sEMG-Based Dynamic Muscle Fatigue Classification Using SVM With Improved Whale Optimization Algorithm

During robot-assisted rehabilitation, failure to detect muscle fatigue in time may cause severe damage to human muscles. Surface electromyography (sEMG) signals are widely used in muscle fatigue analysis, but the dynamic fatigue classification is rarely reported and the accuracy is not satisfactory. In this article, an accurate classification model incorporating support vector machine (SVM) is established to accommodate the muscle fatigue prediction in dynamic conditions by proposing an improved whale optimization algorithm (WOA). Multidomain sEMG features are extracted and then fused to effectively classify the muscle fatigue statuses. WOA’s global optimization capability is able to find out the optimal parameters for SVM, but it will be greatly affected by the initial population. The differential evolution (DE) algorithm is adopted here to generate a more appropriate initial population. Experiments were carried out to distinguish the normal and fatigue status by using sEMG signals only. Results demonstrate the effectiveness and feasibility of the proposed method in dynamic muscle fatigue prediction with an average accuracy of 85.50% in ankle dorsiflexion (DF) and 84.75% in ankle plantarflexion (PF).