Bayesian neural network classification of head movement direction using various advanced optimisation training algorithms

Nguyen, ST; Nguyen, HT; Taylor, PB

Bayesian neural network classification of head movement direction using various advanced optimisation training algorithms

Nguyen, ST Nguyen, HT

Taylor, PB

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006, 2006, 2006 pp. 1014 - 1019
Issue Date:: 2006-12-22

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Field	Value	Language
dc.contributor.author	Nguyen, ST	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Taylor, PB	en_US
dc.date.issued	2006-12-22	en_US
dc.identifier.citation	Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006, 2006, 2006 pp. 1014 - 1019	en_US
dc.identifier.isbn	1424400406	en_US
dc.identifier.isbn	9781424400409	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2767
dc.description.abstract	Head movement is one of the most effective hands-free control modes for powered wheelchairs. It provides the necessary mobility assistance to severely disabled people and can be used to replace the joystick directly. In this paper, we describe the development of Bayesian neural networks for the classification of head movement commands in a hands-free wheelchair control system. Bayesian neural networks allow strong generalisation of head movement classifications during the training phase and do not require a validation data set. Various advanced optimisation training algorithms are explored. Experimental results show that Bayesian neural networks can be developed to classify head movement commands by abled and disabled people accurately with limited training data.	en_US
dc.relation.ispartof	Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006	en_US
dc.relation.isbasedon	10.1109/BIOROB.2006.1639224	en_US
dc.title	Bayesian neural network classification of head movement direction using various advanced optimisation training algorithms	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2006	en_US
utslib.for	090305 Rehabilitation Engineering	en_US
dc.location.activity	Pisa, Italy	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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2006	en_US

Abstract:

Head movement is one of the most effective hands-free control modes for powered wheelchairs. It provides the necessary mobility assistance to severely disabled people and can be used to replace the joystick directly. In this paper, we describe the development of Bayesian neural networks for the classification of head movement commands in a hands-free wheelchair control system. Bayesian neural networks allow strong generalisation of head movement classifications during the training phase and do not require a validation data set. Various advanced optimisation training algorithms are explored. Experimental results show that Bayesian neural networks can be developed to classify head movement commands by abled and disabled people accurately with limited training data.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/2767