Recognition of Finger Motions for Myoelectric Prosthetic Hand via Surface EMG

Abstract

Recently, myoelectric prosthetic arms/hands, in which arm/hand gesture is distinguished by the identification of the surface electromyogram (SEMG) and the artificial arms/hands are controlled based on the result of the identification, have been studied (Weir, 2003). The SEMG has attracted an attention of researchers as an interface signal of an electric actuated arm for many years, and many of studies on the identification of the SEMG signal have been executed. Nowadays, it can be said that the SEMG is the most powerful source of control signal to develop the myoelectric prosthetic arms/hands. From the 1970s to the 1980s, elementary pattern recognition technique such as linear discriminant analysis, was used for the identification of the SEMG signals in (Graupe et al., 1978) and (Lee et al., 1984). In the 1990s, research on learning of a nonlinear map between the SEMG pattern and arm/hand gesture using a neural network has been performed in (Hudgins et al., 1993). Four kinds of motions of the forearm were distinguished by combining Hopfield-type neural network and back propagation neural network in (Kelly et al., 1990). The amplitude and the frequency band are typical information extracted from the SEMG signal, which can be used for the identification of arm/hand gesture. (Ito et al., 1992) presumed muscle tension from the EMG signal, and tried to control the forearm type myoelectric prosthetic arm driven by ultrasonic motor. (Farry et al., 1996) has proposed a technique of teleoperating the robot hand through the identification of frequency spectrum pattern of the SEMG signal. At present, however, most of the myoelectric prosthetic arms/hands can only realize some limited motions such as palmar seizure, flexion-extension of a wrist, and inward-outward rotation of a wrist. To the best of our knowledge, myoelectric prosthetic hands which can distinguish motions of plural fingers and can independently actuate each finger have not been developed yet, since recognition of independent motions of plural fingers through the SEMG is fairly difficult. Probably, a present cutting edge practical myoelectric prosthetic hand is the i-LIMB Hand produced by Touch Bionics Inc.. However, myoelectric prosthetic hands which imitate the hand of human, such as the i-LIMB Hand, are quite expensive, since they require accurate measurement of SEMG signal and use many actuators to drive finger joints. Therefore, improvement of operativity of the myoelectric prosthetic arms/hands and simplification of structure of the artificial arms/hands to lower the price are in demand.