Human Gait Analysis

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. 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. The results was compared with the Multiscale Entropy (MSE) Analysis proposed and we concluded that the Symbolic Analysis is more robust than multiscale entropy method as well as our method is also useful for smaller time series whereas MSE is not suitable for shorter time series.