in 3D printed tibia and fibula models","long_description":"To calculate the average external rotation force on the fibula during gait to be used in mechanical testing of syndesmosis devices in 3D printed tibia and fibula models","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Anna Sugrue","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2547","unix_group_name":"imu-kinematics","modified":"1677602964","downloads":"0","group_name":"Estimation of kinematics from inertial measurement units using a combined deep l","logo_file":"","short_description":"The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontr","long_description":"The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. \n\nCODE: https://github.com/CMU-MBL/JointAnglePrediction_JOB\n\nTo cite this work:\n@article{rappshin2021,\ntitle={Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework},\nauthor={Rapp, Eric and Shin, Soyong and Thomsen, Wolf and Ferber, Reed and Halilaj, Eni},\njournal={Journal of Biomechanics},\nyear={2021},\n}\n\nLink to paper: https://www.sciencedirect.com/science/article/pii/S0021929021000099","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Soyong Shin","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2552","unix_group_name":"imu-exercise","modified":"1703263503","downloads":"1","group_name":"Seven things to know about exercise monitoring with inertial sensing wearables","logo_file":"","short_description":"We present open-source deep learning based classifiers for predicting physical therapy exercises using inertial measurement units, and a comprehensive analysis of impacts of sensor density, location, type, state estimation, and sample size on the performance. ","long_description":"GitHub: https://github.com/CMU-MBL/IMU_Exercise_Prediction.\nData: Downloads/View\nPreprint: https://doi.org/10.36227/techrxiv.23296487.v1.\n\nData: Nineteen (19) subjects were recruited to perform 37 lower-body exercises while wearing ten (10) inertial measurement units (IMUs) on chest, pelvis, wrists, thighs, shanks, and feet. Check our preprint for more details of the data collection. Available data include:\n1. IMU data (100 Hz). \n2. Ideal joint angles of hips, knees, and ankles obtained from a marker-based motion capture system (100 Hz).\n\nCode: Instruction can be found on the GitHub link above.\n\nCitation: If you find this helpful for your project, please consider citing the following paper: Phan, Vu; Song, Ke; Silva, Rodrigo Scattone; Silbernagel, Karin G.; Baxter, Josh R.; Halilaj, Eni (2023). Seven Things to Know about Exercise Monitoring with Inertial Sensing Wearables. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.23296487.v1. ","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Vu Phan,Ke Song","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2565","unix_group_name":"imuvisbiomech","modified":"1699282237","downloads":"37","group_name":"IMU &amp; Computer Vision Fusion via Biomechanical Modeling","logo_file":"imuvisbiomech","short_description":"Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused without incorporating equations of motion. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion. To test this hypothesis, we fused RGB video and inertial sensing data with analytical kinematics equations of motion and dynamics equations of motion with a nine degree-of-freedom model and investigated whether adding these equations of motion enables fusion methods to outperform video-only and inertial-sensing-only methods on data of varying qualities.","long_description":"This project links to a repository containing code for running 4 classes of simulations in MATLAB with a nine DOF biomechanical model for estimating full body kinematics (and dynamics and contact forces if using direct collocation):\n\n(1) IMU and vision data fusion (tracking) via direct collocation (kinematics+dynamics equations of motion)\n\n(2) IMU only data tracking (and denoising) via direct collocation (kinematics+dynamics equations of motion)\n\n(3) Unconstrained IMU and vision data fusion via inverse kinematics (using kinematics equations of motion)\n\n(4) Unconstrained kinematics calculations using computer vision keypoints only (using kinematics equations of motion)","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Soyong Shin,Owen Pearl","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2566","unix_group_name":"bmc_508","modified":"1679336690","downloads":"0","group_name":"Musculoskeletal dynamics in-class workshop","logo_file":"","short_description":"Musculoskeletal dynamics in-class workshop","long_description":"Musculoskeletal dynamics in-class workshop","has_downloads":false,"keywords":"","ontologies":"","projMembers":"William Elzemeyer","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2568","unix_group_name":"caspm","modified":"1679526093","downloads":"0","group_name":"Carotid Artery Stenosis Model","logo_file":"","short_description":"A computational model for predicting the progression of carotid artery stenosis.","long_description":"A computational model for predicting the progression of carotid artery stenosis.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Yaqi Li","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2573","unix_group_name":"femors-rbf","modified":"1701273799","downloads":"10","group_name":"Finite Element Mesh Overclosure Reduction and Slicing (FEMORS)","logo_file":"femors-rbf","short_description":"This code contains functions to slice and remove overclosures of 2D and 3D meshes using RBF Networks, as well as conventional nodal adjustment. It also contains helper functions, that can slice and load STL geometries and manipulate and view 3D hexahedral meshes.","long_description":"The code was developed with the project to make freely available 3D geometries of the lower limbs of the Visible Human Female and Visible Human Male. The FEMORS code was used to remove all overclosures between adjacent geometries. The VH 3D geometries are available at: https://simtk.org/projects/3d-vh-geometry\n\nThe code was implemented in MATLAB utilizing the Machine Learning Toolbox and is available free and open-source, but we ask that you cite the following two works:\n\nAndreassen, T. E., Hume, D. R., Hamilton, L. D., Higinbotham, S. E. &amp; Shelburne, K. B. &quot;An Automated Process for 2D and 3D Finite Element Overclosure and Gap Adjustment using Radial Basis Function Networks&quot;. 1–13 (2022) https://doi.org/10.48550/arXiv.2209.06948\n\nTE Andreassen, DR Hume, LD Hamilton, K Walker, SE Higinbotham, KB Shelburne, &quot;Three-dimensional lower extremity musculoskeletal geometry of the Visible Human Female and Male,” Sci Data 10, 34 (2023). https://doi.org/10.1038/s41597-022-01905-2.\n\nAdding changes to the code is encouraged and can be added to the repository by contacting the author. The author will check new or revised content for accuracy and completeness and add it to the repository.\n\nFuture/ongoing work aims to recreate the code using code that does not need the Machine Learning Toolbox, as well as implementing the code into a Python Toolbox for widespread use.","has_downloads":true,"keywords":"GRNN,Morphing,Overclosure,Biomechanics,Finite Element Analysis","ontologies":"","projMembers":"Kevin Shelburne,Sean Higinbotham,Donald Hume,Thor Andreassen,Landon Hamilton","trove_cats":[{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"309","fullname":"Cardiovascular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software Libraries"},{"id":"402","fullname":"Software 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Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image Processing"},{"id":"412","fullname":"Image 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bow.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ryan McGaughey","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2590","unix_group_name":"triadic","modified":"1681426045","downloads":"0","group_name":"Triadic Collaboration Between Humans and Robots","logo_file":"","short_description":"This project contains data, model files and documentation for experiments on inverse optimal control and physical human-robot interaction. The files in this repository are associated with two publications which have been accepted and are awaiting publicat","long_description":"This project contains data, model files and documentation for experiments on inverse optimal control and physical human-robot interaction. The files in this repository are associated with two publications which have been accepted and are awaiting publication.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Daniel Gordon","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2605","unix_group_name":"lzstorm6","modified":"1682959027","downloads":"0","group_name":"Biomechanics Lacrosse Injury Simulation (head)","logo_file":"","short_description":"For Biomechanics Final\n\nSimulation of Lacrosse Head Hit with Helmet on and then with the addition of a student-created device that supports the next and slows angular acceleration.","long_description":"For Biomechanics Final\n\nSimulation of Lacrosse Head Hit with Helmet on and then with the addition of a student-created device that supports the next and slows angular acceleration.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Luke Zibbell","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2610","unix_group_name":"okr-fyp","modified":"1683135184","downloads":"0","group_name":"Oxford Knee Rig","logo_file":"","short_description":"OpenSim Model of Oxford knee rig","long_description":"OpenSim Model of Oxford knee rig","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Kyle Magwood","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2617","unix_group_name":"frontkick","modified":"1683739303","downloads":"0","group_name":"Kicks","logo_file":"","short_description":"Kick without load and with load","long_description":"Kick without load and with load","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Michal Vagner","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2618","unix_group_name":"osscolosisspine","modified":"1683763036","downloads":"0","group_name":"Personalised scoliotic spine models","logo_file":"","short_description":"This project is a codified workflow to create personalised OpenSim models of the scoliotic spine from a set of virtually palpated landmarks. The project provides a simplified model of the spine (adapted from Bruno et al (2017)), that includes 6DoF at each","long_description":"This project is a codified workflow to create personalised OpenSim models of the scoliotic spine from a set of virtually palpated landmarks. The project provides a simplified model of the spine (adapted from Bruno et al (2017)), that includes 6DoF at each joint with linear bushing forces, and wrapping surfaces on each of the vertebrae. The documents include the virtual palpation protocol that needs to be followed if the code which creates the scoliotic model is to work. Finally MatLab codes are provided, there are two main codes and associated functions. One code is to created the simplified spine model, and the other main code takes the simplified model and the set of virtually palpated landmarks to create the personalised spine scoliotic spine model. See the code and additional documentation for further details.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Samuele Gould","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2619","unix_group_name":"ss_spine_models","modified":"1683763043","downloads":"0","group_name":"Specimen specific spine models","logo_file":"","short_description":"This is a code to create specimen specific spine models from segmented geometries and virtually palpated landmarks. Documents explaining the necessary inputs are provided, as well as 6 example models are provided.","long_description":"This is a code to create specimen specific spine models from segmented geometries and virtually palpated landmarks. Documents explaining the necessary inputs are provided, as well as 6 example models are provided.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Samuele Gould","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2620","unix_group_name":"emily_project","modified":"1705483152","downloads":"0","group_name":"Musculoskeletal model of the emu (Dromaius novaehollandiae)","logo_file":"","short_description":"This project page contains all the relevant data of our project: Emu Model for Investigating Locomotor dYnamics (EMILY). The emu is a large (ratite) running bird from Australia. We have used this model for predictive gait simulations in OpenSim/Moco.","long_description":"This project page will contain all the relevant data and files for our musculoskeletal model of the emu. This includes (OpenSim) model files, 3D skeletal geometry, and other all relevant simulator outputs pertaining to our publications. We will also provide example Matlab scripts to run (predictive) gait optimizations in Moco, together with scripts that post-process these outputs and generate plots.\n\nThe data in this project is currently set to private. For review purposes, all the relevant files are available here: https://drive.google.com/drive/folders/1hPOsFYcOVosepdbMNUbTw22zH7wUUrpr?usp=sharing","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Pasha van Bijlert","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2626","unix_group_name":"tamu_ca_abd_vol","modified":"1685327519","downloads":"0","group_name":"Kinematic Analysis of Motions with Different Abduction Volition","logo_file":"","short_description":"I am trying to create an upper extremity model where I model movement from different starting points. These starting points refer to different shoulder abduction orientations.","long_description":"I am trying to create an upper extremity model where I model movement from different starting points. These starting points refer to different shoulder abduction orientations.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Adib Laskar","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2630","unix_group_name":"muscle_act","modified":"1685727119","downloads":"0","group_name":"First_year_exploration","logo_file":"","short_description":"Exploring the capabilities of OpenSim for use as part of my PhD - exploring muscle activation when operating different types of equipment","long_description":"Exploring the capabilities of OpenSim for use as part of my PhD - exploring muscle activation when operating different types of equipment","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Isabelle Ormerod","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2635","unix_group_name":"muscle_regen","modified":"1706216573","downloads":"0","group_name":"Agent-Based Model of Muscle Regeneration with Microvascular Remodeling","logo_file":"","short_description":"ABM of muscle regeneration that incorporates spatial cytokine and cell dynamics as well as microvascular changes. ","long_description":"This agent-based model uses the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. It simulates the spatial behaviors of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. The model was used to perturb altered cytokine dynamics to analyze the impact on cell behaviors and recovery outcomes.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Alexa Petrucciani,Silvia Blemker,Tien C,Megan Haase,Shayn Peirce-Cottler","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2637","unix_group_name":"spaurk","modified":"1686085874","downloads":"0","group_name":"SPAURK","logo_file":"","short_description":"Prosthetic Alignment System Using OpenCap","long_description":"Prosthetic Alignment System Using OpenCap","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jakob Markham","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2641","unix_group_name":"loading_est","modified":"1686588141","downloads":"0","group_name":"loading_stiffness_vsl","logo_file":"","short_description":"This project is aiming at visualizing stiffness of human's lower body.","long_description":"This project is aiming at visualizing stiffness of human's lower body.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ruoding An","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2645","unix_group_name":"tcf_comp_forces","modified":"1701271280","downloads":"87","group_name":"Tibiofemoral forces for medial and lateral compartment in knee osteoarthritis","logo_file":"tcf_comp_forces","short_description":"The papers related to this project are currently in the submission process. After publication, the access link will be available on this webpage. The model used for all studies involved in this project is available for download. ","long_description":"The model used for all studies involved in this project is available for download.\n\nPlease cite:\nPelegrinelli ARM, Catelli DS, Kowalski E, Lamontagne M, Moura FA. Comparing three generic musculoskeletal models to estimate the tibiofemoral reaction forces during gait and sit-to-stand tasks, Medical Engineering &amp; Physics, 2023,104074.\nISSN 1350-4533\nhttps://doi.org/10.1016/j.medengphy.2023.104074.\n\nKnee osteoarthritis has a prevalence increasing around the world, and tibiofemoral contact forces are related to the onset and progression of osteoarthritis. Using OpenSim, it is possible to estimate the tibiofemoral contact forces and muscle forces during different functional tasks. Different musculoskeletal models have been developed to improve the accuracy of contact force estimation.\nThis project aims to investigate the difference in tibiofemoral contact forces between healthy individuals and knee osteoarthritis patients. Recently, some researchers improved the capacity of musculoskeletal models to predict contact forces. Rajagopal et al. (2016), using cadaveric and MRI data, improved the geometry of the lower limb model, significantly improving the accuracy of muscle force prediction. This model is the most commonly used to evaluate lower limb forces. More recently, Bedo et al. (2020) combined the Catelli et al. (2019) model, which accounts for movements with high hip and knee flexion, with a compartment tibiofemoral force proposed by Lerner et al. (2015) to estimate compartmental forces during different tasks. Finally, Uhlrich et al. (2022) improved the Rajagopal model by adjusting some muscle parameters to enhance the accuracy of muscle forces.\nFor the general purpose of this project, which is to analyze the differences between healthy individuals and knee osteoarthritis patients, a combined model using the Bedo model, Rajagopal model, and Ulrich model was tested. This combined model was evaluated using the CAMS Knee Dataset to assess its capacity to estimate tibiofemoral forces compared to measurements with instrumented knee prostheses. The analyses were performed for gait, sit-to-stand, and stand-to-sit tasks.\nOther two studies were developed comparing the tibiofemoral contact forces and muscle forces during gait and sitting down and standing up tasks in healthy and knee osteoarthritis patients. Lastly, one more study was developed to investigate the performance of different machine learning models to predict the tibiofemoral contact forces during the gait using only the kinematic and joint moments. \n\nReferences about the models included in the new adapted model:\n\nBedo B., Catelli, D.S., Lamontagne, M., Santiago, P.R.P., 2020. A custom musculoskeletal model for estimation of medial and lateral tibiofemoral contact forces during tasks with high knee and hip flexions. Computer methods in biomechanics and biomedical engineering 23, 658-663.\nCatelli, D.S., Wesseling, M., Jonkers, I., Lamontagne, M., 2019. A musculoskeletal model customized for squatting task. Computer methods in biomechanics and biomedical engineering 22, 21-24.\nLerner, Z.F., DeMers, M.S., Delp, S.L., Browning, R.C., 2015. How tibiofemoral alignment and contact locations affect predictions of medial and lateral tibiofemoral contact forces. Journal of Biomechanics 48, 644-650.\nRajagopal, A., Dembia, C.L., DeMers, M.S., Delp, D.D., Hicks, J.L., Delp, S.L., 2016. Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait. IEEE Transactions on Biomedical Engineering 63, 2068-2079.\nUhlrich, S.D., Jackson, R.W., Seth, A., Kolesar, J.A., Delp, S.L., 2022. Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force. Scientific reports 12, 9842.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Alexandre  Pelegrinelli,Mario Lamontagne,Erik Kowalski,Danilo Catelli","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2648","unix_group_name":"upperlimb_fhdp","modified":"1687457528","downloads":"0","group_name":"Upper limb FHDP","logo_file":"","short_description":"This project pretends to help study upper limb movement and dynamics.","long_description":"This project pretends to help study upper limb movement and dynamics.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Fabian Diaz","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2651","unix_group_name":"bayes_design","modified":"1687890594","downloads":"0","group_name":"BayesDesign","logo_file":"","short_description":"Data for the paper: \"A probabilistic view of protein stability, conformational specificity, and design\"","long_description":"Data for the paper: &quot;A probabilistic view of protein stability, conformational specificity, and design&quot;","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jacob Stern","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2656","unix_group_name":"caa","modified":"1688582567","downloads":"0","group_name":"Effects of Anticipation and Confidence on Standing Balance Outcomes","logo_file":"","short_description":"Using CoM and CoP data as measures of standing balance vulnerability. Testing fear and confidence surveys, anticipation, and age, and indicators of standing balance.","long_description":"Using CoM and CoP data as measures of standing balance vulnerability. Testing fear and confidence surveys, anticipation, and age, and indicators of standing balance.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Owen Streppa","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2658","unix_group_name":"strokerehab","modified":"1710196435","downloads":"440","group_name":"Grand Challenge Competition to Design Stroke Neurorehabilitation Treatments","logo_file":"","short_description":"Provide a comprehensive data set with associated models (scaled generic and personalized) that will enable researchers to design personalized stroke neurorehabilitation treatments.","long_description":"Despite the uniqueness of each patient, in the stroke rehabilitation field treatment design for movement impairments has not progressed substantially beyond off-the-shelf interventions selected based on subjective clinical judgement. If affected individuals are to recover the most function possible, a paradigm shift is needed toward personalized interventions designed using objective evidence-based methods. This project provides the biomechanics community with a unique and comprehensive data set to design personalized stroke neurorehabilitation treatments. This data set includes motion capture, ground reaction, and EMG collected from subjects post-stroke in addition to associated scaled generic and personalized OpenSim models. \n\nIf you have any questions or concerns please either post a message on the forum or send an email to nmsm@rice.edu and we will follow up with you shortly. \n\n","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Kayla Pariser,B.J. Fregly,Claire V. Hammond,Mohammad S. Shourijeh,Marleny Arones,Di Ao,Geng Li,Spencer Williams,Carolynn Patten","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2660","unix_group_name":"neck","modified":"1689615461","downloads":"0","group_name":"Neck","logo_file":"","short_description":"To look at neck force production","long_description":"To look at neck force production","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Anthony Acevedo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2667","unix_group_name":"noisy-video-imu","modified":"1690413117","downloads":"0","group_name":"Markerless Motion Tracking with Noisy Video and IMU Data","logo_file":"","short_description":"We use synthetic video and IMU data generated from the AMASS datasets (n = 500 subjects) to train deep learning models that can predict 3-D motion from noisy videos and/or uncalibrated IMUs. ","long_description":"Marker-based motion capture, considered the gold standard in human motion analysis, is expensive and requires trained personnel. Advances in inertial sensing and computer vision offer new opportunities to obtain research-grade assessments in clinics and natural environments. A challenge that discourages clinical adoption, however, is the need for careful sensor-to-body alignment, which slows the data collection process in clinics and is prone to errors when patients take the sensors home. We trained deep learning models that estimate human movement from noisy video data (VideoNet), inertial data (IMUNet), and a combination of the two (FusionNet), obviating the need for careful calibration.\n\nData: We use a public dataset (https://amass.is.tue.mpg.de) to train the models. We will post the test data soon.\n\nCode: https://github.com/CMU-MBL/FS_Video_IMU_Fusion\n\nCitation: Shin, Soyong, Zhixiong Li, and Eni Halilaj. &quot;Markerless Motion Tracking with Noisy Video and IMU Data.&quot; IEEE Transactions on Biomedical Engineering (2023).","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Soyong Shin,Zhixiong Li","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2670","unix_group_name":"c5a_receptors","modified":"1691600778","downloads":"0","group_name":"Helix 8 in chemotactic receptors of the complement system","logo_file":"","short_description":"Host response to infection involves the activation of the complement system leading to the production of anaphylatoxins C3a and C5a. Complement factor C5a exerts its effect through the activation of C5aR1, chemotactic receptor 1, and triggers the G protei","long_description":"Host response to infection involves the activation of the complement system leading to the production of anaphylatoxins C3a and C5a. Complement factor C5a exerts its effect through the activation of C5aR1, chemotactic receptor 1, and triggers the G protein-coupled signaling cascade. Orthosteric and allosteric antagonists of C5aR1 are a novel strategy for anti-inflammatory therapies. Here, we discuss recent crystal structures of inactive C5aR1 in terms of an inverted orientation of helix H8, unobserved in other GPCR structures. An analysis of mutual interactions of subunits in the C5aR1—G protein complex has provided new insights into the activation mechanism of this distinct receptor. By comparing two C5aR receptors C5aR1 and C5aR2 we explained differences between their signaling pathways on the molecular level. By means of molecular dynamics we explained why C5aR2 cannot transduce signal through the G protein pathway but instead recruits beta-arrestin. A comparison of microsecond MD trajectories started from active and inactive C5aR1 receptor conformations has provided insights into details of local and global changes in the transmembrane domain induced by interactions with the Gα subunit and explained the impact of inverted H8 on the C5aR1 activation.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Dorota Latek","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2675","unix_group_name":"bosm_hip_model","modified":"1707319366","downloads":"6","group_name":"Model to Investigate Hip Mechanics in Response to Dynamic Multiplanar Tasks","logo_file":"bosm_hip_model","short_description":"The purposes of this study were to: i) modify the existing 2396Hip model to simulate dynamic tasks with multiplanar hip joint motion; and ii) validate the modified model quantitatively against experimental data.","long_description":"This model is based on the 2396Hip Model (Harris et al., 2017). The model was modified to increase the hip flexion capacity from 100° to 138°, hip adduction from 20° to 30°, and knee flexion from 110° to 145° (Catelli et al., 2019). Muscle wrapping surfaces were added to maintain physiological muscle paths during the increased range of motion as previously developed for the Full-body Squat Model (Catelli et al., 2019). The maximum isometric muscle forces were increased by 30%  so the model could generate the joint moments for all tasks (Harrington &amp; Burkart, 2023). \n\nCitation: Harrington, M. S., &amp; Burkhart, T. A. (2023). Validation of a musculoskeletal model to investigate hip joint mechanics in response to dynamic multiplanar tasks. Journal of Biomechanics, 158, 111767–111767. https://doi.org/10.1016/j.jbiomech.2023.111767","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Timothy Burkhart,Margaret Harrington","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2680","unix_group_name":"pm_pilot_0815","modified":"1692384078","downloads":"0","group_name":"PM Pilot Data Analysis","logo_file":"","short_description":"Knee and ankle angles during a 10-minute submaximal run test.","long_description":"Knee and ankle angles during a 10-minute submaximal run test.