Evolutionary fuzzy discriminant analysis feature projection technique in myoelectric control

Khushaba, RN; Al-Jumaily, A; Al-Ani, A

Evolutionary fuzzy discriminant analysis feature projection technique in myoelectric control

Khushaba, RN

Al-Jumaily, A

Al-Ani, A

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Publication Type:: Journal Article
Citation:: Pattern Recognition Letters, 2009, 30 (7), pp. 699 - 707
Issue Date:: 2009-05-01

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Field	Value	Language
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.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.date.issued	2009-05-01	en_US
dc.identifier.citation	Pattern Recognition Letters, 2009, 30 (7), pp. 699 - 707	en_US
dc.identifier.issn	0167-8655	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9341
dc.identifier.uri	http://hdl.handle.net/10453/34995
dc.description.abstract	The controller of a multifunction prosthetic hand usually employs a pattern recognition scheme to discriminate between the myoelectric signals (MES) from different classes of the forearm movements. The MES is recorded using a multichannel approach that makes the feature vector size very large. Hence a dimensionality reduction technique is needed to identify an informative moderate size feature set. This paper proposes a novel feature projection technique based on a combination of fisher linear discriminant analysis (LDA), fuzzy logic (FL), and differential evolution (DE) optimization technique. The new technique, DEFLDA, assigns different membership degrees to the data points in order to reduce the effect of overlapping points in the discrimination process. Furthermore, an optimized weighting scheme is presented in which certain weights are assigned to the features according to their contribution in the discrimination process. The proposed DEFLDA is tested on different datasets and compared with other projection techniques to prove its functionality. © 2009 Elsevier B.V.	en_US
dc.relation.ispartof	Pattern Recognition Letters	en_US
dc.relation.isbasedon	10.1016/j.patrec.2009.02.004	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Evolutionary fuzzy discriminant analysis feature projection technique in myoelectric control	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	30	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	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.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

The controller of a multifunction prosthetic hand usually employs a pattern recognition scheme to discriminate between the myoelectric signals (MES) from different classes of the forearm movements. The MES is recorded using a multichannel approach that makes the feature vector size very large. Hence a dimensionality reduction technique is needed to identify an informative moderate size feature set. This paper proposes a novel feature projection technique based on a combination of fisher linear discriminant analysis (LDA), fuzzy logic (FL), and differential evolution (DE) optimization technique. The new technique, DEFLDA, assigns different membership degrees to the data points in order to reduce the effect of overlapping points in the discrimination process. Furthermore, an optimized weighting scheme is presented in which certain weights are assigned to the features according to their contribution in the discrimination process. The proposed DEFLDA is tested on different datasets and compared with other projection techniques to prove its functionality. © 2009 Elsevier B.V.

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http://hdl.handle.net/10453/9341