电子测量与仪器学报

姚贺龙,吕东澔,张勇,张鹏,曹震.基于傅里叶分解方法的肌肉疲劳状态分类研究[J].电子测量与仪器学报,2023,37(6):48-58

基于傅里叶分解方法的肌肉疲劳状态分类研究

Study of muscle fatigue state classification based on Fourier decomposition method

DOI：

英文关键词:muscle fatigue surface electromyography signal Fourier decomposition method machine learning support vector machine

基金项目:内蒙古自治区自然科学基金(2019BS06004)、国家自然科学基金(62263026)项目资助

作者	单位
姚贺龙	1.内蒙古科技大学信息工程学院
吕东澔	1.内蒙古科技大学信息工程学院
张勇	1.内蒙古科技大学信息工程学院
张鹏	1.内蒙古科技大学信息工程学院
曹震	1.内蒙古科技大学信息工程学院

Author	Institution
Yao Helong	1.School of Information Engineering, Inner Mongolia University of Science & Technology
Lyu Donghao	1.School of Information Engineering, Inner Mongolia University of Science & Technology
Zhang Yong	1.School of Information Engineering, Inner Mongolia University of Science & Technology
Zhang Peng	1.School of Information Engineering, Inner Mongolia University of Science & Technology
Cao Zhen	1.School of Information Engineering, Inner Mongolia University of Science & Technology

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中文摘要:

由于表面肌电(sEMG)信号具有非线性和非平稳性,导致传统的肌肉疲劳分类方法存在局限性,基于此提出一种基于傅里叶分解方法(FDM)和机器学习相结合的肌肉疲劳分类方法。使用 FDM 将 sEMG 信号分解为一系列傅里叶固有频带函数 (FIBF),确定最优分解水平,利用 FDM 提取各 FIBF 分量总功率占 sEMG 信号总功率的比例(FTPR)作为分类特征,对比各机器学习分类算法的有效性和数据长度对分类准确率的影响。研究表明基于 FDM 的特征提取方法能够有效的识别肌肉疲劳状态, 在数据长度为 3 000 且 FDM 的 10 层分解水平下,使用支持向量机分类器,得到了 98. 17%的平均分类准确率。对每个 FIBF 分量单独进行分析,发现在第 5 个 FIBF 分量下的 FTPR 有最好的类可分性,肌肉疲劳时第 1 ~ 2 分量的 FTPR 会变大,第 4 ~ 10 分量的 FTPR 会变小,即当肌肉疲劳时 sEMG 信号 0~ 117 Hz 区间的频率幅度会增加,175. 5 ~ 585 Hz 区间的频率幅度会下降。通过对比不同特征提取方法的肌肉疲劳分类效果,实验结果表明 FDM 和 FTPR 特征能够显著提高分类准确率。因此,所提方法可用于肌肉疲劳状态识别。

英文摘要:

Due to the nonlinearity and non-smoothness of surface electromyography ( sEMG) signals, which leads to the limitations of traditional muscle fatigue classification methods, a muscle fatigue classification method based on the combination of Fourier decomposition method (FDM) and machine learning is proposed based on this. The FDM is used to decompose the sEMG signal into a series of Fourier intrinsic band functions (FIBF), determine the optimal decomposition level, extract the ratio of the total power of each FIBF component to the total power of the sEMG signal (FTPR) as classification features using the FDM, and compare the effectiveness of each machine learning classification algorithm and the effect of data length on the classification accuracy. It was shown that the FDMbased feature extraction method can effectively identify muscle fatigue states, and an average classification accuracy of 98. 17% was obtained using a support vector machine classifier with a data length of 3 000 and a 10-level decomposition level of FDM. Each FIBF component was analyzed individually, and it was found that the FTPR under the 5th FIBF component had the best class separability, and the FTPR of the 1st to 2nd components would become larger when muscle fatigue was present, and the FTPR of the 4th to 10th components would become smaller, i. e. , the frequency amplitude of the sEMG signal in the 0 ~ 117 Hz interval would increase when muscle fatigue was present, and the frequency amplitude in the 175. 5 ~ 585 Hz interval would decreases. By comparing the muscle fatigue classification effects of different feature extraction methods, the experimental results show that the FDM and FTPR features can significantly improve the classification accuracy. Therefore, the proposed method can be used for muscle fatigue state recognition.

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