Head Direction Command Classification using an Adaptive Optimal Bayesian Neural Network

Nguyen, HT; Nguyen Thanh, S; Taylor, PW; Middleton, J

Head Direction Command Classification using an Adaptive Optimal Bayesian Neural Network

Nguyen, HT

Nguyen Thanh, S Taylor, PW Middleton, J

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Publisher:: InternationalSar
Publication Type:: Journal Article
Citation:: International Journal of Factory Automation, Robotics and Soft Computing, 2007, July, 1 (3), pp. 98 - 103
Issue Date:: 2007-01

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Field	Value	Language
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Nguyen Thanh, S	en_US
dc.contributor.author	Taylor, PW	en_US
dc.contributor.author	Middleton, J	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	International Journal of Factory Automation, Robotics and Soft Computing, 2007, July, 1 (3), pp. 98 - 103	en_US
dc.identifier.issn	1828-6984	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5829
dc.description.abstract	Mobility has become very important for our quality of life. Head movement is a natural form of pointing and can be used to directly replace the joystick for severely disabled people. In this paper, we describe the development of an optimal Bayesian neural network for the classification of head direction commands in a hands-free wheelchair control system as it allows strong generalisation during the training phase and does not require a validation data set. Experimental results show that with limited training data, an adaptive optimal Bayesian neural network can be developed to classify head direction commands by disabled users with a high sensitivity and specificity of 93.75% and 97.92% respectively.	en_US
dc.publisher	InternationalSar	en_US
dc.relation	http://purl.org/au-research/grants/arc/LE0454081
dc.relation	http://purl.org/au-research/grants/arc/DP0666942
dc.relation.ispartof	International Journal of Factory Automation, Robotics and Soft Computing	en_US
dc.title	Head Direction Command Classification using an Adaptive Optimal Bayesian Neural Network	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	1	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	090305 Rehabilitation Engineering	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.citation.edition	July	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.consider-herdc	true	en_US
pubs.edition	July	en_US
pubs.issue	3	en_US
pubs.volume	1	en_US

Abstract:

Mobility has become very important for our quality of life. Head movement is a natural form of pointing and can be used to directly replace the joystick for severely disabled people. In this paper, we describe the development of an optimal Bayesian neural network for the classification of head direction commands in a hands-free wheelchair control system as it allows strong generalisation during the training phase and does not require a validation data set. Experimental results show that with limited training data, an adaptive optimal Bayesian neural network can be developed to classify head direction commands by disabled users with a high sensitivity and specificity of 93.75% and 97.92% respectively.

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

http://hdl.handle.net/10453/5829