Hand Sign Classification Employing Myoelectric Signals of Forearm

Abstract

Electromyogram (EMG) signals are generated in muscles, when the muscles contract and a joint is flexed or extended. EMG signals can be measured from a skin surface with noninvasive electrodes, and they include some information on motions such as muscle torque or joint angles. Hence, it is possible to achieve more intuitive human-machine interface using EMG signals than conventional interfaces such as joysticks, data gloves, motion captures. Various interfaces using EMG signals have been proposed to control robot hands (Graupe et al.; Jacobson et al.; Yoshikawa et al., 2009; Ibe at al.). Some methods for hand motion identification have been reported since the 1990s based on soft-computing approaches, e. g. artificial neural networks (Fukuda et al.; Hudgins et al.), fuzzy logic (Karlik & Tokhi; Chan et al.), support vector machine (Yoshikawa et al., 2007; Oskoei & Huosheng), and so on (Chen et al.; Huang et al.). These approaches have improved accuracy of motion discrimination and the number of discriminated motions. However, they need complicated processes and huge amount of calculations.