Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control

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

Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control

Khushaba, RN

Al-Ani, A

Al-Jumaily, A

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Biomedical Engineering, 2010, 57 (6), pp. 1410 - 1419
Issue Date:: 2010-06-01

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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-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	2010-06-01	en_US
dc.identifier.citation	IEEE Transactions on Biomedical Engineering, 2010, 57 (6), pp. 1410 - 1419	en_US
dc.identifier.issn	0018-9294	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13484
dc.description.abstract	Developing accurate and powerful electromyogram (EMG) driven prostheses controllers that can provide the amputees with effective control on their artificial limbs, has been the focus of a great deal of research in the past few years. One of the major challenges in such research is extracting an informative subset of features that can best discriminate between the different forearm movements. In this paper, a new dimensionality reduction method, referred to as orthogonal fuzzy neighborhood discriminant analysis (OFNDA), is proposed as a response to such a challenge. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed OFNDA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The proposed OFNDA is validated on EMG datasets collected from seven subjects performing a range of 5 to 10 classes of forearm movements. Practical results indicate the significance of OFNDA in comparison to many other feature projection methods (including locality preserving and uncorrelated variants of discriminant analysis) with accuracies ranging from 97.66% to 87.84% for 5 to 10 classes of movements, respectively, using only two EMG electrodes. © 2010 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Biomedical Engineering	en_US
dc.relation.isbasedon	10.1109/TBME.2009.2039480	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Hand	en_US
dc.subject.mesh	Muscle, Skeletal	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Discriminant Analysis	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Action Potentials	en_US
dc.subject.mesh	Muscle Contraction	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Fuzzy Logic	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	57	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	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	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	57	en_US

Abstract:

Developing accurate and powerful electromyogram (EMG) driven prostheses controllers that can provide the amputees with effective control on their artificial limbs, has been the focus of a great deal of research in the past few years. One of the major challenges in such research is extracting an informative subset of features that can best discriminate between the different forearm movements. In this paper, a new dimensionality reduction method, referred to as orthogonal fuzzy neighborhood discriminant analysis (OFNDA), is proposed as a response to such a challenge. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed OFNDA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The proposed OFNDA is validated on EMG datasets collected from seven subjects performing a range of 5 to 10 classes of forearm movements. Practical results indicate the significance of OFNDA in comparison to many other feature projection methods (including locality preserving and uncorrelated variants of discriminant analysis) with accuracies ranging from 97.66% to 87.84% for 5 to 10 classes of movements, respectively, using only two EMG electrodes. © 2010 IEEE.

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http://hdl.handle.net/10453/13484