SEMG for Identifying Hand Gestures using ICA

Ganesh R. Naik, Dinesh K. Kumar, Vijay Pal Singh, M. Palaniswami


There is an urgent need for establishing a simple yet robust system that can be used to identify hand actions and gestures for machine and computer control. Researchers have reported the use of multi-channel electromyogram (EMG) to determine the hand actions and gestures. 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 system is tested and found to be effective in classifying EMG.


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Paper Citation

in Harvard Style

R. Naik G., K. Kumar D., Pal Singh V. and Palaniswami M. (2006). SEMG for Identifying Hand Gestures using ICA . In Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006) ISBN 978-972-8865-67-2, pages 61-67. DOI: 10.5220/0001223500610067

in Bibtex Style

author={Ganesh R. Naik and Dinesh K. Kumar and Vijay Pal Singh and M. Palaniswami},
title={SEMG for Identifying Hand Gestures using ICA},
booktitle={Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006)},

in EndNote Style

JO - Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006)
TI - SEMG for Identifying Hand Gestures using ICA
SN - 978-972-8865-67-2
AU - R. Naik G.
AU - K. Kumar D.
AU - Pal Singh V.
AU - Palaniswami M.
PY - 2006
SP - 61
EP - 67
DO - 10.5220/0001223500610067