Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal

In this manuscript, eight hand motions were classified using ten different extracted features from sEMG signals. These signals were collected from four different muscles placed on the forearm. It was found out that the performance of a classifier was improved through the implementation of more than one feature. We tested two feature combinations; the classification accuracy rate of 94 % was achieved using linear discriminant analysis (LDA) based on wavelength (WAVE), Wilson amplitude (WAMP), and root mean square combination. The performance of four wavelet families was tested to select the proper wavelet family that leads to highest classification rate. Our experimental results demonstrate that the highest average classification accuracy was 95 % achieved by implementing general neural network (GRNN) classification method based on energy of wavelet coefficients (using Sym4 family). Moreover, this study investigated the performance of three SVM-kernel functions (support vector machine) and found that polynomial function is the optimal choice in most cases. The highest achieved classification accuracy was 93 % using extracted wavelet coefficients.