Nonparametric Modelling Based Model Predictive Control for Human Heart Rate Regulation during Treadmill Exercise

Wang, L; Yang, Y; Su, S

Nonparametric Modelling Based Model Predictive Control for Human Heart Rate Regulation during Treadmill Exercise

Wang, L Yang, Y Su, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, 2021, pp. 148-151
Issue Date:: 2021-12-09

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Field	Value	Language
dc.contributor.author	Wang, L
dc.contributor.author	Yang, Y
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852
dc.date	2021-11-01
dc.date.accessioned	2022-06-11T01:09:13Z
dc.date.available	2022-06-11T01:09:13Z
dc.date.issued	2021-12-09
dc.identifier.citation	2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, 2021, pp. 148-151
dc.identifier.isbn	9781728111797
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/158093
dc.description.abstract	This paper applies a kernel-based nonparametric modelling method to estimate the heart rate response during treadmill exercise and proposes a model predictive control (MPC) method to perform heart rate control for an automated treadmill system. This kernel-based method introduces a kernel regularisation term, which brings prior information to the model estimation phase. By adding this prior information, the experimental protocol can be significantly simplified and only a small amount of model training experiments are needed. The model parameters were experimentally estimated from 12 participants for the treadmill exercise with a short and practical exercise protocol. The modelling results show that the model identified using the proposed method can accurately describe the heart rate response to the treadmill exercise. Based on the identified model, an MPC controller is designed to track a predefined reference heart rate profile. An advantage is the speed and acceleration of the treadmill can be limited to within a safe range for vulnerable exercisers. The proposed controller was experimentally validated in a self-developed automated treadmill system. The tracking results indicate that the desired automatic treadmill system can regulate the participants’ heart rate to follow the reference profile efficiently and safely.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
dc.relation.ispartof	2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
dc.relation.isbasedon	10.1109/embc46164.2021.9629548
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Algorithms
dc.subject.mesh	Exercise
dc.subject.mesh	Exercise Test
dc.subject.mesh	Heart Rate
dc.subject.mesh	Humans
dc.subject.mesh	Humans
dc.subject.mesh	Exercise Test
dc.subject.mesh	Exercise
dc.subject.mesh	Heart Rate
dc.subject.mesh	Algorithms
dc.title	Nonparametric Modelling Based Model Predictive Control for Human Heart Rate Regulation during Treadmill Exercise
dc.type	Conference Proceeding
utslib.citation.volume	2021
utslib.location.activity	Mexico
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 Health
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-11T01:09:11Z
pubs.finish-date	2021-11-05
pubs.publication-status	Published
pubs.start-date	2021-11-01
pubs.volume	2021

Abstract:

This paper applies a kernel-based nonparametric modelling method to estimate the heart rate response during treadmill exercise and proposes a model predictive control (MPC) method to perform heart rate control for an automated treadmill system. This kernel-based method introduces a kernel regularisation term, which brings prior information to the model estimation phase. By adding this prior information, the experimental protocol can be significantly simplified and only a small amount of model training experiments are needed. The model parameters were experimentally estimated from 12 participants for the treadmill exercise with a short and practical exercise protocol. The modelling results show that the model identified using the proposed method can accurately describe the heart rate response to the treadmill exercise. Based on the identified model, an MPC controller is designed to track a predefined reference heart rate profile. An advantage is the speed and acceleration of the treadmill can be limited to within a safe range for vulnerable exercisers. The proposed controller was experimentally validated in a self-developed automated treadmill system. The tracking results indicate that the desired automatic treadmill system can regulate the participants’ heart rate to follow the reference profile efficiently and safely.

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

http://hdl.handle.net/10453/158093