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Julie Walton","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2681","unix_group_name":"mocoparallel","modified":"1695744034","downloads":"29","group_name":"Multicore parallel computing with OpenSim Moco","logo_file":"mocoparallel","short_description":"This project evaluates the performance of multicore parallel computing for solving optimal control musculoskeletal simulation problems using OpenSim Moco.","long_description":"In this project, we investigated the computational speed‐up obtained via multicore parallel computing relative to solving problems serially (i.e., using a single core) in optimal control simulations of human movement in OpenSim Moco. Simulations were solved using up to 18 cores with a variety of temporal mesh interval densities and using two different initial guess strategies. Considerable speed‐up can be achieved for some optimal control simulation problems in OpenSim Moco by leveraging the multicore processors often available in modern computers. \n\nThis work is described in the paper &quot;Computational performance of musculoskeletal simulation in OpenSim Moco using parallel computing&quot; which is available on the Publications page. Models and complete working examples are provided on the Downloads page. This project was supported by a Rackham Graduate Student Research Grant.","has_downloads":true,"keywords":"parallel processing,biomechanics,musculoskeletal model,optimal control,optimization","ontologies":"","projMembers":"Brian Umberger,Alex Denton","trove_cats":[{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"306","fullname":"Applications"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"405","fullname":"Public Downloads"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"416","fullname":"Statistical Analysis"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"},{"id":"419","fullname":"Scripts, Plug-Ins, and Other Utilities"}],"is_toolkit":false,"is_model":true,"is_application":true,"is_data":false},{"group_id":"2682","unix_group_name":"dynamic-quest","modified":"1712815895","downloads":"0","group_name":"Quest for Dynamic Consistency: Comparing OpenSim Tools for Residual Reduction","logo_file":"dynamic-quest","short_description":"This project compares various OpenSim tools for residual reduction and producing dynamically consistent simulations of human running.","long_description":"The code and data associated with this project are archived on Zenodo. Please visit &lt;a href=&quot;https://zenodo.org/records/10634026&quot;&gt;this link&lt;/a&gt; to preview and download.\n\nPlease cite the following work if using data or code from this repository:\n\nFox A (2024). The quest for dynamic consistency: A Comparison of OpenSim tools for residual reduction in simulations of human running. &lt;i&gt;Royal Society Open Science&lt;/i&gt;, doi: &lt;a href=&quot;https://doi.org/10.1098/rsos.231909&quot;&gt;10.1098/rsos.231909&lt;/a&gt;.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Aaron Fox","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2683","unix_group_name":"endurance_run","modified":"1693244550","downloads":"0","group_name":"Skeletal Traits of Endurance Running in Homo and Pan","logo_file":"","short_description":"Looking at musculoskeletal walking and running mechanics of Homo sapiens and Pan troglodytes based on skeletal traits from previous data collection and analysis.  This is to find out whether or not chimpanzees benefit from any overlapping skeletal traits ","long_description":"Looking at musculoskeletal walking and running mechanics of Homo sapiens and Pan troglodytes based on skeletal traits from previous data collection and analysis.  This is to find out whether or not chimpanzees benefit from any overlapping skeletal traits we may share that help humans to endurance run.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emily Graves","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2693","unix_group_name":"var-val-tool","modified":"1701009434","downloads":"25","group_name":"Knee Varus/Valgus Malalignment Tool for OpenSim Models","logo_file":"var-val-tool","short_description":"A MATLAB tool has been developed allowing researchers to modify generic OpenSim models and generate semi-personalized models that incorporate knee varus/valgus malalignment.","long_description":"The tool is coded based on the OpenSim API 4.x and is able to modify OpenSim models such as Gait 2354, Gait 2392, Rajagopal, and Hang Xu.\nTo generate a semi-personalized model using the tool, the following inputs are required:\n1.\tAn OpenSim generic model.\n2.\tThe center of rotation angulation (known as CORA in orthopedics) of the knee joint, specified on the OpenSim model using a virtual marker in the OpenSim GUI.\n3.\tThe magnitude of varus/valgus malalignment in each femur and tibia bone (Known as mLDFA and mMPTA in Orthopedics).\nThe tool then modifies the generic OpenSim model to incorporate varus/valgus malalignment, according to the location of CORA on each bone and the magnitude of deformation. The resulting semi-personalized model can then be used for further research purposes.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Sina Tabeiy","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2701","unix_group_name":"6modulo_biomech","modified":"1695078385","downloads":"0","group_name":"Kinematics joint angles analysis","logo_file":"","short_description":"Investigate how muscle-tendon lengths and moment arms depend on limb configuration.","long_description":"Investigate how muscle-tendon lengths and moment arms depend on limb configuration.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Valeria Sanchez","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2705","unix_group_name":"mvasculature","modified":"1695150657","downloads":"0","group_name":"MRA analysis of mice Vasculature","logo_file":"","short_description":"I would like to measure the capillary vessel radius and density of mice MRA images for longitudinal study. Especially the small capillaries in cortex region of brain.","long_description":"I would like to measure the capillary vessel radius and density of mice MRA images for longitudinal study. Especially the small capillaries in cortex region of brain.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ishan Pathak","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2711","unix_group_name":"ualbertaexo","modified":"1695596246","downloads":"0","group_name":"Exoskeleton","logo_file":"","short_description":"Creating an exoskeleton for the legs that is controlled through a brain-computer interface.","long_description":"Creating an exoskeleton for the legs that is controlled through a brain-computer interface.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Landon Black","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2716","unix_group_name":"sv_project_vw","modified":"1695847521","downloads":"0","group_name":"SVProject","logo_file":"","short_description":"This is the SV Project I did on my mac","long_description":"This is the SV Project I did on my mac","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Viola Wu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2725","unix_group_name":"freemocap","modified":"1697140272","downloads":"2","group_name":"FreeMocap - OpenSim - converter","logo_file":"","short_description":"This project provides preliminary codes to convert data from FreeMocap(https://freemocap.org/) files to OpenSim.","long_description":"This project provides preliminary codes to convert data from FreeMocap(https://freemocap.org/) files to OpenSim.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Rodrigo Bini","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2730","unix_group_name":"kneepatfemjoint","modified":"1713634763","downloads":"6","group_name":" Patellofemoral joint model for knee pathologies analysis","logo_file":"kneepatfemjoint","short_description":"The project presents a knee model, including a six degree-of-freedom patellofemoral joint, patellar stabilizers and contact surfaces for patella and femur. ","long_description":"To analyze the movement of patella during knee flexion and to study deviations from the normal joint function, one can deactivate certain ligaments. The model contains the main stabilizers of the patella: medial patellofemoral ligament (MPFL), medial patellotibial ligament (MPTL), lateral retinaculum (LR). The contact surface of the patella is presented as seven facets.