Detection of Muscle Fatigue Based on sEMG Signal with AR Model
Yang Zheng, Wang Liling*, Ma Dong
Key Laboratory of Digital Medical Engineering of Hebei Province,College of Electronic and Information Engineering, Hebei University,Baoding 071002,Hebei,China
Abstract：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.
杨铮, 王立玲, 马东. 基于自回归模型表面肌电信号检测肌肉疲劳研究[J]. 中国生物医学工程学报, 2018, 37(6): 673-679.
Yang Zheng, Wang Liling, Ma Dong. Detection of Muscle Fatigue Based on sEMG Signal with AR Model. Chinese Journal of Biomedical Engineering, 2018, 37(6): 673-679.
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