Sample Entropy as a tool for quantifying human gait complexity: the effect of age and walking velocity

Master thesis (2021)

Authors

J.P. Vermeulen Mechanical Engineering

Contributors

A.C. Schouten Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (mentor)
W. Mugge Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (mentor)
J.H. de Groot Biomechanical Engineering (graduation committee member)
M. Stijntjes Support Biomechanical Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2021 Jeroen Vermeulen
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Published Date

29-01-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

Purpose – The entropy algorithm is a recently developed statistic for quantifying the complexity of time series data. To date, research of biomechanics and motor control discussed whether entropy algorithms could be used as a convenient tool to identify healthy gait function, to evaluate outcomes of physical therapies and to monitor the progression of disease. Here, we show that Sample Entropy (SaEn) is a sensitive measure for exposing complexity changes in human gait function.

Methods – We analyzed signal complexity changes in electromyography (EMG), ground reaction force (GRF) and joint angle (GA) time series data of asymmetrical step tasks. We used the coarse-grained time series method and the SaEn algorithm, to determine the temporal resolution that contained most complex structures per datatype. Subsequently, we analyzed complexity changes with age and with walking velocity in the selected resolution. We analyzed complexity changes with age, since healthy gait function is known to deteriorate with age. In turn, we analyzed complexity changes with walking velocity, since walking velocity is known to alter gait function. Eighteen young (mean age 23.27 +/- 1.79 years) and nineteen old (mean age 66.37 +/- 5.26 years) subjects were analyzed for an equal number of strides, described by an equal number of samples, to account for the SaEn dataset length bias.

Results – Age increased entropy in EMG signals. Consecutively, age decreased GRF entropy in the medial-lateral (ML) component for short steps and increased entropy for long steps. Lastly, age decreased entropy in GA signals. Furthermore, walking velocity decreased entropy in EMG signals. Consecutively, walking velocity increased GRF entropy in anterior-posterior (AP) and vertical (VE) components and decreased entropy in the medial-lateral (ML) component. Lastly, walking velocity increased entropy in GA signals.

Conclusions – We portrayed that EMG, GRF and GA signals of human gait altered in entropy with walking velocity and with age. Therefore, our results demonstrate the feasibility of SaEn to quantify changes in healthy gait function. Additional research should confirm possible future clinical applications for entropy algorithms.