Muscle Fatigue Classification and The Effect of Electrical Stimulation on Muscle Fatigue Recovery

Abstract

Abstract The purpose of this work is to assess and grade muscle fatigue, and then reveal the different recovery effects of the three methods of natural recovery, electrical stimulation of acupoints, and electrical stimulation of non-acupoints based on the classification and changes in characteristics. Muscle fatigue mainly refers to the weakness of muscles and the weakening of working ability after long-term or high-intensity use. It is a problem that people often encounter in daily life. In this study, 20 subjects participated in the study. By collecting surface EMG signals, and then extracting time-domain features and frequency-domain features, three algorithms of KNN, LR, and SVM were used to classify muscle fatigue into three categories: good, transitional fatigue, fatigue. The average accuracy of KNN algorithm reached 83.2%, the average accuracy of LR reached 84.7%, and the average accuracy of SVM reached 88.6%. Afterwards, the SVM classifier was used to evaluate the subsequent three recovery effects. The experiment found that electrical stimulation of acupuncture points has a better recovery effect than natural recovery and electrical stimulation of non-acupuncture points, and has a more active recovery promotion effect.