SEMG for Human Computer Interface Using Ann to Navigate Wheel Chair

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Author(s): V. Rajesh (Sir C.R.R.C.O.E, Eluru, A.P, India)and P. Rajesh Kumar (Andhra University, India)
Copyright: 2012
Pages: 8
Source title: Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs
Source Author(s)/Editor(s): Joseph Tan (McMaster University, Canada)
DOI: 10.4018/978-1-4666-1755-1.ch012

Keywords: Health Information Systems / Medical Information Science Reference / Medicine & Healthcare

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Abstract

This paper presents an approach to identify hand gestures with muscle activity separated from electromyogram (EMG) using Back Propagation analysis with the goal of using hand gestures for human-computer interaction. While there are a number of previous reported works where EMG has been used to identify movement, the limitation of these 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 back propagation neural networks. The experimental results show that the system was accurately able to identify the hand gesture using this technique (95%). The advantage of this system is that it is easy to train one to use it and can easily be implemented in real time.

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SEMG for Human Computer Interface Using Ann to Navigate Wheel Chair

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