Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2023-04-01 DOI:10.1016/j.bbe.2023.04.002

MohammadJavad Shariatzadeh , Ehsan Hadizadeh Hafshejani , Cameron J.Mitchell , Mu Chiao , Dana Grecov

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Abstract

Muscle fatigue is defined as a reduction in the capability of muscle to exert force or power. Although surface electromyography (sEMG) signals during exercise have been used to assess muscle fatigue, analyzing the sEMG signal during dynamic contractions is difficult because of the many signal distorting factors such as electrode movements, and variations in muscle tissue conductivity. Besides the non-deterministic and non-stationary nature of sEMG in dynamic contractions, no fatigue indicator is available to predict the ability of a muscle to apply force based on the sEMG signal properties.

In this study, we designed and manufactured a novel wearable sensor system with both sEMG electrodes and motion tracking sensors to monitor the dynamic muscle movements of human subjects. We detected the state of muscle fatigue using a new wavelet analysis method to predict the maximum isometric force the subject can apply during dynamic contraction.

Our method of signal processing consists of four main steps. 1- Segmenting sEMG signals using motion tracking signals. 2- Determine the most suitable mother wavelet for discrete wavelet transformation (DWT) based on cross-correlation between wavelets and signals. 3- Deoinsing the sEMG using the DWT method. 4- Calculation of normalized energy in different decomposition levels to predict maximal voluntary isometric contraction force as an indicator of muscle fatigue.

The monitoring system was tested on healthy adults doing biceps curl exercises, and the results of the wavelet decomposition method were compared to well-known muscle fatigue indices in the literature.

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利用表面肌电信号的小波分析预测动态收缩过程中的肌肉疲劳

肌肉疲劳被定义为肌肉施加力量或动力的能力下降。虽然运动过程中的表面肌电图(sEMG)信号已被用于评估肌肉疲劳，但由于电极运动和肌肉组织电导率变化等许多信号扭曲因素，分析动态收缩过程中的表面肌电图信号是困难的。除了动态收缩时表面肌电信号的不确定性和非平稳性外，没有疲劳指标可以根据表面肌电信号的特性来预测肌肉施加力的能力。在这项研究中，我们设计并制造了一种新型的可穿戴传感器系统，该系统具有肌电信号电极和运动跟踪传感器，用于监测人类受试者的动态肌肉运动。我们使用一种新的小波分析方法来检测肌肉疲劳状态，以预测受试者在动态收缩时可以施加的最大等长力。我们的信号处理方法包括四个主要步骤。1-使用运动跟踪信号分割肌电信号。2-根据小波和信号之间的相互关系，确定离散小波变换(DWT)最合适的母小波。3-使用DWT方法去除表面肌电信号。4-计算不同分解水平的归一化能量，以预测最大自主等长收缩力，作为肌肉疲劳的指标。对健康成人进行肱二头肌弯曲运动的监测系统进行了测试，并将小波分解方法的结果与文献中已知的肌肉疲劳指标进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biocybernetics and Biomedical Engineering ENGINEERING, BIOMEDICAL-

CiteScore

16.50

自引率

6.20%

发文量

审稿时长

38 days

期刊介绍： Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.