Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network

Al-Fatlawi, AH; Fatlawi, HK; Ling, SH

Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network

Al-Fatlawi, AH Fatlawi, HK Ling, SH

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 2871 - 2874
Issue Date:: 2017-09-13

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Field	Value	Language
dc.contributor.author	Al-Fatlawi, AH	en_US
dc.contributor.author	Fatlawi, HK	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2017-09-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 2871 - 2874	en_US
dc.identifier.isbn	9781509028092	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116150
dc.description.abstract	© 2017 IEEE. Daily physical activities monitoring is benefiting the health care field in several ways, in particular with the development of the wearable sensors. This paper adopts effective ways to calculate the optimal number of the necessary sensors and to build a reliable and a high accuracy monitoring system. Three data mining algorithms, namely Decision Tree, Random Forest and PART Algorithm, have been applied for the sensors selection process. Furthermore, the deep belief network (DBN) has been investigated to recognise 33 physical activities effectively. The results indicated that the proposed method is reliable with an overall accuracy of 96.52% and the number of sensors is minimised from nine to six sensors.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2017.8037456	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Monitoring, Ambulatory	en_US
dc.subject.mesh	Exercise	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Data Mining	en_US
dc.subject.mesh	Wearable Electronic Devices	en_US
dc.title	Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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:

© 2017 IEEE. Daily physical activities monitoring is benefiting the health care field in several ways, in particular with the development of the wearable sensors. This paper adopts effective ways to calculate the optimal number of the necessary sensors and to build a reliable and a high accuracy monitoring system. Three data mining algorithms, namely Decision Tree, Random Forest and PART Algorithm, have been applied for the sensors selection process. Furthermore, the deep belief network (DBN) has been investigated to recognise 33 physical activities effectively. The results indicated that the proposed method is reliable with an overall accuracy of 96.52% and the number of sensors is minimised from nine to six sensors.

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http://hdl.handle.net/10453/116150