Citation

Martin AE, Villarreal DJ, Gregg RD. J. Biomech. 2016; 49(14): 3298-3305.

Affiliation

Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA; Department of Mechanical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA. Electronic address: rgregg@utdallas.edu.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.jbiomech.2016.08.015

PMID

27594679

Abstract

Although human gait is often assumed to be periodic, significant variability exists. This variability appears to provide different information than the underlying periodic signal, particularly about fall risk. Most studies on variability have either used step-to-step metrics such as stride duration or point-wise standard deviations, neither of which explicitly capture the joint-level variability as a function of time. This work demonstrates that a second-order Fourier series for stance joints and a first-order Fourier series for swing joints can accurately capture the variability in joint angles as a function of time on a per-step basis for overground walking at the self-selected speed. It further demonstrates that a total of seven normal distributions, four linear relationships, and twelve continuity constraints can be used to describe how the Fourier series vary between steps. The ability of the proposed method to create curves that match human joint-level variability was evaluated both qualitatively and quantitatively using randomly generated curves.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Language: en