Offline Analysis of Hand Prosthesis for Trans Radial Amputation People Using Machine Learning Algorithm

Abstract

Identification hand gestures using electromyogram have been used to improve the performance of tarns radial amputees. Information obtained from noninvasive electrode can be used to control hand prosthesis. In this paper, we propose hand gestures system for improving performance of trans radial amputees using only two channels bio amplifier. Two hand gestures like hand open, hand close are performed by ten subjects and then preprocessed by band pass filter. Reference power used as feature extractor. Learning Vector Quantization Neural Network (LVQ) is used to identify pattern. From the identification output, it is concluded that the proposed two channel system is possible of online interfacing.