Complexity of human walking: the attractor complexity index is sensitive to gait synchronization with visual and auditory cues

Abstract

Background. During steady walking, gait parameters fluctuate from one stride to another with complex fractal patterns and long-range statistical persistence. When a metronome is used to pace the gait (sensorimotor synchronization), long-range persistence is replaced by stochastic oscillations (anti-persistence). Fractal patterns present in gait fluctuations are most often analyzed using detrended fluctuation analysis (DFA). This method requires the use of a discrete times series, such as intervals between consecutive heel strikes, as an input. Recently, a new nonlinear method, the attractor complexity index (ACI), has been shown to respond to complexity changes like DFA. But in contrast to DFA, ACI can be applied to continuous signals, such as body accelerations. The aim of this study was to further compare DFA and ACI in a treadmill experiment that induced complexity changes through sensorimotor synchronization. Methods. Thirty-six healthy adults walked 30 minutes on an instrumented treadmill under three conditions: no cueing, auditory cueing (metronome walking), and visual cueing (stepping stones). The center-of-pressure trajectory was discretized into time series of gait parameters, after which a complexity index (scaling exponent alpha) was computed via DFA. Continuous pressure position signals were used to compute the ACI. Correlations between ACI and DFA were then analyzed. The predictive ability of DFA and ACI to differentiate between cueing and no-cueing conditions was assessed using regularized logistic regressions and areas under the receiver operating characteristic curves (AUROC). Results. DFA and ACI were both significantly different among the cueing conditions. DFA and ACI were correlated (Pearson’s r = 0.78). Logistic regressions showed that DFA and ACI could differentiate between cueing/no cueing conditions with a high degree of confidence (AUROC = 1.0 and 0.96, respectively). Conclusion. Both DFA and ACI responded similarly to changes in cueing conditions and had comparable predictive power. This support the assumption that ACI could be used instead of DFA to assess the long-range complexity of continuous gait signals.

Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory

“…The data used in the present study were collected by the author in a previous study aimed at analyzing the influence of synchronization to external sensory cues on gait variability [24,25]. Thirty-six healthy adults participated in the study:…”

Evaluating dynamic similarity of fixed, self-selected and anatomically scaled speeds in non-linear analysis of gait during treadmill running

“…Like the Froude number, the Strouhal number is a dimensionless number, in this case representing the frequency of the gait pattern (Alexander, 1989). However, previous studies including the Strouhal number have focused on linear measures (Delattre et al, 2009;Villeger et al, 2015), and research suggests that fixing stride frequency affects non-linear measures (Terrier and Deriaz, 2013;Terrier, 2019), so we excluded it from this analysis to avoid introducing a confounding variable.…”