Finger joint continuous interpretation based on sEMG signals and muscular model

The human hand is very dexterous and can perform various of gestures in activities of daily living. Only dividing the motions of hand into several types and applying pattern recognition method for implementation of manipulation control may result in low dexterity and delicacy. In this paper, a novel finger joint interpretation method based on sEMG signals and muscular model is presented. The motion of finger is flexion and extension without any external resistant force and at a natural movement velocity. sEMG signals are recorded from flexor digitorum superficialis and extensor digitorum of the forearm. The Hill-based muscular model is used to calculate the force of muscles according to sEMG signals. In this paper, we assume that the changing of force corresponds directly to the motions of fingers given the circumstance that the subjects hold nothing in their hand and keep the movement velocity. The curve fitting method and Kalman filter are implemented to calculate the relation between force and basic movements of digits. Five subjects participated in the experiment to evaluate the efficiency of this method.