Hands-free Head-movement Gesture Recognition Using Artificial Neural Networks and the Magnified Gradient Function

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dc.contributor.author King, LM
dc.contributor.author Nguyen, HT
dc.contributor.author Taylor, PW
dc.contributor.editor Zhang, YT
dc.date.accessioned 2009-11-09T05:36:59Z
dc.date.issued 2005-01
dc.identifier.citation Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005, pp. 2063 - 2066
dc.identifier.isbn 0-7803-8740-6
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/2890
dc.description.abstract This paper presents a hands-free head-movement gesture classification system using a neural network employing the magnified gradient function (MGF) algorithm. The MGF increases the rate of convergence by magnifying the first order derivative of the activation function, whilst guaranteeing convergence. The MGF is tested on able-bodied and disabled users to measure its accuracy and performance. It is shown that for able-bodied users, a classification improvement from 98.25% to 99.85% is made, and 92.08% to 97.50% for disabled users
dc.publisher IEEE
dc.relation.isbasedon 10.1109/IEMBS.2005.1616864
dc.title Hands-free Head-movement Gesture Recognition Using Artificial Neural Networks and the Magnified Gradient Function
dc.type Conference Proceeding
dc.parent Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society
dc.journal.number en_US
dc.publocation Shanghai, China en_US
dc.publocation Shanghai, China
dc.publocation Shanghai, China
dc.publocation Shanghai, China
dc.publocation Shanghai, China
dc.identifier.startpage 2063 en_US
dc.identifier.endpage en_US
dc.identifier.endpage 2066 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.conference Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.conference.location Shanghai, China en_US
dc.for 090305 Rehabilitation Engineering
dc.personcode 840115
dc.personcode 114716
dc.personcode 044342
dc.percentage 100 en_US
dc.classification.name Rehabilitation Engineering en_US
dc.classification.type FOR-08 en_US
dc.custom Annual International Conference of the IEEE Engineering in Medicine and Biology Society en_US
dc.date.activity 20050901 en_US
dc.date.activity 2005-09-01
dc.date.activity 2005-09-01
dc.date.activity 2005-09-01
dc.date.activity 2005-09-01
dc.location.activity Shanghai, China en_US
dc.location.activity Shanghai, China
dc.location.activity Shanghai, China
dc.location.activity Shanghai, China
dc.location.activity Shanghai, China
dc.description.keywords Hand-free system, power wheelchair control, neural network, magnified gradient function en_US
dc.description.keywords Hand-free system, power wheelchair control, neural network, magnified gradient function
dc.description.keywords Hand-free system, power wheelchair control, neural network, magnified gradient function
dc.description.keywords Hand-free system, power wheelchair control, neural network, magnified gradient function
dc.description.keywords Hand-free system, power wheelchair control, neural network, magnified gradient function
pubs.embargo.period Not known
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/Strength - Health Technologies


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