Physical actions classification of surface EMG signals using VMD

The musculoskeletal disorder of a patient can be analyzed by using surface electromyogram (sEMG) signals. Its diagnosis is possible by classification of physical actions are bowing, clapping, handshaking, hugging, jumping, running, standing, seating, walking, and waving of surface-EMG signals. In this paper, an efficient method based on variational mode decomposition (VMD) is proposed for identification of physical activities of sEMG signals. VMD is an adaptive and non recursive signal decomposition method which decomposes sEMG signals into several modes. These modes are used for extraction of statistical features like coefficient of variation, entropy, mean, negentropy, standard deviation, and zero crossing rate. Extracted features are fed into the multiclass least squares support vector machine (MC-LS-SVM) classifier with radial basis function (RBF) in order to classify normal physical actions of surface-EMG signals. The performance of obtained results shows that the method used provides a better classification accuracy of 98.17% for physical actions of surface-EMG signals as compared to existing methods.