Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms
Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms
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DOI：
10.1016/j.cmpb.2017.10.024
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发表时间：
2018-02-01
影响因子：
6.1
通讯作者：
Ramakrishnan, S.
中科院分区：
文献类型：
--
作者：
Karthick, P. A.;Ghosh, Diptasree Maitra;Ramakrishnan, S.
背景和目的：基于表面肌电图（sEMG）的肌肉疲劳研究由于其无创性而在运动科学和职业/康复研究中被广泛首选。然而，这些信号是复杂的，多分量的和高度非平稳的大受试者间的变化，特别是在动态收缩。因此，基于时频的机器学习方法可以改善这些signal.Methods自动化系统的设计：在这项工作中，分析的基础上的高分辨率时频方法，即Stockwell变换（S-变换），B-分布（BD）和扩展的修改B-分布（EMBD），提出了区分动态肌肉非疲劳和疲劳状态。对52名健康志愿者肱二头肌表面肌电信号的非疲劳段和疲劳段进行了预处理，并分别进行了S变换、BD和EMBD。每种方法提取12个特征，并使用遗传算法（GA）和二进制粒子群优化（BPSO）选择突出的特征。分别采用朴素贝叶斯、多项式和径向基核支持向量机、随机森林和旋转森林5种机器学习算法对sEMG信号进行分类。在肌肉疲劳和非疲劳状态下，大多数特征表现出统计学上的显著差异。对于EMBD和BD-TFD，GA和BPSO分别减少了最大特征数（66%）。EMBD-多项式核基于SVM的组合被认为是最准确的（91%的准确率）在分类的条件下使用GA选择的功能。结论：所提出的方法被认为是能够处理的非平稳和多组分变化的sEMG信号记录在动态疲劳收缩。特别地，基于支持向量机的EMBD-多项式核的组合可以用于检测动态肌肉疲劳状态。(C)2017爱思唯尔B. V.保留所有权利。
Background and objective: Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals.Methods: In this work, the analysis based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distribution (BD) and extended modified B-distribution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the biceps brachii of 52 healthy volunteers are preprocessed and subjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naive Bayes, support vector machine (SVM) of polynomial and radial basis kernel, random forest and rotation forests are used for the classification.Results: The results show that all the proposed time-frequency distributions (TFDs) are able to show the nonstationary variations of sEMG signals. Most of the features exhibit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum number of features (66%) is reduced by GA and BPSO for EMBD and BD-TFD respectively. The combination of EMBD-polynomial kernel based SVM is found to be most accurate (91% accuracy) in classifying the conditions with the features selected using GA.Conclusions: The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the combination of EMBD-polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions. (C) 2017 Elsevier B.V. All rights reserved.