Index finger motion recognition using self-advise support vector machine

Anam, K; Al Jumaily, A; Maali, Y

Index finger motion recognition using self-advise support vector machine

Anam, K

Al Jumaily, A

Maali, Y

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Publication Type:: Journal Article
Citation:: International Journal on Smart Sensing and Intelligent Systems, 2014, 7 (2), pp. 644 - 657
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Anam, K https://orcid.org/0000-0003-4169-2237	en_US
dc.contributor.author	Al Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Maali, Y	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	International Journal on Smart Sensing and Intelligent Systems, 2014, 7 (2), pp. 644 - 657	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30178
dc.description.abstract	Because of the functionality of an index finger, the disability of its motion in the modern age can decrease the person's quality of life. As a part of rehabilitation therapy, the recognition of the index finger motion for rehabilitation purposes should be done properly. This paper proposes a novel recognition system of the index finger motion suing a cutting-edge method and its improvements. The proposed system consists of combination of feature extraction method, a dimensionality reduction and well-known classifier, Support Vector Machine (SVM). An improvement of SVM, Self-advise SVM (SA-SVM), is tested to evaluate and compare its performance with the original one. The experimental result shows that SA-SVM improves the classification performance by on average 0.63 %.	en_US
dc.relation.ispartof	International Journal on Smart Sensing and Intelligent Systems	en_US
dc.title	Index finger motion recognition using self-advise support vector machine	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1007 Nanotechnology	en_US
dc.location.activity	New Zeland
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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Because of the functionality of an index finger, the disability of its motion in the modern age can decrease the person's quality of life. As a part of rehabilitation therapy, the recognition of the index finger motion for rehabilitation purposes should be done properly. This paper proposes a novel recognition system of the index finger motion suing a cutting-edge method and its improvements. The proposed system consists of combination of feature extraction method, a dimensionality reduction and well-known classifier, Support Vector Machine (SVM). An improvement of SVM, Self-advise SVM (SA-SVM), is tested to evaluate and compare its performance with the original one. The experimental result shows that SA-SVM improves the classification performance by on average 0.63 %.

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http://hdl.handle.net/10453/30178