A smartphone based real-time daily activity monitoring system

Shumei Zhang; Paul McCullagh; Jing Zhang; Tiezhong Yu

doi:10.1007/s10586-013-0335-y

A smartphone based real-time daily activity monitoring system

Shumei Zhang, Paul McCullagh, Jing Zhang, Tiezhong Yu

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20 Citations (Scopus)

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Abstract

A real-time activity monitoring system within anAndroid based smartphone is proposed and evaluated. Motionand motionless postures may be classified using principlesof kinematical theory, which underpins hierarchicalrule-based algorithms, based on accelerometer and orientationdata. Falls detection was implemented by analyzingwhether the postures classified as ‘lying’ or ‘sit-tilted’ postureare deemed normal or abnormal, based on the analysisof time, users’ current position and posture transition. Experimentalresults demonstrate that the approach can detectvarious types of falls efficiently (i.e., in real-time within asmart phone processor) and also correctly (95 % and 93 %true positives for falls ending with ‘lying’ and ‘sit-tilted’ respectively).The approach is reliable for different subjectsand different situations, since it is not only based on empiricalthresholds and subject-based training models, but inaddition it is underpinned by theory.

Original language	English
Pages (from-to)	711-721
Journal	Cluster Computing
Volume	17
Issue number	3
DOIs	https://doi.org/10.1007/s10586-013-0335-y
Publication status	Published (in print/issue) - 1 Sept 2014

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Cite this

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title = "A smartphone based real-time daily activity monitoring system",

abstract = "A real-time activity monitoring system within anAndroid based smartphone is proposed and evaluated. Motionand motionless postures may be classified using principlesof kinematical theory, which underpins hierarchicalrule-based algorithms, based on accelerometer and orientationdata. Falls detection was implemented by analyzingwhether the postures classified as {\textquoteleft}lying{\textquoteright} or {\textquoteleft}sit-tilted{\textquoteright} postureare deemed normal or abnormal, based on the analysisof time, users{\textquoteright} current position and posture transition. Experimentalresults demonstrate that the approach can detectvarious types of falls efficiently (i.e., in real-time within asmart phone processor) and also correctly (95 % and 93 %true positives for falls ending with {\textquoteleft}lying{\textquoteright} and {\textquoteleft}sit-tilted{\textquoteright} respectively).The approach is reliable for different subjectsand different situations, since it is not only based on empiricalthresholds and subject-based training models, but inaddition it is underpinned by theory.",

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AU - Zhang, Shumei

AU - McCullagh, Paul

AU - Zhang, Jing

AU - Yu, Tiezhong

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A smartphone based real-time daily activity monitoring system

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