Abstract:

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.

Abstract:

The chronological vacillations in the stride to stride interval provide a noninvasive method to assess the influence of malfunction of human gait and its alterations with disease and age. To extract information from the human stride interval, various complexity analysis techniques have been proposed. In the present study, the comparison of two recently developed complexity analysis methodologies: multiscale entropy (MSE) and symbolic entropy (SyEn) has been made. These techniques were applied to stride interval time series data of human gait walking at normal and metronomically paced stressed conditions. Wilcoxon-rank-sum test (Mann-Whitney-Wilcoxon (MWW) test) was used to find the significant difference between the groups. For each method of analysis, parameters were adjusted to optimize the separation of the groups. The symbolic entropy method provided maximum separation at wide range of threshold values and this measure was found to be more robust for analyzing the human gait data as compared to multiscale entropy in the presence of dynamical and observational noise. The results of this study can have implication modeling physiological control mechanism and for quantifying human gait dynamics in physiological and stressed conditions.