Improved Head Direction Command Classification Using An Optimised Bayesian Neural Network

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Show simple item record Nguyen Thanh, Son en_US Nguyen, Hung en_US Taylor, Philip en_US Middleton, James en_US
dc.contributor.editor N/A en_US 2009-11-09T05:39:14Z 2009-11-09T05:39:14Z 2006 en_US
dc.identifier 2006005651 en_US
dc.identifier.citation Nguyen Thanh Son et al. 2006, 'Improved Head Direction Command Classification Using An Optimised Bayesian Neural Network', IEEE, New York, USA, pp. 5679-5682. en_US
dc.identifier.issn 14244-0033-3 en_US
dc.identifier.other E1 en_US
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 en_US
dc.publisher IEEE en_US
dc.relation.isbasedon en_US
dc.title Improved Head Direction Command Classification Using An Optimised Bayesian Neural Network en_US
dc.parent Proceedings of the 28th IEEE EMBS Annual International Conference en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation New York, USA en_US
dc.identifier.startpage 5679 en_US
dc.identifier.endpage 5682 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference.location New York, USA en_US
dc.for 090305 en_US
dc.personcode 10113710 en_US
dc.personcode 840115 en_US
dc.personcode 114716 en_US
dc.personcode 0000017333 en_US
dc.percentage 100 en_US 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 20060830 en_US
dc.location.activity New York, USA en_US
dc.description.keywords Bayesian neural networks, hands-free control, head movement, power wheelchairs en_US

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