Estimation of walking energy expenditure by using support vector regression

Su, SW; Wang, L; Celler, BG; Ambikairajah, E; Savkin, AV

Estimation of walking energy expenditure by using support vector regression

Su, SW

Wang, L Celler, BG Ambikairajah, E Savkin, AV

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Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2005, 7 VOLS pp. 3526 - 3529
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Celler, BG	en_US
dc.contributor.author	Ambikairajah, E	en_US
dc.contributor.author	Savkin, AV	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2005, 7 VOLS pp. 3526 - 3529	en_US
dc.identifier.isbn	0780387406	en_US
dc.identifier.isbn	9780780387409	en_US
dc.identifier.issn	0589-1019	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11081
dc.description.abstract	This paper develops a new predictor of walking energy expenditure from wireless measurements of body movements using triaxial accelerometers. Reliable data were collected from repeated walking experiments in different conditions on a treadmill with simultaneous measurement of expired oxygen and carbon dioxide. Support vector regression, a powerful non-linear regression method, was used to process and model the data. This novel processing method sets this investigation apart from existing papers. Good results were achieved in the robust estimation of walking related energy expenditure from a number of variables derived from triaxial accelerometer and treadmill speed. ©2005 IEEE.	en_US
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en_US
dc.title	Estimation of walking energy expenditure by using support vector regression	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7 VOLS	en_US
utslib.for	090303 Biomedical Instrumentation	en_US
dc.location.activity	Shanghai, China	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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	7 VOLS	en_US

Abstract:

This paper develops a new predictor of walking energy expenditure from wireless measurements of body movements using triaxial accelerometers. Reliable data were collected from repeated walking experiments in different conditions on a treadmill with simultaneous measurement of expired oxygen and carbon dioxide. Support vector regression, a powerful non-linear regression method, was used to process and model the data. This novel processing method sets this investigation apart from existing papers. Good results were achieved in the robust estimation of walking related energy expenditure from a number of variables derived from triaxial accelerometer and treadmill speed. ©2005 IEEE.

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http://hdl.handle.net/10453/11081