Hands-free control of power wheelchairs using Bayesian neural network classification

Nguyen, S; Nguyen, H; Taylor, P

Hands-free control of power wheelchairs using Bayesian neural network classification

Nguyen, S Nguyen, H

Taylor, P

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Publication Type:: Conference Proceeding
Citation:: 2004 IEEE Conference on Cybernetics and Intelligent Systems, 2004, pp. 745 - 749
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Nguyen, S	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Taylor, P	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	2004 IEEE Conference on Cybernetics and Intelligent Systems, 2004, pp. 745 - 749	en_US
dc.identifier.isbn	0780386442	en_US
dc.identifier.isbn	9780780386440	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2691
dc.description.abstract	This paper describes the formulation and implementation of Bayesian neural networks for head-movement classification in a hands-free wheelchair navigation system. Bayesian neural network training adjusts the weight decay parameters automatically to their near-optimal values that give the best generalisation. Moreover, no separate validation set is used so all available data can be used for training. Experimental results are presented showing that Bayesian neural network can classify the head movement accurately.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LE0454081
dc.relation.ispartof	2004 IEEE Conference on Cybernetics and Intelligent Systems	en_US
dc.title	Hands-free control of power wheelchairs using Bayesian neural network classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Singapore	en_US
dc.location.activity	Prague, Czech Republic
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

Abstract:

This paper describes the formulation and implementation of Bayesian neural networks for head-movement classification in a hands-free wheelchair navigation system. Bayesian neural network training adjusts the weight decay parameters automatically to their near-optimal values that give the best generalisation. Moreover, no separate validation set is used so all available data can be used for training. Experimental results are presented showing that Bayesian neural network can classify the head movement accurately.

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

http://hdl.handle.net/10453/2691