SEMG for identifying hand gestures using ICA

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

There is an urgent need for establishing a simple yet robust systemthat 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.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Workshop on Biosignal Processing and Classification (BPC), in Conjunction with ICINCO 2006
Pages61-67
Number of pages7
Publication statusPublished - 2006
Externally publishedYes
Event2nd International Workshop on Biosignal Processing and Classification, BPC 2006, in Conjunction with ICINCO 2006 - Setubal, Portugal
Duration: 1 Aug 20065 Aug 2006

Conference

Conference2nd International Workshop on Biosignal Processing and Classification, BPC 2006, in Conjunction with ICINCO 2006
CountryPortugal
CitySetubal
Period1/08/065/08/06

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