Improved head direction command classification using an optimised Bayesian neural network.

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dc.contributor.author Nguyen, ST
dc.contributor.author Nguyen, HT
dc.contributor.author Taylor, PB
dc.contributor.author Middleton, J
dc.date.accessioned 2009-11-09T05:39:14Z
dc.date.issued 2006
dc.identifier.citation Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2006, pp. 5679 - 5682
dc.identifier.issn 1557-170X
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/3166
dc.description.abstract Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In hands-free control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Recently, neural networks have been shown to be useful not only for real-time pattern recognition but also for creating user-adaptive models. Since multi-layer perceptron neural networks trained using standard back-propagation may cause poor generalisation, the Bayesian technique has been proposed to improve the generalisation and robustness of these networks. This paper describes the use of Bayesian neural networks in developing a hands-free wheelchair control system. The experimental results show that with the optimised architecture, classification Bayesian neural networks can detect head commands of wheelchair users accurately irrespective to their levels of injuries.
dc.title Improved head direction command classification using an optimised Bayesian neural network.
dc.type Conference Proceeding
dc.parent Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.identifier.startpage 5679 en_US
dc.identifier.endpage 5682 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.location New York, USA en_US
dc.for 090305 Rehabilitation Engineering
dc.personcode 840115
dc.personcode 114716
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 20060830 en_US
dc.date.activity 2006-08-30
dc.location.activity New York, USA en_US
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
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)


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