Improving isometric hand gesture identification for HCI based on independent component analysis in bio-signal processing

Abstract

Hand gesture identification has various human computer interaction (HCI) applications. There is an urgent need for establishing a simple yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Here, an approach is explained to demonstrate how hand gestures can be identified from isometric muscular activity, where signal level is low and changes are very subtle. Obvious difficulties arise from a very poor signal to noise ratio in the recorded electromyograms (EMG). Independent component analysis (ICA) is applied to separate these low-level muscle activities. The order and magnitude ambiguity of ICA ..