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18 projects in result set.
Are subject-specific musculoskeletal models robust to parameter identification?
- This study analyzed the sensitivity of the predictions of an MRI-based musculoskeletal model (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the unavoidable uncertainties in parameter identification, i.e., body landmark positions, maximum muscle tension and musculotendon geometry. 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. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. | |
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Activity Percentile: 93.13 Registered: 2014-11-10 15:19 |
A three-dimensional musculoskeletal model of the dog
- 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. | |
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Registered: 2020-11-30 08:11 |
ACL Reconstruction Decision Support Through Personalized Simulation of the Lachm
- The objective of the proposed approach is to develop a clinical decision support system (DSS) that will help clinicians optimally plan the ACL reconstruction procedure in a patient specific manner.
Methods: A full body model is developed in this study with 23 degrees of freedom and 93 muscles. The knee ligaments are modeled as non-linear spring-damper systems and a tibiofemoral contact model was utilized. The parameters of the ligaments were calibrated based on an optimization criterion. Forward dynamics were utilized during simulation for predicting the model’s response to a given set of external forces, posture configuration and physiological parameters.
Results: The proposed model is quantified using MRI scans and measurements of the well-known Lachman test, on several patients with a torn ACL. The clinical potential of the proposed framework is demonstrated in the context of flexion-extension, gait and jump actions. The clinician is able to modify and fine tune several parameters such as number of bundles, insertion position on the tibia or femur and the resting length that correspond to the choices of the surgical procedure and study their effect on the biomechanical behavior of the knee.
Conclusion: Computational knee models can be used to predict the effect of surgical decisions and to give insight on how different parameters can affect the stability of the knee. Special focus has to be given in proper calibration and experimental validation.
<iframe width="560" height="315" src="https://www.youtube.com/embed/zgcq0c5_w3c" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> | |
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Activity Percentile: 46.95 Registered: 2015-08-31 08:55 |
Musculoskeletal model sensitivity to soft tissue artefacts
- 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. | |
Registered: 2016-09-19 13:22 |
ForceBalance : Systematic Force Field Optimization
- ForceBalance is free software for force field optimization.
It facilitates the development of more accurate force fields using a systematic and reproducible procedure.
ForceBalance is highly versatile and can optimize nearly any set of parameters using experimental measurements and/or ab initio calculations as reference data.
<b>SOURCE CODE:</b> For the newest features, visit the GitHub source code repository at https://github.com/leeping/forcebalance.
The SVN repository on the left frame is an outdated archive.
<b>RELEASES:</b> Stable versions of the code updated once every few months. Click "Releases" on the left frame for the most recent release and notes.
<b>CONTACT:</b> Please contact me (Lee-Ping, right frame) if you have questions or comments! | |
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Registered: 2011-12-20 17:04 |
Multicore parallel computing with OpenSim Moco
- 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.
This work is described in the paper "Computational performance of musculoskeletal simulation in OpenSim Moco using parallel computing" 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. | |
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Registered: 2023-08-21 23:33 |
Neuromuscular Models for the Predictive Treatment of Parkinson's Disease
- NoTremor aims to provide patient specific computational models of the coupled brain and neuromuscular systems that will be subsequently used to improve the quality of analysis, prediction and progression of Parkinson’s disease. In particular, it aspires to establish the neglected link between brain modelling and neuromuscular systems that will result in a holistic representation of the physiology for PD patients. | |
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Activity Percentile: 17.56 Registered: 2014-06-18 13:56 |
Neuromusculoskeletal Modeling (NMSM) Pipeline
- <div style="display:inline-block"><a href="https://nmsm.rice.edu"><img src="https://nmsm.rice.edu/img/nmsm-pipeline-social-card.jpg" style="float:left;max-width:calc(100% - 40px);"></a></div>
Full project information is available at: https://nmsm.rice.edu. Please direct any inquiries about the NMSM Pipeline to us by posting your questions on this SimTK project forum or emailing nmsm@rice.edu.
Neuromusculoskeletal Modeling (NMSM) Pipeline is a set of tools for personalizing models and designing treatments for movement impairments and other pathologies.
The NMSM Pipeline consists of two toolsets:
Model Personalization - Personalize joint, muscle-tendon, neural control, and ground contact model properties.
Treatment Optimization - Design treatments using personalized models and an optimal control methodology.
