Abstract

Info

Title:: Detection of muscle fatigue based on surface electromyography signals segmented by subband spectral entropy algorithm

Author(s):: BAO Qiyang; WANG Junxia; YUE Xiaoli; No. 940 Hospital of the Joint Logistics Support Force of PLA, Lanzhou 730050, China

Keywords:: Keywords: subband spectral entropy surface electromyography signal muscle fatigue double-threshold contraction region mean power frequency sensitivity to variability ratio

Abstract:: Abstract: Objective In view of the poor sensitivity of traditional methods for muscle fatigue detection, a new method is proposed to segment surface electromyography (sEMG) signals according to muscle contraction regions. Considering that the power and shape of muscle will affect the representation of the mean power frequency of the characteristic parameters when the muscle is in dynamic contraction, the number of muscle contraction regions instead of time is taken as the unit in sEMG signal segmentation. Methods The combining single-parameter and double-threshold is used to determine the starting and ending points of muscle contraction regions. The sensitivity of muscle fatigue is characterized by the sensitivity to variability ratio of the mean power frequency of the characteristic parameters of the dynamic contraction regions of muscle. The brand of sEMG acquisition equipment is NORAXON, and the model is MR3.6. MATLAB is used for programming. Results and Conclusion The simulation results show that compared with traditional fixed-length segmentation, the proposed method has a higher sensitivity and a better characterization in muscle fatigue detection.