A method to concatenate multiple short time series for evaluating dynamic behaviour during walking.

ABSTRACT: Gait variability is a sensitive metric for assessing functional deficits in individuals with mobility impairments. To correctly represent the temporal evolution of gait kinematics, nonlinear measures require extended and uninterrupted time series. In this study, we present and validate a novel algorithm for concatenating multiple time-series in order to allow the nonlinear analysis of gait data from standard and unrestricted overground walking protocols. The full-body gait patterns of twenty healthy subjects were captured during five walking trials (at least 5 minutes) on a treadmill under different weight perturbation conditions. The collected time series were cut into multiple shorter time series of varying lengths and subsequently concatenated using a novel algorithm that identifies sim

SUBMITTER: Orter S 

PROVIDER: S-EPMC6588245 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature