Abstract
The complexity quantification of human gait time series has received considerable interest for wearable healthcare. Symbolic entropy is one of the most prevalent algorithms used to measure the complexity of a time series, but it fails to account for the multiple time scales and multi-channel statistical dependence inherent in such time series. To overcome this problem, multivariate multiscale symbolic entropy is proposed in this paper to distinguish the complexity of human gait signals in health and disease. The embedding dimension, time delay and quantization levels are appropriately designed to construct similarity of signals for calculating complexity of human gait. The proposed method can accurately detect healthy and pathologic group from realistic multivariate human gait time series on multiple scales. It strongly supports wearable healthcare with simplicity, robustness, and fast computation.
Highlights
The human gait is a nonlinear dynamic behavior based on the feedback of space and time, mainly controlled by the nerve and locomotor systems
The multivariate multiscale symbolic entropy (MMSyEn) analysis is evaluated for multichannel stochastic data and real-world multivariate human stride interval recordings
The MMSyEn analysis is evaluated for multichannel stochastic dataon and real-world multivariate specifications:
Summary
The human gait is a nonlinear dynamic behavior based on the feedback of space and time, mainly controlled by the nerve and locomotor systems. Some algorithms assign a larger entropy for certain pathologic processes that are generally presumed to represent less complexity than for healthy dynamics [11] This is misleading, especially when the signal comes from more complex systems, with underlying significant correlation over multiple spatio-temporal scales. The wearable health monitoring approach prefers simple and fast computation capability and robustness in the presence of noise To meet these demands, a multivariate multiscale complexity measure method is proposed to robustly distinguish physiologic signals in health and disease with high computation efficiency. The MMSyEn calculation results of simulated stochastic data and experimental gait signals obtained under different disease states and walking conditions demonstrate the advantageous performance in the complexity quantification and characteristics extraction of real-world time series
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