\n\nPaper:\nPatellar motion and dysfunction of its stabilizers in a biomechanical model of the knee joint.\nSECHENOV MEDICAL JOURNAL VOL. 15, No. 1, 2024\n\nhttps://doi.org/10.47093/2218-7332.2024.15.1.47-60","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Alexandra Yurova","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2742","unix_group_name":"mmfvfeb","modified":"1712868358","downloads":"0","group_name":"Muscle Material with Force-Velocity Properties: A FEBio Material Plugin","logo_file":"","short_description":"A FEBio plugin that adds a force-velocity capable muscle constitutive model for use in quasi-static simulations. ","long_description":"A plugin package developed for use in FEBio (v. 2.9.1) that modifies the FEBio Muscle Material based on Blemker et al. 2005 (10.1016/j.jbiomech.2004.04.009) to include force-velocity properties. Used as the constitutive material for DiSalvo &amp; Blemker 2024 (https://doi.org/10.1016/j.jbiomech.2024.112089).\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Matthew DiSalvo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2761","unix_group_name":"biomech_5","modified":"1700453088","downloads":"0","group_name":"Inverse Joint Dynamics","logo_file":"","short_description":"Exploring inverse joint dynamics in the leg","long_description":"Exploring inverse joint dynamics in the leg","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Leslie Walker","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2763","unix_group_name":"muscle_fe_titin","modified":"1700468136","downloads":"0","group_name":"Muscle constitutive model with actin-titin binding to simulate force enhancement","logo_file":"","short_description":"FEBio implementation of a muscle constitutive model with actin-titin binding that demonstrates force enhancement.","long_description":"FEBio implementation of a muscle constitutive model with actin-titin binding that demonstrates force enhancement.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Thomas Uchida,Manuel Lucas Sampaio de Oliveira","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2767","unix_group_name":"mallard","modified":"1701070117","downloads":"0","group_name":"mallard","logo_file":"","short_description":"The green headed duck (Anas platyrhynchos) is a type of amphibious walking bird that can fly intermittently. Its foot movements are mainly completed by three toes, which are the second, third, and fourth toes from the inside out,\nThere are webs between t","long_description":"The green headed duck (Anas platyrhynchos) is a type of amphibious walking bird that can fly intermittently. Its foot movements are mainly completed by three toes, which are the second, third, and fourth toes from the inside out,\nThere are webs between the toes, which will open when touching the ground and close when leaving the ground. They often live in shallow and lush freshwater lakes, ponds, marshes, mudflat and other areas, and have the ability to walk on mudflat. When the green headed duck touches the ground with its feet, its toes and fins open, and when it leaves the ground, its toes and fins close. As the joint angle between the toes gradually decreases, it drives the fins to close and rapidly increases before the next touchdown, causing the fins to open. The decrease in the joint angle between the toes of the green headed duck during the first half of the swing period can reduce the air resistance during ground motion and the resistance from water during water motion for the fins of the green headed duck. The mechanism may be the joint pulling effect of the flexor tendon and ligament of the green headed duck's feet. For the second half of the swing period, the joint angle between the toes of the green headed duck gradually increases and drives the fins to open, allowing the maximum ground contact area to accommodate the next touchdown, which to some extent reflects the anti sinking ability of the green headed duck.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"hairui liu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2770","unix_group_name":"neck_shoulder","modified":"1701811647","downloads":"0","group_name":"A biomechanical model for joint functioning of neck and shoulder","logo_file":"neck_shoulder","short_description":"Currently, there exist freely available biomechanical models that include independent representations of both the neck and shoulder. To our knowledge, there are no open-source biomechanical models for joint functioning of neck and shoulder.\n","long_description":"The aim of this research is to construct biomechanical model for neck and shoulder joint functioning and to investigate the work of neck and shoulder muscles during head and arm movements numerically. The results of this study are expected to improve the understanding of the relationship between pathological changes in the cervical spine and the shoulder girdle. This could lead to the development of more effective treatments and improve the quality of life for patients.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Alexandra Yurova","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2778","unix_group_name":"hotspots","modified":"1703374866","downloads":"0","group_name":"Predicting hotspots for disease-causing single nucleotide variants","logo_file":"","short_description":"To enable personalized genetics and medicine, it is important yet highly challenging to accurately predict disease-causing mutations from the sequences alone at high throughput. To meet this challenge, we build upon recent progress in machine learning, ne","long_description":"To enable personalized genetics and medicine, it is important yet highly challenging to accurately predict disease-causing mutations from the sequences alone at high throughput. To meet this challenge, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (e.g., RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (e.g., Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 13 network centrality scores (calculated by NetworkX) with 7 other network scores calculated from the contact probability matrices to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively in comparison with alternative structures-based network scores (using predicted structures by AlphaFold). By optimally combing three coevolution analysis methods and the resulting network scores by machine learning, we are able to discriminate deleterious and neutral mutation sites accurately (AUC of ROC ~ 0.84). Furthermore, by combining our method with a state-of-the-art predictor of the functional effects of sequence variations based on large protein language models, we have significantly improved the prediction of disease variant sites (AUC ~ 0.89). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods via machine learning to predict candidate sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"W Zheng","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2780","unix_group_name":"in_vivo_valid","modified":"1705522130","downloads":"270","group_name":"Validation of Subject-Specific Knee Models from In Vivo Measurements","logo_file":"in_vivo_valid","short_description":"Working models, data, code, and results for work to validate that in vivo methods for measuring laxity and obtaining knee geometry are comparable to methods previously standard for knee modeling of in vitro specimens.","long_description":"Working models, data, code, and results for work to validate that in vivo methods for measuring laxity and obtaining knee geometry are comparable to methods previously standard for knee modeling of in vitro specimens.\n\nThis dataset is part of an ongoing manuscript to validate that sources of data from currently available in vivo methods are sufficient to create computational models of the knee compared with existing in vitro techniques. The data included in this repository is for the S192803 specimen of that dataset and includes experimental data, working models, code, and results obtained for that model and used in that manuscript.\n\nThe dataset contains experimental data, models, code, and results for the S192803 specimen data. This dataset is one of two model datasets used in the paper Validation of Subject-Specific Knee Models from In Vivo Measurements, which is in review at the Journal of Biomechanical Engineering. The dataset contained herein is derived from the experimental data collected during a previous publication in the Journal of Medical Devices, entitled: &quot;Apparatus for In Vivo Knee Laxity Assessment Using High-Speed Stereo Radiography&quot;. Available at: https://doi.org/10.1115/1.4051834\n\nA similar dataset exists for the other specimen, S193761.\n\nWork was created by Dr. Thor E. Andreassen, Dr. Donald R. Hume, Dr. Landon D. Hamilton, Stormy L. Hegg, Sean E. Higinbotham, and Dr. Kevin B. Shelburne at the Center for Orthopaedic Biomechanics at the University of Denver.\n\nThe work was funded by the NIH National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institute of Biomedical Imaging and Bioengineering, and the National Institute of Child Health and Human Development (Grant U01 AR072989).\n\nIf you have any questions, please email the main author, Dr. Thor Andreassen, at thor.andreassen@du.edu\n\nSharing/USE\n\nThis Code/Software is free to use for any reason. However, we ask that if you use any part of this work, that you cite the original two works that made it possible:\n\nAndreassen, T. E., Hamilton, L. D., Hume, D., Higinbotham, S. E., Behnam, Y., Clary, C., and Shelburne, K. B. (September 10, 2021). &quot;Apparatus for In Vivo Knee Laxity Assessment Using High-Speed Stereo Radiography.