Biomechanical gait simulations with military body borne loads: An exploration of predicted gait kinematics, ground reaction forces & estimated metabolic cost of transport

Master thesis (2024)

Authors

T.A. van Kuijk Mechanical Engineering

Contributors

A. Seth Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (mentor)
J. Harlaar Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (graduation committee member)
M. Wesseling Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (graduation committee member)
B.J.C. Bastiaansen TNO (graduation committee member)
Faculty
Mechanical Engineering
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Published Date

21-08-2024

Language

English

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Faculty

Mechanical Engineering

Abstract

The ability to simulate the impact on performance of military body-borne loads enables effective analysis and optimisation of load and equipment configurations for military personnel performance. Additionally, these simulations can reduce research & development costs of new equipment by analysing its impact in an early stage of development. While research has been done into the effects of body borne loads on kinematics, ground reaction forces (GRF) and metabolic cost of transport, the accuracy and reliability of simulations are not clear yet.
This study set out to predict loaded gait kinematics and GRFs and estimate metabolic cost of transport for gait at 1.5 m/s, carrying different types of military relevant body-borne loads, to evaluate the applied methods for implementation in a future load configuration optimisation tool.
The kinematic/GRF prediction was performed by forward dynamics simulations in SCONE/hyfydy, using a planar musculoskeletal model and a 2 gait-state controller, simulating 15 solutions for each load condition. While only 60% of experimentally measured differences between load conditions were correctly predicted, the expected differences from literature were all correctly predicted. It was assessed that improving the number of gait states and the number of optimisations per load condition is expected to improve these results.
The metabolic cost of transport (mCoT) estimation was performed by the Computed Muscle Control algorithm of OpenSim, using experimental kinematics and ground reaction forces. However, small compounding errors in experimental data and data processing prevented accurate mCoT estimations. Although the applied kinematic/GRF and mCoT simulation methods could not be validated yet, based on these results, the study as a whole does show promise for the continued development of these models and their future implementation for loaded gait performance optimisation.

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