Hand motion identification using independent component analysis of data glove and multichannel surface EMG

Pei Jarn Chen, Ming Wen Chang, Yi Chun Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study presents an approach to identify hand motions using muscle activity separated from multichannel surface electromyogram (SEMG) and the information of data glove. There are nigh features included six features are extracted from each SEMG channel, and three features are computed from five bend sensors in the data glove. Independent Component Analysis (ICA) was used to examine its effect on independent component extraction and features reduction in this study. The results demonstrate that ICA can effectively reduce the amount of required computation data with the price of reduced identification rates. The results also indicate that the proposed method provides high accuracy (>90%) and fast processing time that is achieved to the performance of real-time system.

Original languageEnglish
Title of host publicationInnovation in Design, Communication and Engineering - Proceedings of the 3rd International Conference on Innovation, Communication and Engineering, ICICE 2014
EditorsTeen-Hang Meen, Stephen D. Prior, Artde Donald Kin-Tak Lam
PublisherCRC Press/Balkema
Pages159-162
Number of pages4
ISBN (Print)9781138027527
DOIs
Publication statusPublished - 2015
Event3rd International Conference on Innovation, Communication and Engineering, ICICE 2014 - Guiyang, Guizhou, China
Duration: 2014 Oct 172014 Oct 22

Publication series

NameInnovation in Design, Communication and Engineering - Proceedings of the 3rd International Conference on Innovation, Communication and Engineering, ICICE 2014

Other

Other3rd International Conference on Innovation, Communication and Engineering, ICICE 2014
CountryChina
CityGuiyang, Guizhou
Period14-10-1714-10-22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

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