At this time, Treatment Optimization requires the use of <a href="https://www.gpops2.com/">GPOPS-II optimal control solver</a>.
The NMSM Pipeline is written in MATLAB to lower the barrier for entry and to facilitate accessibility to the core codebase. We encourage users to modify the code to meet their needs.
The core codebase and examples are available to download for use in research. 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.
If you need help or want to start a discussion, please use the SimTK forum for this project.
Note: This project is a living entity. Updates will be made available as the Pipeline, examples, and tutorials are developed further and improved. | |
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Registered: 2022-07-07 14:55 |
Proteolytic and non-proteolytic regulation of collective cell invasion
- Cancer cells manoeuvre through extracellular matrices (ECMs) using different invasion modes, including single cell and collective cell invasion. These modes rely on MMP-driven ECM proteolysis to make space for cells to move. How cancer-associated alterations in ECM influence the mode of invasion remains unclear. Further, the sensitivity of the two invasion modes to MMP dynamics remains unexplored. In this paper, we address these open questions using a multiscale hybrid computational model combining ECM density-dependent MMP secretion, MMP diffusion, ECM degradation by MMP and active cell motility. Our results demonstrate that in randomly aligned matrices, collective cell invasion is more efficient than single cell invasion. Although increase in MMP secretion rate enhances invasiveness independent of cell–cell adhesion, sustenance of collective invasion in dense matrices requires high MMP secretion rates. However, matrix alignment can sustain both single cell and collective cell invasion even without ECM proteolysis. Similar to our in-silico observations, increase in ECM density and MMP inhibition reduced migration of MCF-7 cells embedded in sandwich gels. Together, our results indicate that apart from cell intrinsic factors (i.e., high cell–cell adhesion and MMP secretion rates), ECM density and organization represent two important extrinsic parameters that govern collective cell invasion and invasion plasticity. | |
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Activity Percentile: 0.00 Registered: 2016-03-07 06:05 |
Application for the simulation of the prosthetic gait
- This application has a dataset belonging to macha prosthetic patterns , in which the angle of the socket and prosthetic foot is changed.
It focuses on patients with transtibial amputation and uses opensim in MATLAB libraries to link and generate a model for opensim , based on data captured from a measuring TECHNAID brand. | |
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Registered: 2016-08-24 14:21 |
3D Numerical Investigation of Endothelial Shear Stress in Arteries
- 3D numerical investigation of endothelial shear stress in coronary arteries. | |
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Activity Percentile: 0.00 Registered: 2015-11-30 13:34 |
The Musculoskeletal Atlas Project
- 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. The MAP Client uses statistical shape modelling to provide a best-match to your mocap and medical imaging data and generate surface geometry to generate an OpenSim model.
MAPClient Homepage :
http://map-client.readthedocs.io/en/latest/
MAPClient - FAI Workshop :
http://map-client-fai-workshop.readthedocs.io/en/stable/
Git Hub Repo Generic MAP plugins :
https://github.com/mapclient-plugins
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Activity Percentile: 0.00 Registered: 2012-08-15 00:07 |
Probabilistic Tool for Considering Patient Populations & Model Uncertainty
- 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. | |
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Activity Percentile: 0.00 Registered: 2013-08-30 19:54 |
The Reference Model for Disease Progression
- The Reference Model is now:
• <a href="http://dx.doi.org/10.7759/cureus.9455" target="_blank"> COVID-19 model for US states and territories </a>
• <a href="https://jacob-barhak.github.io/InteractivePoster_MSM_IMAG_2019.html" target="_blank"> The Most Validated Cardiovascular (CVD) Diabetes Model known </a>
• <a href="https://patents.google.com/patent/US20140297241A1/en" target="_blank"> United States Patent 9,858,390</a>
• <a href="https://patents.google.com/patent/US20170286627A1/en" target="_blank"> United States Patent Number 10,923,234</a>
The Reference Model can now:
• Attempt to explain COVID-19 for US states
• Determine CVD models that significantly behave better on several diabetic populations
• Deduce that CVD probability halves every 5 years due to medicine improving - according to information from the last 3 decades
• Calculate life tables for diabetics
• Interface with ClinicalTrials.Gov
• Include human interpretation in the model
• Create an interactive map of our <a href="https://jacob-barhak.github.io/InteractivePoster_MSM_IMAG_2019.html" target="_blank"> <b> cumulative computational knowledge gap</b>
<a href="http://dx.doi.org/10.7759/cureus.9455" target="_blank"> <b> COVID-19 MODEL</b> </a>
The 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.
