Analysis of Lower Limb Muscle Fatigue Based on Surface Electromyographic Signal

Abstract

Surface electromyographic (sEMG) signal contains abundant information such as joint torque and joint motion, which is widely used in human-computer interactive intelligent rehabilitation equipment. In this work, the ankle torque of lower limb is taken as the research object, and the feature parameters of sEMG which represent the fatigue state are analysed. Advance prediction of fatigue features for specific time periods was performed using a normalized minimum average square (NLMS) filter. While the modified cerebellar model neural network (WFCMNN) is used to classify fatigue, which can be divided into three states, namely no fatigue, transition to fatigue, and fatigue. The results show that the accuracy of classification is 96.429%, which is better than other advanced models. At the same time, sEMG signal is used to predict fatigue in advance, which can solve the problem of differences between different individuals. Such strategy is helpful for doctors and physiotherapists to carry out rehabilitation treatment for patients, as a pre judgment and diagnosis index.