Characterizing and modeling the joint-level variability in human walking. Issue 14 (3rd October 2016)

• Record Type: Journal Article
• Authors: Martin, Anne E.; Villarreal, Dario J.; Gregg, Robert D.
• Is Part Of: Journal of biomechanics. Volume 49:Issue 14(2016)
• Page Start: 3298
• Page End: 3305
• Publication Date: 2016-10-03
• Subjects: Variability -- Human gait -- Modeling; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2016.08.015
• Languages: English
• ISSNs: 0021-9290

Abstract

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.