Electromyography Based Finger Movement Identification for Human Computer Interface

This paper reports experiments conducted to classify single channel Surface Electromyogram recorded from the forearm with the flexion and extension of the different fingers. Controlled experiments were conducted where single channel SEMF was recorded from the flexor digitorum superficialis muscle for various finger positions from the volunteers. A modified wavelet network called Thresholding Wavelet Networks that has been developed by the authors (D Kumar, 2003) has been applied for this classification. The purpose of this research was towards developing a reliable man machine interface that could have applications for rehabilitation, robotics and industry. The network is promising with accuracy better than 85%.