Intelligent technologies for real-time biomedical engineering applications

Nguyen, HT

Intelligent technologies for real-time biomedical engineering applications

Nguyen, HT

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Publication Type:: Journal Article
Citation:: International Journal of Automation and Control, 2008, 2 (2-3), pp. 274 - 285
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	International Journal of Automation and Control, 2008, 2 (2-3), pp. 274 - 285	en_US
dc.identifier.issn	1740-7516	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9337
dc.description.abstract	Intelligent technologies are essential for many biomedical engineering applications in order to cope with a wide variety of patient conditions or user disability. The development of advanced optimisation training algorithms such as adaptive optimal Bayesian neural networks is particularly useful when only limited training data are available. Two specific biomedical engineering applications will be presented. The first application concerns the development of a non-invasive monitor for real-time detection of hypoglycaemic episodes in Type 1 diabetes mellitus patients (T1DM). The second application relates to the development of real-time hands-free wheelchair control systems using head movement to provide mobility independence for severely disabled people. Copyright © 2008, Inderscience Publishers.	en_US
dc.relation.ispartof	International Journal of Automation and Control	en_US
dc.relation.isbasedon	10.1504/IJAAC.2008.022181	en_US
dc.title	Intelligent technologies for real-time biomedical engineering applications	en_US
dc.type	Journal Article
utslib.citation.volume	2-3	en_US
utslib.citation.volume	2	en_US
utslib.for	0910 Manufacturing Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	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	open_access
pubs.issue	2-3	en_US
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Intelligent technologies are essential for many biomedical engineering applications in order to cope with a wide variety of patient conditions or user disability. The development of advanced optimisation training algorithms such as adaptive optimal Bayesian neural networks is particularly useful when only limited training data are available. Two specific biomedical engineering applications will be presented. The first application concerns the development of a non-invasive monitor for real-time detection of hypoglycaemic episodes in Type 1 diabetes mellitus patients (T1DM). The second application relates to the development of real-time hands-free wheelchair control systems using head movement to provide mobility independence for severely disabled people. Copyright © 2008, Inderscience Publishers.

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http://hdl.handle.net/10453/9337