Subtle Hand Gesture Identification for Human-Computer Interaction using Independent Component Analysis of Surface Electromyography

Surface electromyography (sEMG) is an indicator of muscle activity and is related to body movement and posture. One common shortcoming in the use of sEMG is to distinguish between small actions that require simultaneous contraction of number of adjoining muscles. This paper presents a method for subtle hand gesture identification from sEMG of the forearm by decomposing the signal into components originating from different muscles. The processing requires the decomposition of the surface EMG by independent component analysis (ICA) technique. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prostheses and other computer-assisted devices. The proposed model-based approach is able to overcome the ambiguity problems (order and magnitude problems) of ICA methods by selecting an a priori mixing matrix based on known hand muscle anatomy. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify the data with 97% accuracy.