TY - JOUR
T1 - Characterizing and modeling the joint-level variability in human walking
AU - Martin, Anne E.
AU - Villarreal, Dario J.
AU - Gregg, Robert D.
N1 - Funding Information:
This work was supported by the National Institute of Child Health & Human Development of the NIH under Award Number DP2HD080349 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. R.D. Gregg holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.
Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2016/10/3
Y1 - 2016/10/3
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jbiomech.2016.08.015
DO - 10.1016/j.jbiomech.2016.08.015
M3 - Article
C2 - 27594679
AN - SCOPUS:84992520788
SN - 0021-9290
VL - 49
SP - 3298
EP - 3305
JO - Journal of Biomechanics
JF - Journal of Biomechanics
IS - 14
ER -