Symbolic Time Series Analysis of Temporal Gait Dynamics

Signals obtained from biological systems exhibit pronounced complexity. The patterns of change contain valuable information about the dynamics of underlying control mechanism of the complex biological systems. Human gait is a complex process with multiple inputs and numerous outputs. Various complexity analysis tools have been proposed to extract information from human gait time series. In this study, we used recently developed threshold based symbolic entropy to compare the spontaneous output of the human locomotors system during constrained and metronomically paced walking protocols. For that purpose, stride interval time series of healthy subjects who walked for 1 h at normal, slow and fast rates under different conditions was transformed into symbol sequences. Normalized corrected Shannon entropy (NCSE) was computed from the symbol sequences of the stride interval time series. The findings indicated that the unprompted output of human locomotors system is more complex during unconstrained normal walking as compared with slow, fast or metronomically paced walking.