A Method of Recognizing Finger Motion Using Wavelet Transform of Surface EMG Signal

In this paper, an identification method of finger motions using the wavelet transform of multi-channel electromyography (EMG) signal is presented. The first step of this method is to analyze surface EMG signal detected from the subject's upper arm using the multi-resolution of wavelet transform, and extract features using the variance, maximum and mean absolute value of the wavelet coefficients. In this way, a new feature space is established by wavelet coefficients. The second step is to import the feature values into an Artificial Neural Network (ANN) to identify the finger motion. Based on the results of experiments, it is concluded that this method is effective in identification of finger motion. Thus, it provides an alternative approach to use the surface EMG in controlling the finger motion of a multi-fingered prosthetic hand.