&quot; ASME. J. Med. Devices. December 2021; 15(4): 041004. https://doi.org/10.1115/1.4051834\n\nAndreassen, T. E., Hume, D. R., Hamilton, L. D., Hegg, S.L., Higinbotham, S. E., and Shelburne, K. B. &quot;Validation of Subject-Specific Knee Models from In Vivo Measurements.&quot; ASME. J. Biomech, Engineering.\n\nLiability Agreement\n\nThe Data is provided “as is” with no express or implied warranty or guarantee. The University of Denver and the Center for Orthopaedic Biomechanics do not accept any liability or provide any guarantee in connection with uses of the Data, including but not limited to, fitness for a particular purpose and noninfringement. The University of Denver and the Center for Orthopaedic Biomechanics are not liable for direct or indirect losses or damage, of any kind, which may arise through the use of this data.","has_downloads":true,"keywords":"Digital Twins,biomechanics,modelling,subject-specific,Finite Element Analysis","ontologies":"","projMembers":"Kevin Shelburne,Stormy Hegg,Donald Hume,Sean Higinbotham,Thor Andreassen,Landon Hamilton","trove_cats":[{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"310","fullname":"Neuromuscular System"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"318","fullname":"Models"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"400","fullname":"Data Sets"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"},{"id":"409","fullname":"Physics-Based Simulation"}],"is_toolkit":false,"is_model":true,"is_application":false,"is_data":true},{"group_id":"2786","unix_group_name":"oi-tfp-reduced","modified":"1705234911","downloads":"19","group_name":"The gait1415+2 OpenSim Musculoskeletal Model of Osseointegrated Transfemoral Amp","logo_file":"","short_description":"This short communication presents the gait1415+2 musculoskeletal model which has been developed in OpenSim to describe the lower extremity of a human subject with transfemoral amputation wearing a generic lower-limb bone-anchored prosthesis.","long_description":"This short communication presents the gait1415+2 musculoskeletal model, that has been developed in OpenSim to describe the lower-extremity of a human subject with transfemoral amputation wearing a generic lower-limb bone-anchored prosthesis. The model has fourteen degrees of freedom, governed by fifteen musculotendon units (placed at the contralateral and residual limbs) and two generic actuators (one placed at the knee joint and one at the ankle joint of the prosthetic leg). Even though the model is a simplified abstraction, it is capable of generating a human-like walking gait and, specifically, it is capable of reproducing both the kinematics and the dynamics of a person with transfemoral amputation wearing a bone-anchored prosthesis during normal level-ground walking. The model is released as support material to this short communication with the final goal of providing the scientific community with a tool for performing forward and inverse dynamics simulations, and for developing computationally-demanding control schemes based on artificial intelligence methods for lower-limb prostheses.\n\nRefer to this article: https://doi.org/10.1016/j.medengphy.2023.104091","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Raffaella Carloni,Vishal R","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2788","unix_group_name":"ankle","modified":"1703539354","downloads":"0","group_name":"Ankle rehabilitation robot based on parallel mechanism","logo_file":"","short_description":"Due to the fact that the motors of the series mechanism cannot be fixed and installed completely, the motion inertia is increased, and it is difficult to arrange the driving motor in a relatively small space. On the other hand, the parallel mechanism has ","long_description":"Due to the fact that the motors of the series mechanism cannot be fixed and installed completely, the motion inertia is increased, and it is difficult to arrange the driving motor in a relatively small space. On the other hand, the parallel mechanism has advantages such as small cumulative error, high accuracy, large load-bearing capacity, easy closed-loop control, stable motion, and flexible driving. Therefore, many scholars have conducted extensive design and research on rehabilitation robots based on parallel mechanisms.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jingke Song","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2801","unix_group_name":"imufpaaccuracy","modified":"1710249385","downloads":"2","group_name":"IMU-Based Foot Progression Angle Estimation Accuracy","logo_file":"","short_description":"This dataset consists of foot-mounted IMU and marker data from 30 subjects, which are used to estimate the foot progression angle. ","long_description":"Wearable inertial measurement units (IMUs) are used for estimating joint kinematics without  motion capture equipment. Real-time estimation of the foot progression angle (FPA) with IMUs is used for portable and customized gait retraining for knee osteoarthritis by providing feedback to the patient based on whether their FPA is close to their therapeutic target angle. However, the vibrotactile feedback that users receive directly depends on the accuracy of IMU-based kinematics. Here, we present data from 30 subjects used for an investigation into the effect of kinematic tracking accuracy on an individual's ability to learn a toe-in gait with vibrotactile cues.\n\nCitation: Rokhmanova N, Pearl O, Kuchenbecker KJ, Halilaj E (2024) IMU-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 10.1109/TNSRE.2024.3365204","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Eni Halilaj,Nataliya Rokhmanova,Katherine J. Kuchenbecker,Owen Pearl","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2802","unix_group_name":"testt","modified":"1705958704","downloads":"0","group_name":"Ankle Foot Orthosis Simulation","logo_file":"","short_description":"Want to see if I can simulate ground reaction forces and muscle forces on an ankle foot orthosis","long_description":"Want to see if I can simulate ground reaction forces and muscle forces on an ankle foot orthosis","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jaiya Morphet","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2804","unix_group_name":"cai_ankle_model","modified":"1706036261","downloads":"0","group_name":"Capstone Project, Chronic Ankle Instability","logo_file":"","short_description":"We are working to quantify ligament laxity in chronic ankle instability and measure how it changes with operative and non-operative treatments","long_description":"We are working to quantify ligament laxity in chronic ankle instability and measure how it changes with operative and non-operative treatments","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Madeleine Krotine","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2809","unix_group_name":"msdinkitchen","modified":"1706209314","downloads":"0","group_name":"Prevalance of Musculoskeletal disorders in Commercial Kitchen Workers","logo_file":"","short_description":"This project aims to address the prevalent issue of Musculoskeletal Disorders (MSDs) among commercial kitchen workers in the food industry. Recognizing the physically demanding nature of their work, the project focuses on implementing ergonomic interventi","long_description":"This project aims to address the prevalent issue of Musculoskeletal Disorders (MSDs) among commercial kitchen workers in the food industry. Recognizing the physically demanding nature of their work, the project focuses on implementing ergonomic interventions to enhance working conditions, reduce the risk of injuries, and improve the overall well-being of kitchen staff.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Gokul T","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2813","unix_group_name":"pred_sims","modified":"1706558005","downloads":"0","group_name":"Predictive Simulations of Top Speed Sprinting","logo_file":"","short_description":"I am just beginning to create a project - detailed explanation to follow.","long_description":"I am just beginning to create a project - detailed explanation to follow.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Nicos Haralabidis","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2818","unix_group_name":"hdtdm","modified":"1707154372","downloads":"0","group_name":"Human Digital Twin for Diabetes Reversal","logo_file":"","short_description":"We are building a human digital twin for diabetes reversal using SimTK.","long_description":"We are building a human digital twin for diabetes reversal using SimTK.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Arin Basu","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2821","unix_group_name":"shadowfax","modified":"1711102243","downloads":"21","group_name":"Musculoskeletal model of the horse (Equus ferus caballus) for gait simulations","logo_file":"shadowfax","short_description":"This project will contain all the relevant files for our project &quot;SHADOWFAX&quot; - Simulated Horse Anatomy Demonstrating Optimal Walking &amp; Fast ACCeleration. This project page will include model files, simulator outputs, and (matlab) code for simulations in O","long_description":"This project will contain all the relevant files for our project &quot;SHADOWFAX&quot; - Simulated Horse Anatomy Demonstrating Optimal Walking &amp; Fast ACCeleration. This project page will include model files, simulator outputs, and (matlab) code for simulations in OpenSim Moco.