Three-dimensional data-tracking dynamic optimization simulations of human locomotion generated by direct collocation

Journal article


Lin, Yi-Chung and Pandy, Marcus. (2017). Three-dimensional data-tracking dynamic optimization simulations of human locomotion generated by direct collocation. Journal of Biomechanics. 59, pp. 1-8. https://doi.org/10.1016/j.jbiomech.2017.04.038
AuthorsLin, Yi-Chung and Pandy, Marcus
Abstract

The aim of this study was to perform full-body three-dimensional (3D) dynamic optimization simulations of human locomotion by driving a neuromusculoskeletal model toward in vivo measurements of body-segmental kinematics and ground reaction forces. Gait data were recorded from 5 healthy participants who walked at their preferred speeds and ran at 2 m/s. Participant-specific data-tracking dynamic optimization solutions were generated for one stride cycle using direct collocation in tandem with an OpenSim-MATLAB interface. The body was represented as a 12-segment, 21-degree-of-freedom skeleton actuated by 66 muscle-tendon units. Foot-ground interaction was simulated using six contact spheres under each foot. The dynamic optimization problem was to find the set of muscle excitations needed to reproduce 3D measurements of body-segmental motions and ground reaction forces while minimizing the time integral of muscle activations squared. Direct collocation took on average 2.7 ± 1.0 h and 2.2 ± 1.6 h of CPU time, respectively, to solve the optimization problems for walking and running. Model-computed kinematics and foot-ground forces were in good agreement with corresponding experimental data while the calculated muscle excitation patterns were consistent with measured EMG activity. The results demonstrate the feasibility of implementing direct collocation on a detailed neuromusculoskeletal model with foot-ground contact to accurately and efficiently generate 3D data-tracking dynamic optimization simulations of human locomotion. The proposed method offers a viable tool for creating feasible initial guesses needed to perform predictive simulations of movement using dynamic optimization theory. The source code for implementing the model and computational algorithm may be downloaded at http://simtk.org/home/datatracking.

Year2017
JournalJournal of Biomechanics
Journal citation59, pp. 1-8
PublisherElsevier Ltd
ISSN0021-9290
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jbiomech.2017.04.038
Scopus EID2-s2.0-85020103951
Research or scholarlyResearch
Page range1-8
FunderAustralian Research Council (ARC)
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online19 May 2017
Publication process dates
Accepted30 Apr 2017
Deposited18 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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