Rate estimation for the monitoring of rehabilitation exercises

Weng, K; Nguyen, N; Nguyen, HT; Su, S

Rate estimation for the monitoring of rehabilitation exercises

Weng, K Nguyen, N

Nguyen, HT

Su, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2009, pp. 6267 - 6270
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Weng, K	en_US
dc.contributor.author	Nguyen, N https://orcid.org/0000-0002-9958-9876	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2009, pp. 6267 - 6270	en_US
dc.identifier.isbn	9781424432967	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10982
dc.description.abstract	This study investigates the rate estimation problem encountered in rehabilitation exercise monitoring by using noninvasive portable sensors. The purpose of this paper has two main parts. The first part is to find suitable approaches for the rate detection of tri-axial accelerometer (TA) signals and ECG signals respectively. It is found that the integral type approaches (the average magnitude difference function (AMDF) and autocorrelation function (ACF)) are particularly suitable for TA signal pre-processing, while differential type approaches are very efficient for electrocardiographic (ECG) signal pre-processing. The second part is to develop a square wave matching method to detect the rate from the pre-processed signals. Experimental results indicate that the proposed methods can effectively detect pace rate from TA and heart rate from ECG and remove undesirable spikes. ©2009 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009	en_US
dc.relation.isbasedon	10.1109/IEMBS.2009.5332391	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diagnosis, Computer-Assisted	en_US
dc.subject.mesh	Electrocardiography	en_US
dc.subject.mesh	Monitoring, Ambulatory	en_US
dc.subject.mesh	Exercise Therapy	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Motor Activity	en_US
dc.subject.mesh	Heart Rate	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Physical Exertion	en_US
dc.subject.mesh	Actigraphy	en_US
dc.title	Rate estimation for the monitoring of rehabilitation exercises	en_US
dc.type	Conference Proceeding
utslib.for	090305 Rehabilitation Engineering	en_US
dc.location.activity	Minneapolis, Minnesota, USA	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:

This study investigates the rate estimation problem encountered in rehabilitation exercise monitoring by using noninvasive portable sensors. The purpose of this paper has two main parts. The first part is to find suitable approaches for the rate detection of tri-axial accelerometer (TA) signals and ECG signals respectively. It is found that the integral type approaches (the average magnitude difference function (AMDF) and autocorrelation function (ACF)) are particularly suitable for TA signal pre-processing, while differential type approaches are very efficient for electrocardiographic (ECG) signal pre-processing. The second part is to develop a square wave matching method to detect the rate from the pre-processed signals. Experimental results indicate that the proposed methods can effectively detect pace rate from TA and heart rate from ECG and remove undesirable spikes. ©2009 IEEE.

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http://hdl.handle.net/10453/10982