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This project provides downloads organized into three packages: software, data and results related to the publication.
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1. Software
This package contains the software developed (NMSBuilder and PMM) and used in the present paper. NMSBuilder allows biomedical data processing and creation of subject-specific musculoskeletal models for OpenSim. It includes a graphical user interface that integrates the functionalities of the Multimode Application Framework and the OpenSim API. The Probabilistic Musculoskeletal Modeling module interfaces MATLAB and the OpenSim API to access, visualize and modify model variables, and repeatedly run simulations of movement.
Release 2.0
Nov 24, 2016

nmsBuilder v2.0, including major software enhancement, has been released via nmsbuilder.org  View License

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Nov 23, 2016
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Any
Other

Nov 23, 2016
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Windows
Application/binary
nmsBuilder is a freely-available software application to process biomedical data and create subject-specific musculoskeletal models for OpenSim. nmsBuilder v2.0 features a major software refactoring, including integration of the OpenSim 3.3 library and Application Programming Interface, upgrades to latest Multimode Application Framework library, and graphical restyling. Particularly, no additional software is needed to create and export OpenSim models, and the concept of the modeling pre-processing workflow is completely new. The result is a more stable, user-friendly and advanced version of the application.

Documentation Links

Nov 23, 2016
nmsBuilder is a freely-available software application to process biomedical data and create subject-specific musculoskeletal models for OpenSim. nmsBuilder v2.0 features a major software refactoring, including integration of the OpenSim 3.3 library and Application Programming Interface, upgrades to latest Multimode Application Framework library, and graphical restyling. Particularly, no additional software is needed to create and export OpenSim models, and the concept of the modeling pre-processing workflow is completely new. The result is a more stable, user-friendly and advanced version of the application.

PLEASE CITE THESE PAPERS

Valente G, Pitto L, Testi D, Seth A, Delp SL, Stagni R, Viceconti M, Taddei F. Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification? PLoS ONE 9(11): e112625. doi:10.1371/journal.pone.0112625 (2014) View

Previous Releases


Release 1.0

Nov 11, 2014

  View License

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NMSBuilder

Nov 14, 2014
82 MB
Windows
Application/binary
NMSBuilder 1.0 Installer

Nov 14, 2014
137 MB
Windows
Application/binary
Additional Software for NMSBuilder

Probabilistic Module

Nov 14, 2014
126 KB
Windows
Application/binary
Probabilistic Module

Documentation Links

NMSBuilder

Nov 14, 2014
NMSBuilder1.0 User Manual


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2. Data
This package contains MRI (DICOM), gait data (trc and mot files) and the sampled input variables (MAT-file) used in the study to create the image-based musculoskeletal model and run probabilistic simulations of gait.
Release 1.0
Nov 11, 2014

  View License

Download Links

Nov 14, 2014
26 MB
Any
Data/images/video
Raw MRI Data

Nov 14, 2014
1 MB
Any
Data/images/video
Input variables sampled according to their statistical distribution

Nov 14, 2014
104 KB
Any
Data/images/video
Gait Data

PLEASE CITE THESE PAPERS

Valente G, Pitto L, Testi D, Seth A, Delp SL, Stagni R, Viceconti M, Taddei F. Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification? PLoS ONE 9(11): e112625. doi:10.1371/journal.pone.0112625 (2014) View


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3. Results
This package contains post-processed results (MAT-files) of the probabilistic simulations of gait
Release 1.0
Nov 11, 2014

  View License

Download Links

Nov 14, 2014
188 KB
Any
Data/images/video
Mean and standard deviation values of the stochastic output variables

Nov 14, 2014
1 MB
Any
Data/images/video
Correlations between joint contact forces and all input variables

PLEASE CITE THESE PAPERS

Valente G, Pitto L, Testi D, Seth A, Delp SL, Stagni R, Viceconti M, Taddei F. Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification? PLoS ONE 9(11): e112625. doi:10.1371/journal.pone.0112625 (2014) View


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