Swarm based fuzzy discriminant analysis for multifunction prosthesis control

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

Swarm based fuzzy discriminant analysis for multifunction prosthesis control

Khushaba, RN

Al-Ani, A

Al-Jumaily, A

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Publication Type:: Chapter
Citation:: 2010, 5998 LNAI pp. 197 - 206
Issue Date:: 2010-05-21

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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-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2010-05-21	en_US
dc.identifier.citation	2010, 5998 LNAI pp. 197 - 206	en_US
dc.identifier.isbn	3642121586	en_US
dc.identifier.isbn	9783642121586	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14268
dc.description.abstract	In order to interface the amputee's with the real world, the myoelectric signal (MES) from human muscles is usually utilized within a pattern recognition scheme as an input to the controller of a prosthetic device. Since the MES is recorded using multi channels, the feature vector size can become very large. In order to reduce the computational cost and enhance the generalization capability of the classifier, a dimensionality reduction method is needed to identify an informative moderate size feature set. This paper proposes a new fuzzy version of the well known Fisher's Linear Discriminant Analysis (LDA) feature projection technique. Furthermore, based on the fact that certain muscles might contribute more to the discrimination process, a novel feature weighting scheme is also presented by employing Particle Swarm Optimization (PSO) for the weights calculation. The new method, called PSOFLDA, is tested on real MES datasets and compared with other techniques to prove its superiority. © 2010 Springer-Verlag.	en_US
dc.relation.isbasedon	10.1007/978-3-642-12159-3_18	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Swarm based fuzzy discriminant analysis for multifunction prosthesis control	en_US
dc.type	Chapter
utslib.citation.volume	5998 LNAI	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	5998 LNAI	en_US

Abstract:

In order to interface the amputee's with the real world, the myoelectric signal (MES) from human muscles is usually utilized within a pattern recognition scheme as an input to the controller of a prosthetic device. Since the MES is recorded using multi channels, the feature vector size can become very large. In order to reduce the computational cost and enhance the generalization capability of the classifier, a dimensionality reduction method is needed to identify an informative moderate size feature set. This paper proposes a new fuzzy version of the well known Fisher's Linear Discriminant Analysis (LDA) feature projection technique. Furthermore, based on the fact that certain muscles might contribute more to the discrimination process, a novel feature weighting scheme is also presented by employing Particle Swarm Optimization (PSO) for the weights calculation. The new method, called PSOFLDA, is tested on real MES datasets and compared with other techniques to prove its superiority. © 2010 Springer-Verlag.

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http://hdl.handle.net/10453/14268