Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gestures as Input: Neuroelectric Joysticks and Keyboards
IEEE Pervasive Computing
Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EMG-based hand gesture recognition for realtime biosignal interfacing
Proceedings of the 13th international conference on Intelligent user interfaces
Enabling always-available input with muscle-computer interfaces
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Identification of Hand and Finger Movements Using Multi Run ICA of Surface Electromyogram
Journal of Medical Systems
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Aiming at the use of hand gestures for human-computer interaction, this paper presents an approach to identify hand gestures using muscle activity separated from electromyogram (EMG) using independent component analysis. While there are a number of previous reported works where EMG has been used to identify movement, 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 paper reports experimental results where the system was accurately able to identify the hand gesture using this technique for all the experiments (100%). The system has been shown not to be sensitive to electrode position as the experiments were repeated on different days. The advantage of such a system is that it is easy to train by a lay user, and can easily be implemented in real time after the initial training.