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Pasha van Bijlert,Thomas Geijtenbeek","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2822","unix_group_name":"1facob","modified":"1707335269","downloads":"0","group_name":"Biomechanics of Flight attendants stowing baggage in overhead bins","logo_file":"","short_description":"This project analyses the tasks and biomechanics involved in cabin crew moving carry-on baggage from the cabin floor to an overhead bin.","long_description":"This project analyses the tasks and biomechanics involved in cabin crew moving carry-on baggage from the cabin floor to an overhead bin.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"darlene maclachlan","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2824","unix_group_name":"pelvic_sarcoma","modified":"1707700786","downloads":"0","group_name":"Changes in Walking Function and Neural Control following Pelvic Cancer Surgery","logo_file":"","short_description":"Changes in Walking Function and Neural Control following Pelvic Cancer Surgery with Reconstruction: A Case Study\n\nSurgical planning and custom prosthesis design for pelvic cancer patients is challenging due to the unique clinical characteristics of each","long_description":"Changes in Walking Function and Neural Control following Pelvic Cancer Surgery with Reconstruction: A Case Study\n\nSurgical planning and custom prosthesis design for pelvic cancer patients is challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. This case study combined comprehensive human movement data collection with personalized neuromusculoskeletal computer modeling to provide a thorough assessment of pre- to post-surgery changes in walking function and neural control for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Extensive walking data (video motion capture, ground reaction, and EMG) were collected from the patient before surgery and after plateau in recovery after surgery. Pre- and post-surgery personalized neuromusculoskeletal computer models of the patient were then constructed using the patient’s pre- and post-surgery walking data. These models were used to calculate the patient’s pre- and post-surgery joint motions, joint moments, and muscle synergies. The calculated muscle synergies were described by time-invariant synergy vectors and time-varying synergy activations, were consistent with the patient’s experimental EMG data, and produced the patient’s experimental joint moments found via inverse dynamics. The patient’s post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the patient’s post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified synergy vectors) with little change in recruitment timing (as evidenced by conserved synergy activations). Furthermore, the patient’s post-surgery muscle activations were fitted accurately using the patient’s pre-surgery synergy activations but poorly using the patient’s pre-surgery synergy vectors. These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient’s pre-surgery walking data and the planned surgical decisions and custom prosthesis design.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Geng Li,B.J. Fregly","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2827","unix_group_name":"ocarabeo_2030","modified":"1707759267","downloads":"0","group_name":"AI, NEUROSCIENCE","logo_file":"","short_description":"The model simulates the Neurophysiologic network of the patient’s most vital electrodes and entry points location on anatomy in the area of the back.\n“Propose a virtual planning to visualize the responses to the treatment on a computer before the act","long_description":"The model simulates the Neurophysiologic network of the patient’s most vital electrodes and entry points location on anatomy in the area of the back.\n“Propose a virtual planning to visualize the responses to the treatment on a computer before the actual intervention.”\nDevelop intelligent algorithms that generate digital models of the brain, neural networks, ionic channels, reverberatory circuitry, novel electromagnetic asymmetric biphasic waveforms, and neurophysiologic stimulation of the nerves innervating the pancreas, \nand which the nerves innervating the pancreas and its beta cells are reached by such waveform through the Vagus nerve, celiac ganglia, and associated complexes.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Oresteban Carabeo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2832","unix_group_name":"femurhuman","modified":"1707947283","downloads":"0","group_name":"Building fracture model to predict GRF for femur fracture","logo_file":"","short_description":"We have fracture forces from mechanical test performed on human femurs. We now want to build a fracture model to predict GRF in individuals in our study based not heir height and weight to compare it to their actual fracture force","long_description":"We have fracture forces from mechanical test performed on human femurs. We now want to build a fracture model to predict GRF in individuals in our study based not heir height and weight to compare it to their actual fracture force","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Rachana  Vaidya","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2835","unix_group_name":"shoulder-model","modified":"1712631863","downloads":"0","group_name":"Personalizable Kinematic Shoulder Model","logo_file":"","short_description":"This project provides a kinematic shoulder model that can be personalized via the NMSM Pipeline. Included are all scripts and data necessary to develop a personalized shoulder model.","long_description":"This project provides a kinematic shoulder model that can be personalized via the NMSM Pipeline. Included are all scripts and data necessary to develop a personalized shoulder model.","has_downloads":true,"keywords":"","ontologies":"","projMembers":"Claire V. Hammond,B.J. Fregly,Jonathan Gustafson","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2836","unix_group_name":"ms-spectra","modified":"1708500268","downloads":"0","group_name":"MALDI-TOF MS spectra of E.coli strains","logo_file":"","short_description":"Here are MS spectrum data of 48 E.coli strains. A single MS spectrum is listed in each line of the text file.","long_description":"Here are MS spectrum data of 48 E.coli strains. A single MS spectrum is listed in each line of the text file.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Jin Ling","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2840","unix_group_name":"recruitment","modified":"1708711198","downloads":"0","group_name":"Lower limb analysis","logo_file":"","short_description":"recruitment and analysis","long_description":"recruitment and analysis","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Akshara Shanmuganand","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2842","unix_group_name":"opensim-creator","modified":"1709717418","downloads":"0","group_name":"OpenSim Creator","logo_file":"opensim-creator","short_description":"OpenSim Creator is open-source software for creating/modifying OpenSim models, available at https://opensimcreator.com\n\n&lt;img src=&quot;https://opensimcreator.com/img/gallery/0.5.0_Gorilla.png&quot; alt=&quot;screenshot&quot; /&gt;","long_description":"It's a UI that has tooling for:\n\n- Editing model properties\n- Visually inspecting the model\n- Plotting/tweaking model parameters, with live updates\n- Specialized tasks, such as importing and warping meshes\n\nAvailable from https://opensimcreator.com, or its GitHub repository (https://github.com/ComputationalBiomechanicsLab/opensim-creator).\n","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Ajay Seth,Adam Kewley","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2848","unix_group_name":"ms_shoulder","modified":"1709834872","downloads":"0","group_name":"Upper limb model","logo_file":"","short_description":"I have to create an upper limb model to study the dynamics and kinematics correlated to muscle activation for my master's thesis","long_description":"I have to create an upper limb model to study the dynamics and kinematics correlated to muscle activation for my master's thesis","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Giacomo Zafalon","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2849","unix_group_name":"biomechma","modified":"1710440499","downloads":"0","group_name":"Biomechanic Moments","logo_file":"","short_description":"A project to build and understand moment arms throughout the range of motion in physical activity movements","long_description":"A project to build and understand moment arms throughout the range of motion in physical activity movements","has_downloads":false,"keywords":"","ontologies":"","projMembers":"JP Pie","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2850","unix_group_name":"bodyweightwalk","modified":"1711033788","downloads":"0","group_name":"Modeling the contribution of force required to walk","logo_file":"","short_description":"Modeling the contribution of force provided by certain muscle groups required to walk, as measured as a percentage of body weight. For example, measuring the force provided during hip abduction in a stride made by a 180 lb person. This may be the force eq","long_description":"Modeling the contribution of force provided by certain muscle groups required to walk, as measured as a percentage of body weight. For example, measuring the force provided during hip abduction in a stride made by a 180 lb person. This may be the force equivalent to 250% BW.