Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling

Su, SW; Celler, BG; Savkin, AV; Nguyen, HT; Cheng, TM; Guo, Y; Wang, L

Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling

Su, SW

Celler, BG Savkin, AV Nguyen, HT

Cheng, TM Guo, Y Wang, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology", 2008, pp. 2431 - 2434
Issue Date:: 2008-01-01

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Field	Value	Language
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Celler, BG	en_US
dc.contributor.author	Savkin, AV	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Cheng, TM	en_US
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Wang, L	en_US
dc.date.issued	2008-01-01	en_US
dc.identifier.citation	Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology", 2008, pp. 2431 - 2434	en_US
dc.identifier.isbn	9781424418152	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11002
dc.description.abstract	Noninvasive portable sensors are becoming popular in biomedical engineering practice due to its ease of use. This paper investigates the estimation of human oxygen uptake (VO2) of treadmill exercises by using multiple portable sensors (wireless heart rate sensor and triaxial accelerometers). For this purpose, a multivariable Hammerstein model identification method is developed. Well designed PRBS type of exercises protocols are employed to decouple the identification of linear dynamics with that of nonlinearities of Hammerstein systems. The support vector machine regression is applied to model the static nonlinearities. Multivariable ARX modelling approach is used for the identification of dynamic part of the Hammerstein systems. It is observed the obtained nonlinear multivariable model can achieve better estimations compared with single input single output models. The established multivariable model has also the potential to facilitate dynamic estimation of energy expenditure for outdoor exercises, which is the next research step of this study. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Oxygen	en_US
dc.subject.mesh	Monitoring, Ambulatory	en_US
dc.subject.mesh	Exercise	en_US
dc.subject.mesh	Multivariate Analysis	en_US
dc.subject.mesh	Regression Analysis	en_US
dc.subject.mesh	Biosensing Techniques	en_US
dc.subject.mesh	Biomedical Engineering	en_US
dc.subject.mesh	Oxygen Consumption	en_US
dc.subject.mesh	Heart Rate	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Acceleration	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Male	en_US
dc.title	Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling	en_US
dc.type	Conference Proceeding
utslib.for	090303 Biomedical Instrumentation	en_US
dc.location.activity	Vancouver, Canada	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

Abstract:

Noninvasive portable sensors are becoming popular in biomedical engineering practice due to its ease of use. This paper investigates the estimation of human oxygen uptake (VO2) of treadmill exercises by using multiple portable sensors (wireless heart rate sensor and triaxial accelerometers). For this purpose, a multivariable Hammerstein model identification method is developed. Well designed PRBS type of exercises protocols are employed to decouple the identification of linear dynamics with that of nonlinearities of Hammerstein systems. The support vector machine regression is applied to model the static nonlinearities. Multivariable ARX modelling approach is used for the identification of dynamic part of the Hammerstein systems. It is observed the obtained nonlinear multivariable model can achieve better estimations compared with single input single output models. The established multivariable model has also the potential to facilitate dynamic estimation of energy expenditure for outdoor exercises, which is the next research step of this study. © 2008 IEEE.

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http://hdl.handle.net/10453/11002