An intelligent method for classification of normal and aggressive actions from electromyography signals

In this paper an intelligent method called adaptive neuro-fuzzy inference system (ANFIS) is proposed for discriminating normal actions from aggressive actions using the features extracted from electromyography (EMG) signals. Classification of normal and aggressive actions are essential for diseases and prosthetic arm controls. But accurate classification of physical actions are sometimes not possible using raw EMG signals. To enhance the classification accuracy feature extraction is an essential criterion. Hence in this work wavelet analysis is used for feature extraction from EMG signals to provide a suitable pattern to the ANFIS based classifier. The EMG signals are decomposed using DB-4 wavelet up to level 5 and approximate coefficients are extracted. Approximate coefficients from the signals are taken as input to the ANFIS module to classify the physical actions. The proposed method is validated using various test cases and it is observed that accuracy of the proposed method is up to 98% from all the tested cases.