Finger Motion Classification Using Surface-Electromyogram Signals

Abstract

The finger movement has the information about force, speed to bend and the combination of fingers. If these information is estimated, the many degrees of freedom interface can apply it. In this study, we aimed for the many degrees of freedom finger movement classification. We tried each fingers classification and the estimate of the flexural finger force using surface-electromyogram signals. In the technique, amount of characteristic are a cepstral coefficient of EMG signals and an integral calculus EMG signals. A support vector machine performs learning and classification. Therefore, I propose the classification technique and inspected a classification each finger and the combination of fingers by offline data handling using surface EMG signals.