Two-channel surface electromyography for individual and combined finger movements

Anam, K; Khushaba, RN; Al-Jumaily, A

Two-channel surface electromyography for individual and combined finger movements

Anam, K

Khushaba, RN

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, pp. 4961 - 4964
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Anam, K https://orcid.org/0000-0003-4169-2237	en_US
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2013, pp. 4961 - 4964	en_US
dc.identifier.isbn	9781457702167	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26727
dc.description.abstract	This papecr proposes the pattern recognition system for individual and combined finger movements by using two channel electromyography (EMG) signals. The proposed system employs Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The advantage of the SRDA is its speed which is faster than original LDA so that it could deal with multiple features. In addition, the use of ELM which is fast and has similar classification performance to well-known SVM empowers the classification system. The experimental results show that the proposed system was able to recognize the individual and combined fingers movements with up to 98 % classification accuracy by using only just two EMG channels. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2013.6610661	en_US
dc.subject.mesh	Fingers	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Discriminant Analysis	en_US
dc.subject.mesh	Normal Distribution	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Young Adult	en_US
dc.title	Two-channel surface electromyography for individual and combined finger movements	en_US
dc.type	Conference Proceeding
utslib.for	090609 Signal Processing	en_US
utslib.for	090305 Rehabilitation Engineering	en_US
dc.location.activity	Osaka, Japan	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This papecr proposes the pattern recognition system for individual and combined finger movements by using two channel electromyography (EMG) signals. The proposed system employs Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The advantage of the SRDA is its speed which is faster than original LDA so that it could deal with multiple features. In addition, the use of ELM which is fast and has similar classification performance to well-known SVM empowers the classification system. The experimental results show that the proposed system was able to recognize the individual and combined fingers movements with up to 98 % classification accuracy by using only just two EMG channels. © 2013 IEEE.

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http://hdl.handle.net/10453/26727