Hand gestures for HCI using ICA of EMG

Naik, G.R., Kumar, D.K., Singh, V.P. and Palaniswam, M.

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.

Cite as: Naik, G.R., Kumar, D.K., Singh, V.P. and Palaniswam, M. (2006). Hand gestures for HCI using ICA of EMG. In Proc. HCSNet Workshop on the Use of Vision in Human-Computer Interaction, (VisHCI 2006), Canberra, Australia. CRPIT, 56. Goecke, R., Robles-Kelly, A. and Caelli, T., Eds. ACS. 67-72.

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