A myoelectric interface for robotic hand control using support vector machine

Abstract

This paper reports a new myoelectric interface for robotic hand control consisting of two main parts. The first part concerns the motion classification using electromyogram (EMG) signals of a support vector machine (SVM). Because there has been little research on the application of the SVM to motion classification using EMG signals, its effectiveness has not yet been established. The SVM has some advantages with respect to generalization and computational complexity, and therefore, we used the SVM to examine its classification ability. The second part concerns the estimation of an operator's joint angle corresponding to the motion determined by the first part. Estimation of the operator's joint angles is based on EMG-Joint angle models, which express the linear relationships between the EMG signals and joint angles. To verify the effectiveness of our interface, we performed off-line hand motion classification and real-time robotic hand control experiments with eight subjects. The experimental results showed that seven hand motions achieved a classification rate of more than 90% for all subjects. In addition, a three-dimensional computer graphics robotic hand was controlled in real-time (62.5 Hz) without delay.