Predictive simulations of neuromuscular coordination and joint-contact loading in human gait

Journal article


Lin, Yi-Chung, Walter, Jonathan P. and Pandy, Marcus G.. (2018). Predictive simulations of neuromuscular coordination and joint-contact loading in human gait. Annals of Biomedical Engineering. 46(8), pp. 1216-1227. https://doi.org/10.1007/s10439-018-2026-6
AuthorsLin, Yi-Chung, Walter, Jonathan P. and Pandy, Marcus G.
Abstract

We implemented direct collocation on a full-body neuromusculoskeletal model to calculate muscle forces, ground reaction forces and knee contact loading simultaneously for one cycle of human gait. A data-tracking collocation problem was solved for walking at the normal speed to establish the practicality of incorporating a 3D model of articular contact and a model of foot–ground interaction explicitly in a dynamic optimization simulation. The data-tracking solution then was used as an initial guess to solve predictive collocation problems, where novel patterns of movement were generated for walking at slow and fast speeds, independent of experimental data. The data-tracking solutions accurately reproduced joint motion, ground forces and knee contact loads measured for two total knee arthroplasty patients walking at their preferred speeds. RMS errors in joint kinematics were < 2.0° for rotations and < 0.3 cm for translations while errors in the model-computed ground-reaction and knee-contact forces were < 0.07 BW and < 0.4 BW, respectively. The predictive solutions were also consistent with joint kinematics, ground forces, knee contact loads and muscle activation patterns measured for slow and fast walking. The results demonstrate the feasibility of performing computationally-efficient, predictive, dynamic optimization simulations of movement using full-body, muscle-actuated models with realistic representations of joint function.

Keywordsmusculoskeletal model; dynamic optimization; collocation; knee contact model; foot–ground interaction
Year2018
JournalAnnals of Biomedical Engineering
Journal citation46 (8), pp. 1216-1227
PublisherSpringer
ISSN0090-6964
Digital Object Identifier (DOI)https://doi.org/10.1007/s10439-018-2026-6
Scopus EID2-s2.0-85045693494
Open accessPublished as green open access
Research or scholarlyResearch
Page range1216-1227
FunderAustralian Research Council (ARC)
Author's accepted manuscript
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All rights reserved
File Access Level
Open
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online18 Apr 2018
Publication process dates
Accepted11 Apr 2018
Deposited19 Aug 2022
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104366
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