An Experimental Study of EMG Signal Features for Motion Discriminations Using Support Vector Machine

Abstract. This paper treats a discrimination problem of wrist/hand motion patterns from EMG signal. We examined which of the following signal features was appropriate: raw signal, integrated signal (IEMG), the max frequency component, power spectrum or rising voltage level. For the discrimination algorism, a Support Vector Machine (SVM) was introduced. As a result, around 80% discrimination rate was accomplished from integrated signal, power spectrum and rising voltage level. The IEMG signal scored the highest 83.3% discrimination rate