基于表面肌电信号的肌肉疲劳状态分类系统

Muscle fatigue state classification system based on surface electromyography signal

为了实现肌肉疲劳状态的准确检测分类,提出一个完整的基于人体表面肌电(sEMG)信号的肌肉疲劳分类与检测系统。首先,通过AgCl表面贴片电极和高精度模拟前端ADS1299采集人体sEMG信号,进行小波消噪等预处理之后,提取可反映人体肌肉疲劳状态的sEMG信号时域和频域特征。然后,在常用特征如积分肌电图(IEMG)、均方根(RMS)、中值频率(MF)以及平均功率频率(MPF)基础上,为更加精细地刻画人体肌肉疲劳状态,引入sEMG信号的频域特征带谱熵(BSE);为弥补傅里叶变换分析非平稳信号的不足,引入sEMG信号时频特征——基于经验模态分解-希尔伯特变换(EEMD-HT)的平均瞬时频率。最后,为提高肌肉非疲劳和疲劳状态分类的准确度,利用含突变的粒子群优化算法优化支持向量机(PSO-SVM)并对sEMG进行分类,实现人体肌肉疲劳状态检测。征集15名健康男青年进行sEMG信号采集实验,建立sEMG信号库,提取特征进行分类实验。实验结果表明,所提的系统能够进行高精度sEMG信号采集和肌肉疲劳程度的高准确度分类,分类准确率大于90%。

In order to realize the accurate detection and classification of muscle fatigue states, a new complete muscle fatigue detection and classification system based on human surface ElectroMyoGraphy（sEMG） signals was proposed. Firstly,human sEMG signals were collected through AgCl surface patch electrode and high-precision analog front-end device ADS1299. The time-domain and frequency-domain features of sEMG signals reflecting human muscle fatigue states were extracted after the denoising preprocessing using wavelet transformation. Then, on the basis of the common features such as Integrated ElectroMyoGraphy（IEMG）, Root Mean Square（RMS）, Median Frequency（MF）, Mean Power Frequency（MPF）, in order to depict the fatigue states of human muscle more finely, the Band Spectral Entropy（BSE） of frequency domain features of sEMG signals were introduced. In order to compensate the weakness of Fourier transform in dealing with non-stationary signals, the time-frequency feature of the sEMG signals, named mean instantaneous frequency based on Ensemble Empirical Mode Decomposition-Hilbert transform（EEMD-HT）, was introduced. Finally, in order to improve the classification accuracy of muscle non-fatigue and fatigue states, the Support Vector Machine optimized by Particle Swarm Optimization algorithm（PSO-SVM） with mutation was used for the classification of sEMG signals to realize the detection of human muscle fatigue states. Fifteen healthy young men were recruited to carry out sEMG signal acquisition experiments, and a sEMG signal database was established to extract features for classification. The experimental results show that, the proposed system can realize high-accuracy sEMG signal acquisition and high-accuracy classification of muscle fatigue states, and its accuracy rate of classification is above 90%.