OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement

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Abstract

Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human–device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim’s design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.

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Most cited references 64

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Muscle contributions to propulsion and support during running.

Samuel R Hamner, Ajay Seth, Scott L. Delp (2010)

Muscles actuate running by developing forces that propel the body forward while supporting the body's weight. To understand how muscles contribute to propulsion (i.e., forward acceleration of the mass center) and support (i.e., upward acceleration of the mass center) during running we developed a three-dimensional muscle-actuated simulation of the running gait cycle. The simulation is driven by 92 musculotendon actuators of the lower extremities and torso and includes the dynamics of arm motion. We analyzed the simulation to determine how each muscle contributed to the acceleration of the body mass center. During the early part of the stance phase, the quadriceps muscle group was the largest contributor to braking (i.e., backward acceleration of the mass center) and support. During the second half of the stance phase, the soleus and gastrocnemius muscles were the greatest contributors to propulsion and support. The arms did not contribute substantially to either propulsion or support, generating less than 1% of the peak mass center acceleration. However, the arms effectively counterbalanced the vertical angular momentum of the lower extremities. Our analysis reveals that the quadriceps and plantarflexors are the major contributors to acceleration of the body mass center during running. Copyright © 2010 Elsevier Ltd. All rights reserved.

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Flexing computational muscle: modeling and simulation of musculotendon dynamics.

Scott L. Delp, Brianna M. Millard, Ajay Seth … (2013)

Muscle-driven simulations of human and animal motion are widely used to complement physical experiments for studying movement dynamics. Musculotendon models are an essential component of muscle-driven simulations, yet neither the computational speed nor the biological accuracy of the simulated forces has been adequately evaluated. Here we compare the speed and accuracy of three musculotendon models: two with an elastic tendon (an equilibrium model and a damped equilibrium model) and one with a rigid tendon. Our simulation benchmarks demonstrate that the equilibrium and damped equilibrium models produce similar force profiles but have different computational speeds. At low activation, the damped equilibrium model is 29 times faster than the equilibrium model when using an explicit integrator and 3 times faster when using an implicit integrator; at high activation, the two models have similar simulation speeds. In the special case of simulating a muscle with a short tendon, the rigid-tendon model produces forces that match those generated by the elastic-tendon models, but simulates 2-54 times faster when an explicit integrator is used and 6-31 times faster when an implicit integrator is used. The equilibrium, damped equilibrium, and rigid-tendon models reproduce forces generated by maximally-activated biological muscle with mean absolute errors less than 8.9%, 8.9%, and 20.9% of the maximum isometric muscle force, respectively. When compared to forces generated by submaximally-activated biological muscle, the forces produced by the equilibrium, damped equilibrium, and rigid-tendon models have mean absolute errors less than 16.2%, 16.4%, and 18.5%, respectively. To encourage further development of musculotendon models, we provide implementations of each of these models in OpenSim version 3.1 and benchmark data online, enabling others to reproduce our results and test their models of musculotendon dynamics.

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Long term outcomes of inversion ankle injuries.

A Anandacoomarasamy, L Barnsley (2005)

Ankle sprains are common sporting injuries generally believed to be benign and self limiting. However, some studies report a significant proportion of patients with ankle sprains having persistent symptoms for months or even years. To determine the proportion of patients presenting to an Australian sports medicine clinic who had long term symptoms after a sports related inversion ankle sprain. Consecutive patients referred to the NSW Institute of Sports Medicine from August 1999 to August 2002 with inversion ankle sprain were included. Exclusion criteria were fracture, ankle surgery, or concurrent lower limb problems. A control group, matched for age and sex, was recruited from patients attending the clinic for upper limb injuries in the same time period. Current ankle symptoms, ankle related disability, and current health status were ascertained through a structured telephone interview. Nineteen patients and matched controls were recruited and interviewed. The mean age in the ankle group was 20 (range 13-28). Twelve patients (63%) were male. Average follow up was 29 months. Only five (26%) ankle injured patients had recovered fully, with no pain, swelling, giving way, or weakness at follow up. None of the control group reported these symptoms (p<0.0001). Assessments of quality of life using short form-36 questionnaires (SF36) revealed a difference in the general health subscale between the two groups, favouring the control arm (p<0.05). There were no significant differences in the other SF36 subscales between the two groups. Most patients who sustained an inversion ankle injury at sport and who were subsequently referred to a sports medicine clinic had persistent symptoms for at least two years after their injury. This reinforces the importance of prevention and early effective treatment.

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Author and article information

Contributors

Ajay Seth:

ORCID: http://orcid.org/0000-0003-4217-1580

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Jennifer L. Hicks: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Thomas K. Uchida: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Ayman Habib: Role: ConceptualizationRole: Data curationRole: InvestigationRole: SoftwareRole: Visualization

Christopher L. Dembia:

ORCID: http://orcid.org/0000-0002-7759-7146

Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing

James J. Dunne: Role: ResourcesRole: SoftwareRole: ValidationRole: Visualization

Carmichael F. Ong: Role: InvestigationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing

Matthew S. DeMers: Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: Visualization

Apoorva Rajagopal: Role: InvestigationRole: ValidationRole: VisualizationRole: Writing – review & editing

Matthew Millard: Role: Formal analysisRole: MethodologyRole: SoftwareRole: Validation

Samuel R. Hamner:

ORCID: http://orcid.org/0000-0002-0465-4285

Role: InvestigationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – review & editing

Edith M. Arnold: Role: InvestigationRole: Validation

Jennifer R. Yong: Role: InvestigationRole: Validation

Shrinidhi K. Lakshmikanth: Role: Software

Michael A. Sherman:

ORCID: http://orcid.org/0000-0001-5002-886X

Role: ConceptualizationRole: MethodologyRole: SoftwareRole: Validation

Joy P. Ku: Role: Data curationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: Writing – review & editing

Scott L. Delp: Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing

Dina Schneidman: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Comput Biol

Journal ID (iso-abbrev): PLoS Comput. Biol

Journal ID (publisher-id): plos

Journal ID (pmc): ploscomp

Title: PLoS Computational Biology

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1553-734X

ISSN (Electronic): 1553-7358

Publication date Collection: July 2018

Publication date (Electronic): 26 July 2018

Volume: 14

Issue: 7

Electronic Location Identifier: e1006223

Affiliations

[1 ] Department of Bioengineering, Stanford University, Stanford, California, United States of America

[2 ] Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America

[3 ] Department of Orthopaedic Surgery, Stanford University, Stanford, California, United States of America

Hebrew University of Jerusalem, ISRAEL

Author notes

The authors have declared that no competing interests exist.

* E-mail: aseth@ 123456stanford.edu

Author information

Ajay Seth http://orcid.org/0000-0003-4217-1580

Christopher L. Dembia http://orcid.org/0000-0002-7759-7146

Samuel R. Hamner http://orcid.org/0000-0002-0465-4285

Michael A. Sherman http://orcid.org/0000-0001-5002-886X

Article

Publisher ID: PCOMPBIOL-D-18-00020

DOI: 10.1371/journal.pcbi.1006223

PMC ID: 6061994

PubMed ID: 30048444

SO-VID: a5f80f62-e5f1-4766-bb1f-8b12c7c6c3fc

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 5 January 2018

Date accepted : 23 May 2018

Page count

Figures: 10, Tables: 0, Pages: 20