Classification of Grasp Types through Wavelet Decomposition of EMG Signals

In this paper, we present a methodology to classify grasp types based on two channel forearm electromyogram signals. Six grasp types are identified. Classification is through support vector machine using radial basis function kernel based on sum of wavelet decomposition coefficients of the electromyogram signals. In a study involving six subjects, we achieved an average recognition rate of 86%; better than that reported in the literature.