SEMG for identifying hand gestures using ICA

Abstract

There is an urgent need for establishing a simple yet robust systemthat can be used to identify hand actions and gestures for machine and computer control. Researchers have reported the use of multi-channel electromyogram (EMG) to determine the hand actions and gestures. The limitation of the earlier works is that the systems are suitable for gross actions, and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using backpropogation neural networks. The system is tested and found to be effective in classifying EMG.