AboutDownloadsDocumentsForumsSource CodeIssues
Primary Publication
A. Mantoan, C. Pizzolato, M. Sartori, Z. Sawacha, C. Cobelli, M. Reggiani MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation, Source Code for Biology and Medicine (2015) 10:12; DOI: 10.1186/s13029-015-0044-4 (2015)  View
Abstract

Background. Neuromusculoskeletal modeling and simulation enable investigation of the neuromusculoskeletal system and its role in human movement dynamics. These methods are progressively introduced into daily clinical practice. However, a major factor limiting this translation is the lack of robust tools for the pre-processing of experimental movement data for their use in neuromusculoskeletal modeling software. Results. This paper presents MOtoNMS (matlab MOtion data elaboration TOolbox for NeuroMusculoSkeletal applications), a toolbox freely available to the community, that aims to fill this lack. MOtoNMS processes experimental data from different motion analysis devices and generates input data for neuromusculoskeletal modeling and simulation software, such as OpenSim and CEINMS (Calibrated EMG-Informed NMS Modelling Toolbox). MOtoNMS implements commonly required processing steps and its generic architecture simplifies the integration of new user-defined processing components. MOtoNMS allows users to setup their laboratory configurations and processing procedures through user-friendly graphical interfaces, without requiring advanced computer skills. Finally, configuration choices can be stored enabling the full reproduction of the processing steps. MOtoNMS is released under GNU General Public License and it is available at the SimTK website and from the GitHub repository. Motion data collected at four institutions demonstrate that, despite differences in laboratory instrumentation and procedures, MOtoNMS succeeds in processing data and producing consistent inputs for OpenSim and CEINMS. Conclusions. MOtoNMS fills the gap between motion analysis and neuromusculoskeletal modeling and simulation. Its support to several devices, a complete implementation of the pre-processing procedures, its simple extensibility, the available user interfaces, and its free availability can boost the translation of neuromusculoskeletal methods in daily and clinical practice.

Related Publications
A. Mantoan, M. Reggiani, M. Sartori, Z. Sawacha, C. Pizzolato, and C. Cobelli, A Matlab generic tool to efficiently process C3D files for applications in OpenSim, XXIV Congress of the International Society of Biomechanics, Natal Rio Grande do Norte, Brazil, 4-9 August 2013 (2013)
A. Mantoan and M. Reggiani (2015), MOtoNMS v2.2. DOI: 10.5281/zenodo.18690 (2015)  View
Abstract

Matlab MOtion data elaboration TOolbox for NeuroMusculoSkeletal applications (MOtoNMS) is a freely available Matlab toolbox that aims at providing a complete tool for processing movement data for their use in neuromusculoskeletal software. It is able to read motion data stored in C3D files and process marker trajectories, ground reaction forces, and EMG signals for the OpenSim and CEINMS software. MOtoNMS has been design to be flexible and highly configurable: users can easily setup their own laboratory and processing procedures, without constraints in instruments, software, protocols, and methodologies. At the same time it is also quite simple to use thanks to its graphical user-friendly interfaces that help users in configuring the process without any change in Matlab code. MOtoNMS also improves the data organization, providing a clear structure of input data and automatically generating output directories. Release 2.2 has been updated to support two largely required features: trials with different directions of motion force plates with pads MOtoNMS 2.2 also provides the users with additional logging information about the EMG processing steps. An new output folder (maxemg) is created for each dynamic elaboration, with plots and log data related to the computation of maximum EMG values. A last objective of this release is improving the processing of marker trajectories. Users have the possibility to define the gaps'''' maximum size that will be interpolated, and a piecewise filtering has been implemented for markers trajectories that still have NaN values. Finally, computation of joint centers in Static Elaboration is no more mandatory.

A. Mantoan, M. Reggiani, M. Sartori, C. Pizzolato, Z. Sawacha and C. Cobelli, A Matlab Platform to efficiently process kinematic and kinetic data for application in OpenSim, 2013 Annual Conference of GCMAS, Cincinnati, Ohio, 14-17 May 2013 (2013)
Feedback