Swarm Intelligence In Myoelectric Control: Particle Swarm Based Dimensionality Reduction

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

Swarm Intelligence In Myoelectric Control: Particle Swarm Based Dimensionality Reduction

Khushaba, RN

Al-Ani, A

Al-Jumaily, A

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Publisher:: IASTED
Publication Type:: Conference Proceeding
Citation:: 6th IASTED International Conference on Biomedical Engineering (BioMED 2008), 2008, pp. 601 - 694
Issue Date:: 2008-01

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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.contributor.editor	Hierlemann, A	en_US
dc.date	2008-02-13	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	6th IASTED International Conference on Biomedical Engineering (BioMED 2008), 2008, pp. 601 - 694	en_US
dc.identifier.isbn	978-0-88986-721-5	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10813
dc.description.abstract	The myoelectric signals (MES) from human muscles have been utilized in many applications such as prosthesis control. The identification of various MES temporal structures is used to control the movement of prosthetic devices by utilizing a pattern recognition approach. Recent advances in this field have shown that there are a number of factors limiting the clinical availability of such systems. Several control strategies have been proposed but deficiencies still exist with most of those strategies especially with the Dimensionality Reduction (DR) part. This paper proposes using Particle Swarm Optimization (PSO) algorithm with the concept of Mutual Information (MI) to produce a novel hybrid feature selection algorithm. The new algorithm, called PSOMIFS, is utilized as a DR tool in myoelectric control problems. The PSOMIFS will be compared with other techniques to prove the effectiveness of the proposed method. Accurate results are acquired using only a small subset of the original feature set producing a classification accuracy of 99% across a problem of ten classes based on tests done on six subjects MES datasets.	en_US
dc.publisher	IASTED	en_US
dc.relation.ispartof	6th IASTED International Conference on Biomedical Engineering (BioMED 2008)	en_US
dc.relation.ispartof	IASTED International Conference on Biomedical Engineering	en_US
dc.title	Swarm Intelligence In Myoelectric Control: Particle Swarm Based Dimensionality Reduction	en_US
dc.type	Conference Proceeding
utslib.location	Canada	en_US
utslib.location.activity	Austria	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
dc.location.activity	Austria	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.consider-herdc	true	en_US
pubs.place-of-publication	Canada	en_US
pubs.start-date	2008-02-13	en_US

Abstract:

The myoelectric signals (MES) from human muscles have been utilized in many applications such as prosthesis control. The identification of various MES temporal structures is used to control the movement of prosthetic devices by utilizing a pattern recognition approach. Recent advances in this field have shown that there are a number of factors limiting the clinical availability of such systems. Several control strategies have been proposed but deficiencies still exist with most of those strategies especially with the Dimensionality Reduction (DR) part. This paper proposes using Particle Swarm Optimization (PSO) algorithm with the concept of Mutual Information (MI) to produce a novel hybrid feature selection algorithm. The new algorithm, called PSOMIFS, is utilized as a DR tool in myoelectric control problems. The PSOMIFS will be compared with other techniques to prove the effectiveness of the proposed method. Accurate results are acquired using only a small subset of the original feature set producing a classification accuracy of 99% across a problem of ten classes based on tests done on six subjects MES datasets.

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http://hdl.handle.net/10453/10813