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Predictive modelling of human walking over a complete gait cycle

Ren, L, Jones, RK and Howard, D 2007, 'Predictive modelling of human walking over a complete gait cycle' , Journal of Biomechanics, 40 (7) , pp. 1567-1574.

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Abstract

An inverse dynamics multi-segment model of the body was combined with optimisation techniques to simulate normal walking in the sagittal plane on level ground. Walking is formulated as an optimal motor task subject to multiple constraints with minimisation of mechanical energy expenditure over a complete gait cycle being the performance criterion. All segmental motions and ground reactions were predicted from only three simple gait descriptors (inputs): walking velocity, cycle period and double stance duration. Quantitative comparisons of the model predictions with gait measurements show that the model reproduced the significant characteristics of normal gait in the sagittal plane. The simulation results suggest that minimising energy expenditure is a primary control objective in normal walking. However, there is also some evidence for the existence of multiple concurrent performance objectives. Keywords: Gait prediction; Inverse dynamics; Optimisation; Optimal motor task

Item Type: Article
Uncontrolled Keywords: Gait prediction, inverse dynamics, optimisation, optimal motor task
Themes: Subjects / Themes > R Medicine > R Medicine (General)
Health and Wellbeing
Schools: Colleges and Schools > College of Health & Social Care
Colleges and Schools > College of Science & Technology
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > CASE Control & Systems Engineering Research Centre
Colleges and Schools > College of Health & Social Care > School of Health Sciences > Centre for Health Sciences Research
Colleges and Schools > College of Science & Technology > School of Environment and Life Sciences
Colleges and Schools > College of Health & Social Care > School of Health Sciences
Journal or Publication Title: Journal of Biomechanics
Publisher: Elsevier
Refereed: Yes
ISSN: 00219290
Depositing User: H Kenna
Date Deposited: 09 Aug 2007 12:08
Last Modified: 20 Aug 2013 15:46
URI: http://usir.salford.ac.uk/id/eprint/210

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