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Emily Kate Freeman","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2866","unix_group_name":"manley_thesis","modified":"1711387584","downloads":"0","group_name":"Barefoot Resistance Training on Time to Stabilization (TTS) and Ankle Inversion","logo_file":"","short_description":"Looking at the subtalar_ankle angle upon landing. Investigating the effect of four weeks of barefoot resistance training on ankle inversion in NCAA Division 1 female volleyball players.","long_description":"Looking at the subtalar_ankle angle upon landing. Investigating the effect of four weeks of barefoot resistance training on ankle inversion in NCAA Division 1 female volleyball players.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Logan Manley","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2869","unix_group_name":"mocas","modified":"1711575623","downloads":"0","group_name":"Simulating Motion Capture Data","logo_file":"","short_description":"My project aims at simulation the data obtained from motion analysis and that contribute to the VGRF. The labels I have are based on the markers and I want to know where the markers are placed and see how they are being simulated.","long_description":"My project aims at simulation the data obtained from motion analysis and that contribute to the VGRF. The labels I have are based on the markers and I want to know where the markers are placed and see how they are being simulated.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"SHOOG Nimri","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2871","unix_group_name":"besity_femur","modified":"1711647783","downloads":"0","group_name":"Stress in knee joint","logo_file":"","short_description":"How would force from people with obesity body will affect the knee joint and posture of the femur bone?","long_description":"How would force from people with obesity body will affect the knee joint and posture of the femur bone?","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Arunsawad Vipamaneeroj","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2878","unix_group_name":"tenrotcuff1","modified":"1712249941","downloads":"0","group_name":"Biomechanics: Modeling Tennis serves","logo_file":"","short_description":"Modeling the shoulder (4 muscles around the rotator cuff) to see how much force is applied with an overhead serve versus a volley (side) serve.","long_description":"Modeling the shoulder (4 muscles around the rotator cuff) to see how much force is applied with an overhead serve versus a volley (side) serve.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Madison  Gilmore","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2880","unix_group_name":"sports_model","modified":"1712353194","downloads":"0","group_name":"Full-body model that improves upper body tracking for dynamic cutting motions","logo_file":"","short_description":"The goal of this work was to augment the capability of the currently most widely used full-body model (Rajagopal) to improve the tracking of the kinematics of the head, shoulder, arms and torso during complex/coordinated full-body motions, such as cutting","long_description":"The goal of this work was to augment the capability of the currently most widely used full-body model (Rajagopal) to improve the tracking of the kinematics of the head, shoulder, arms and torso during complex/coordinated full-body motions, such as cutting maneuvers in Football. We achieved this goal by adding 3 joints in the spine and 2 joints between clavicles and sternum based on the existing models of various body segments (Vasavada, Li et al. 1998, Saul, Hu et al. 2014). \nWe tested the model by comparing the inverse kinematics and inverse dynamics from specific movements often involved in American football games from 16 collegiate football players based on the augmented full-body model vs. the original full-body model (Rajagopal, Dembia et al. 2016). These comparisons showed a significant improvement in tracking the kinematics of the upper body, which then led to reduced dynamic inconsistency in inverse dynamics, in the augmented full-body model.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Shawn Russell","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2883","unix_group_name":"chloeh_wss","modified":"1712596536","downloads":"0","group_name":"Hemodynamics Wall Shear Stress","logo_file":"","short_description":"- Studying hemodynamic wall shear stress on cardiovascular health. \n- Gaining insights into the onset of atherosclerosis from different factors of blood vessels (geometries, angles, locations) and hemodynamics.\n- Investigate plaque and normal blood vess","long_description":"- Studying hemodynamic wall shear stress on cardiovascular health. \n- Gaining insights into the onset of atherosclerosis from different factors of blood vessels (geometries, angles, locations) and hemodynamics.\n- Investigate plaque and normal blood vessels to compare how they affect the blood flow and their contribution to the onset of sclerosis.\n- Possibly linking hemodynamics to the onset of cardiovascular diseases, in this instance, atherosclerosis and aneurysms.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Chloe Huynh","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2885","unix_group_name":"mirakos","modified":"1712687300","downloads":"0","group_name":"Musculoskeletal model of the MIRAKOS study","logo_file":"","short_description":"This project shares the musculoskeletal model used for the analysis of walking and forward lunge trials of the MIRAKOS study. The model builds upon a model described in Bedo, BLS; Catelli, DS; Lamontagne, M; Santiago, PRP. A custom musculoskeletal model f","long_description":"This project shares the musculoskeletal model used for the analysis of walking and forward lunge trials of the MIRAKOS study. The model builds upon a model described in Bedo, BLS; Catelli, DS; Lamontagne, M; Santiago, PRP. A custom musculoskeletal model for estimation of medial and lateral tibiofemoral contact forces during tasks with high knee and hip flexions. Computer Methods in Biomechanics and Biomedical Engineering, in press. doi:10.1080/10255842.2020.1757662 (2020)","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Lauri Stenroth","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2886","unix_group_name":"mocap-1-basket","modified":"1712687344","downloads":"0","group_name":"Motion Capture Basketball and Volleyball","logo_file":"","short_description":"Motion capture of basketball layup and of volleyball dive.","long_description":"Motion capture of basketball layup and of volleyball dive.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Karla Carrillo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2894","unix_group_name":"sts_hip1","modified":"1712980546","downloads":"0","group_name":"Sit-to-Stand motion. stress on hip implant","logo_file":"","short_description":"Understanding sit-to-stand motion to evaluate stress created on hip joint. The purpose of this project is to figure out the optimal angle/seat length a patient can sit at, to minimize stress on hip implant during sit-to-stand motion.","long_description":"Understanding sit-to-stand motion to evaluate stress created on hip joint. The purpose of this project is to figure out the optimal angle/seat length a patient can sit at, to minimize stress on hip implant during sit-to-stand motion.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Srividya Kuppa,Mandeep Johal","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2895","unix_group_name":"biomechanics24","modified":"1713200053","downloads":"0","group_name":"Biomechanics Project Spring 2024-Group 14","logo_file":"","short_description":"Hypothesis: \" How different squad depths can affect compression force in the knee joint.\"","long_description":"Hypothesis: &quot; How different squad depths can affect compression force in the knee joint.&quot;","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Esmeralda Crespo","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2899","unix_group_name":"2-footjump","modified":"1713289898","downloads":"0","group_name":"jumping model","logo_file":"","short_description":"It is a two-foot jumping model to use to help my class research project on biomechanics.","long_description":"It is a two-foot jumping model to use to help my class research project on biomechanics.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Michael Marriott","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false},{"group_id":"2907","unix_group_name":"hololens24","modified":"1713810872","downloads":"0","group_name":"cardiac blood flow","logo_file":"","short_description":"designed for detailed understanding of cardiac blood flow with tumor in heart.","long_description":"designed for detailed understanding of cardiac blood flow with tumor in heart.","has_downloads":false,"keywords":"","ontologies":"","projMembers":"Mahesh Ravichandran","trove_cats":[],"is_toolkit":false,"is_model":false,"is_application":false,"is_data":false}]