A Data-Driven Monitoring Technique for Enhanced Fall Events Detection

Handle URI:
http://hdl.handle.net/10754/620948
Title:
A Data-Driven Monitoring Technique for Enhanced Fall Events Detection
Authors:
Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 ) ; Houacine, Amrane
Abstract:
Fall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Zerrouki N, Harrou F, Sun Y, Houacine A (2016) A Data-Driven Monitoring Technique for Enhanced Fall Events Detection. IFAC-PapersOnLine 49: 333–338. Available: http://dx.doi.org/10.1016/j.ifacol.2016.07.135.
Publisher:
Elsevier BV
Journal:
IFAC-PapersOnLine
KAUST Grant Number:
OSR-2015-CRG4-258
Conference/Event name:
4th IFAC Conference on Intelligent Control and Automation SciencesICONS 2016
Issue Date:
26-Jul-2016
DOI:
10.1016/j.ifacol.2016.07.135
Type:
Conference Paper
ISSN:
2405-8963
Sponsors:
This publication is based upon work supported by the King Ab-dullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No:OSR-2015-CRG4-2582.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S2405896316303408
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorZerrouki, Nabilen
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.contributor.authorHouacine, Amraneen
dc.date.accessioned2016-10-12T09:15:30Z-
dc.date.available2016-10-12T09:15:30Z-
dc.date.issued2016-07-26en
dc.identifier.citationZerrouki N, Harrou F, Sun Y, Houacine A (2016) A Data-Driven Monitoring Technique for Enhanced Fall Events Detection. IFAC-PapersOnLine 49: 333–338. Available: http://dx.doi.org/10.1016/j.ifacol.2016.07.135.en
dc.identifier.issn2405-8963en
dc.identifier.doi10.1016/j.ifacol.2016.07.135en
dc.identifier.urihttp://hdl.handle.net/10754/620948-
dc.description.abstractFall detection is a crucial issue in the health care of seniors. In this work, we propose an innovative method for detecting falls via a simple human body descriptors. The extracted features are discriminative enough to describe human postures and not too computationally complex to allow a fast processing. The fall detection is addressed as a statistical anomaly detection problem. The proposed approach combines modeling using principal component analysis modeling with the exponentially weighted moving average (EWMA) monitoring chart. The EWMA scheme is applied on the ignored principal components to detect the presence of falls. Using two different fall detection datasets, URFD and FDD, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional PCA-based methods.en
dc.description.sponsorshipThis publication is based upon work supported by the King Ab-dullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No:OSR-2015-CRG4-2582.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S2405896316303408en
dc.subjectFall detectionen
dc.subjectDimensionality reductionen
dc.subjectSPC chartsen
dc.subjectVisual surveillanceen
dc.subjectImage processingen
dc.titleA Data-Driven Monitoring Technique for Enhanced Fall Events Detectionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIFAC-PapersOnLineen
dc.conference.date1–3 June 2016en
dc.conference.name4th IFAC Conference on Intelligent Control and Automation SciencesICONS 2016en
dc.conference.locationReims, Franceen
dc.contributor.institutionLCPTS, Faculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne (USTHB) Algiers, Algeriaen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-258en
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