A nonlinear dynamical model of human gait

TL;DR

This paper introduces a nonlinear stochastic model of human gait control that captures multifractal fluctuations and the effects of external pacing, providing insights into neural control mechanisms during walking.

Contribution

It presents a novel nonlinear dynamical model that describes human gait across different regimes, including metronomic pacing, integrating neural network control and stochastic fluctuations.

Findings

Normal gait exhibits multifractal fluctuations.

Gait regularity changes with pace alterations.

Metronomic pacing disrupts long-range correlations.

Abstract

We present a nonlinear stochastic model of the human gait control system in a variety of gait regimes. The stride interval time series in normal human gait is characterized by slightly multifractal fluctuations. The fractal nature of the fluctuations become more pronounced under both an increase and decrease in the average gait. Moreover, the long-range memory in these fluctuations is lost when the gait is keyed on a metronome. The human locomotion is controlled by a network of neurons capable of producing a correlated syncopated output. The central nervous system is coupled to the motocontrol system, and together they control the locomotion of the gait cycle itself. The metronomic gait is simulated by a forced nonlinear oscillator with a periodic external force associated with the conscious act of walking in a particular way.