Characterizing gait pattern dynamics during symmetric and asymmetric walking using autoregressive modeling.

ABSTRACT: Gait asymmetry is often observed in populations with varying degrees of neuromuscular control. While changes in vertical ground reaction force (vGRF) peak magnitude are associated with altered limb loading that can be observed during asymmetric gait, the challenge is identifying techniques with the sensitivity to detect these altered movement patterns. Autoregressive (AR) modeling has successfully delineated between healthy and pathological gait during running; but has been little explored in walking. Thus, AR modeling was implemented to assess differences in vGRF pattern dynamics during symmetric and asymmetric walking. We hypothesized that the AR model coefficients would better detect differences amongst the symmetric and asymmetric walking conditions than the vGRF peak magnitude mean. S

SUBMITTER: Mahzoun Alzakerin H 

PROVIDER: S-EPMC7714243 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature