Performance of a Head-Movement Interface for Wheelchair Control

Taylor, PB; Nguyen, HT

Performance of a Head-Movement Interface for Wheelchair Control

Taylor, PB Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2003, 2 pp. 1590 - 1593
Issue Date:: 2003-12-01

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Field	Value	Language
dc.contributor.author	Taylor, PB	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2003-12-01	en_US
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2003, 2 pp. 1590 - 1593	en_US
dc.identifier.issn	0589-1019	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2785
dc.description.abstract	Head movement has been used as a control interface for people with motor impairments in a range of applications. Chin operated joysticks and switch arrays have been incorporated in control systems for electric wheelchairs but have several disadvantages, including being difficult to operate and aesthetically unattractive. A prototype wheelchair control interface has been developed that makes use of an artificial neural network (ANN) to recognize commands given by head movement. This paper presents the results of an experimental investigation of the ANN's performance in terms of classification accuracy and delay. It goes on to compare the results of disabled with able-bodied users, and assesses the effect of providing real-time feedback to the user. The results obtained indicate that ANN techniques can be used to classify head movements sufficiently quickly and accurately to be used in a practical interface. The provision of graphical real-time feedback does not appear to be crucial, but may be of benefit for particular cases.	en_US
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en_US
dc.title	Performance of a Head-Movement Interface for Wheelchair Control	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	090305 Rehabilitation Engineering	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
dc.location.activity	Cancun, Mexico	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	2	en_US

Abstract:

Head movement has been used as a control interface for people with motor impairments in a range of applications. Chin operated joysticks and switch arrays have been incorporated in control systems for electric wheelchairs but have several disadvantages, including being difficult to operate and aesthetically unattractive. A prototype wheelchair control interface has been developed that makes use of an artificial neural network (ANN) to recognize commands given by head movement. This paper presents the results of an experimental investigation of the ANN's performance in terms of classification accuracy and delay. It goes on to compare the results of disabled with able-bodied users, and assesses the effect of providing real-time feedback to the user. The results obtained indicate that ANN techniques can be used to classify head movements sufficiently quickly and accurately to be used in a practical interface. The provision of graphical real-time feedback does not appear to be crucial, but may be of benefit for particular cases.

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http://hdl.handle.net/10453/2785