<iframe width="1000" height="400" src="https://jacob-barhak.netlify.app/thereferencemodel/results_covid19_2020_06_27/populationplot" frameborder="0" > </iframe>
<a href="https://simtk.org/projects/mist" target="_blank"> <b> TECHNOLOGY </b> </a>
The 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 <a href="https://simtk.org/projects/mist" target="_blank"> MIcro Simulation Tool (MIST) </a> .
MIST 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 <a href="https://clinicaltrials.gov/" target="_blank" > ClinicalTrials.Gov</a> while maintaining tractability and reproducibility.
<b> <a href="https://simtk.org/plugins/simtk_news/index.php?group_id=1286" target="_blank"> PUBLICATIONS: </a> </b>
The Reference Model was created in 2012 and evolved since then. You can find key developments and publications by year in the <a href="https://simtk.org/plugins/simtk_news/index.php?group_id=1286" target="_blank"> news section </a>.
Here are some videos describing the Model:
This video gives a brief introduction
<iframe width="800" height="450" src="https://www.youtube.com/embed/s9L-qFF84Ew" title="The Reference Model for Disease Progression: Explaining COVID-19" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
This video will shows recent results of explaining COVID-19 using USA data:
<iframe width="800" height="450" src="https://www.youtube.com/embed/1M645o5gWrc" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
This video will show a breakthrough of becoming the first multiscale ensemble model for COVID-19:
<iframe width="800" height="450" src="https://www.youtube.com/embed/-z8N40TdKDk?start=1860" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
This video explains the model in a larger context as presented in AnacondaCon 2019:
<iframe width="800" height="450" src="https://www.youtube.com/embed/fQIYMf5wKGE" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
This video explains how human interpretation can be used as presented in the Multiscale Viral Pandemics working group webinar:
<iframe width="800" height="450" src="https://www.youtube.com/embed/aTB8-XEZheU" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
This video summarizes a decade of work as presented in PyTexas 2017:
<iframe width="800" height="450" src="https://www.youtube.com/embed/Pj_N4izLmsI?list=PL0MRiRrXAvRiwQUUwTTh5g8rhbQyYlubo" frameborder="0" gesture="media" allow="encrypted-media" allowfullscreen></iframe>
This describes the evolution of the model up to 2016 presented in PyTexas:
<iframe width="800" height="450" src="https://www.youtube.com/embed/htGRRjia-QQ" frameborder="0" allowfullscreen></iframe>
This describes the work presented in PyData in 2014:
<iframe width="800" height="450" src="https://www.youtube.com/embed/vyvxiljc5vA" frameborder="0" allowfullscreen></iframe> | |
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Registered: 2017-05-09 05:34 |
qFit: Multiconformer modeling from X-ray crystallography.
- qFit is a method for automatically disentangling and modeling alternative conformations and their associated occupancies, which are represented by the variable q (short for “occupancy”) in standard structure factor equations. The qFit algorithm examines a vast number of alternative interpretations of the X-ray electron density map simultaneously. It selects a set of one to four conformations for each residue that, collectively, optimally explain the electron density in real space. | |
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Activity Percentile: 0.00 Registered: 2015-05-01 21:07 |
Computational Analysis of Kinase Selectivity using Structural Knowledge
- 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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Registered: 2017-04-18 20:03 |
Statistical Shape Model of the Knee
- A statistical shape and alignment model was created for the structures of the knee: the femur, tibia and patella, associated articular cartilage, and soft tissue structures for a training set of 50 subjects/specimens. 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.
The 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.
As 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.
This work was supported by the National Science Foundation, General and Age Related Disabilities Engineering under CBET Grant: 1034251.
"Population-based evaluation of knee mechanics considering inter-subject and surgical alignment variability"
Investigators: P. Laz, P. Rullkoetter, D. Dennis, R. Kim | |
Activity Percentile: 0.00 Registered: 2014-09-12 19:17 |
FPSM - French Pediatric Shoulder Model to evaluate shoulder joint disorders
- 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.
This 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.
This project is currently funded by Region of Brittany, France; IMT Atlantique, Brest, France, Campus France, INSERM, and CHRU de Brest. | |
Registered: 2018-06-27 13:33 |