Predictive simulations of human walking produce realistic cost of transport at a range of speeds

Abstract (2017)

Authors

Carmichael F. Ong Stanford University
T. Geijtenbeek Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Jennifer L. Hicks Stanford University
Scott L. Delp Stanford University
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2017 Carmichael F. Ong, T. Geijtenbeek, Jennifer L. Hicks, Scott L. Delp
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

Abstract

Predictive simulations of human walking have great potential to expand our understanding of locomotion. For instance, they can isolate the effect of specific impairments on observed gait pathologies or aid in designing assistive devices by modeling human-device interactions. Introducing simulated impairments or adding augmentation devices to a model may change kinematics, including preferred walking speed. Experimental studies have characterized cost of transport over a wide range of walking speeds, and have shown that humans prefer walking at a speed that minimizes their cost of transport [1]. The purpose of this study was to use a predictive simulation framework to reproduce experimental energetic cost of transport. We trained a model to walk at speeds between 0.5 and 2.0 m/s and compared our simulated cost of transport to experimental data.