Characterizing and modeling the joint-level variability in human walking.

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 twelv

SUBMITTER: Martin AE 

PROVIDER: S-EPMC5180457 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature