Collaborative Fall Detection Using Smart Phone and Kinect

Li, X; Nie, L; Xu, H; Wang, X

Collaborative Fall Detection Using Smart Phone and Kinect

Li, X Nie, L Xu, H Wang, X

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Mobile Networks and Applications, 2018, 23, (4), pp. 775-788
Issue Date:: 2018-08-01

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Field	Value	Language
dc.contributor.author	Li, X
dc.contributor.author	Nie, L
dc.contributor.author	Xu, H
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.date.accessioned	2022-08-29T04:57:51Z
dc.date.available	2022-08-29T04:57:51Z
dc.date.issued	2018-08-01
dc.identifier.citation	Mobile Networks and Applications, 2018, 23, (4), pp. 775-788
dc.identifier.issn	1383-469X
dc.identifier.issn	1572-8153
dc.identifier.uri	http://hdl.handle.net/10453/161020
dc.description.abstract	Humanfall detection has attracted broad attentions as sensors and mobile devices are increasingly adopted in real-life scenarios such as smart homes. The complexity of activities in home environments pose severe challenges to the fall detection research with respect to the detection accuracy. We propose a collaborative detection platform that combines two subsystems: a threshold-based fall detection subsystem using mobile phones and a support vector machine (SVM)-based fall detection subsystem using Kinects. Both subsystems have their respective confidence models and the platform detects falls by fusing the data of both subsystems using two methods: the logical rules-based and D-S evidence fusion theory-based methods. We have validated the two confidence models based on mobile phone and Kinect, which achieve the accuracy of 84.17% and 97.08%, respectively. Our collaborative fall detection approach achieves the best accuracy of 100%.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Mobile Networks and Applications
dc.relation.isbasedon	10.1007/s11036-018-0998-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0999 Other Engineering
dc.subject.classification	Networking & Telecommunications
dc.title	Collaborative Fall Detection Using Smart Phone and Kinect
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0805 Distributed Computing
utslib.for	0999 Other Engineering
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-29T04:57:50Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	4

Abstract:

Humanfall detection has attracted broad attentions as sensors and mobile devices are increasingly adopted in real-life scenarios such as smart homes. The complexity of activities in home environments pose severe challenges to the fall detection research with respect to the detection accuracy. We propose a collaborative detection platform that combines two subsystems: a threshold-based fall detection subsystem using mobile phones and a support vector machine (SVM)-based fall detection subsystem using Kinects. Both subsystems have their respective confidence models and the platform detects falls by fusing the data of both subsystems using two methods: the logical rules-based and D-S evidence fusion theory-based methods. We have validated the two confidence models based on mobile phone and Kinect, which achieve the accuracy of 84.17% and 97.08%, respectively. Our collaborative fall detection approach achieves the best accuracy of 100%.

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http://hdl.handle.net/10453/161020