Improving Isometric Hand Gesture Identification for HCI based on Independent Component Analysis in Bio-signal Processing


Dinesh Kant Kumar.

Student: Ganesh Naik.

Hans Weghorn, University of BA, Stuttgart, Germany.

Introduction: 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.
Significance: 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.
Applications: The system was tested across users to investigate the impact of inter-subject variation. The experimental results demonstrate an overall accuracy of 96%, and the system was shown being insensitive against electrode positions, since these successful experiments were repeated on different days. The advantage of such a system is, that it is easy to train by a lay user, and that it can easily be implemented as real-time processing after an initial training. Hence, EMG-based input devices can provide an effective solution for designing mobile interfaces that are subtle and intimate, and there exist a range of applications for communication, emotive machines and human computer interface.
Challenges: 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 have been overcome by using a priori knowledge of the hand muscle anatomy and a fixed un-mixing matrix. The classification is achieved using a back-propagation neural network. Experimental results are shown, where the system was able to reliably recognize motionless gestures.
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