基于自回归模型表面肌电信号检测肌肉疲劳研究

针对表面肌电信号的非平稳特性,采用自回归模型对表面肌电信号进行分析,对短时间内的表面肌电信号肌肉疲劳迅速做出判定。应用非平稳时间序列的时变系统建模方法对10例受试者疲劳前、疲劳后表面肌电信号进行特征提取。建立时变参数自回归模型,通过引入Legendre基函数将线性非平稳过程参数辨识转化为线性时不变系统参数辨识,结合相关指数可以获得时变系统参数估计的最优Legendre基函数维数,进而可以获得最佳模型拟合效果,并采用最小二乘法解出时不变参数。用疲劳前、后的自回归模型的第一个时变参数(ARC1)的变化率作为检测肌肉疲劳敏感性指标,并采用双尾t检验,分别与平均功率频率(MPF)和中值频率(MF)的变化率进行统计学对比分析。结果表明, ARC1、MPF和MF疲劳前后的变化率分别为34.33%±2.41%、25.68%±2.03%、22.80%±2.19%,且ACR1的变化率分别显著高于MPF和MF(P<0.05).所提出的方法通过表面肌电信号对肌肉疲劳检测时,具有时间短和敏感性高等优点,可用于在线实时分析肌肉疲劳程度,为肢肌肉劳损的评估、康复治疗及人体工效学的研究提供一个潜在的分析工具。

Detection of Muscle Fatigue Based on sEMG Signal with AR Model

According to the non-stationary characteristics of the surface electromyography signal, the autoregressive model was employed to analyze the surface electromyography signal. This method could rapidly estimate muscle fatigue by analyzing short surface electromyography signal. Surface electromyography signals from 10 subjects with pre-fatigue and post-fatigue were collected and analyzed using an autoregressive model. The autoregressive model parameters was identified by the Legendre?basis function expansion method, transforming the linear non-stationary problem into the linear time-invariant one. The autoregressive model parameters was solved in least square method. Changing rates (in percentage) of pre-fatigue and post-fatigue for the first parameter of the autoregressive model (ACR1), mean power frequency (MPF), median frequency (MF) were calculated and compared using two-tailedsamples t-test. The results showed that the changing rates of ACR1, MPF and MF were 34.33%±2.41%、25.68%±2.03% and 22.80%±2.19%, respectively. And the changing rate for ACR1 was significantly higher than that for both MPF and MF (P<0.05). ACR1 could not only realize the rapid assessment of muscle fatigue on short surface electromyography signal, but also has higher sensitivity than MPF and MF, providing a promising assessment method in the field of the upper muscular strain and the rehabilitation.