Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

Abstract

This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a five
fingered robotic hand to emulate six grasp types used during 70% daily living activities.