@inproceedings{b359350208ac46d392b9944a16139b0f,
title = "Hand motion identification using independent component analysis of data glove and multichannel surface EMG",
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.",
author = "Chen, {Pei Jarn} and Chang, {Ming Wen} and Du, {Yi Chun}",
note = "Publisher Copyright: {\textcopyright} 2015 Taylor & Francis Group, London.; 3rd International Conference on Innovation, Communication and Engineering, ICICE 2014 ; Conference date: 17-10-2014 Through 22-10-2014",
year = "2015",
doi = "10.1201/b18737-39",
language = "English",
isbn = "9781138027527",
series = "Innovation in Design, Communication and Engineering - Proceedings of the 3rd International Conference on Innovation, Communication and Engineering, ICICE 2014",
publisher = "CRC Press/Balkema",
pages = "159--162",
editor = "Teen-Hang Meen and Prior, {Stephen D.} and Lam, {Artde Donald Kin-Tak}",
booktitle = "Innovation in Design, Communication and Engineering - Proceedings of the 3rd International Conference on Innovation, Communication and Engineering, ICICE 2014",
}