A simple strategy for fall events detection

Handle URI:
http://hdl.handle.net/10754/622745
Title:
A simple strategy for fall events detection
Authors:
Harrou, Fouzi; Zerrouki, Nabil; Sun, Ying ( 0000-0001-6703-4270 ) ; Houacine, Amrane
Abstract:
The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart. The MEWMA fall detection scheme has been successfully applied to two publicly available fall detection databases, the UR fall detection dataset (URFD) and the fall detection dataset (FDD). The monitoring strategy developed was able to provide early alert mechanisms in the event of fall situations.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Harrou F, Zerrouki N, Sun Y, Houacine A (2016) A simple strategy for fall events detection. 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). Available: http://dx.doi.org/10.1109/INDIN.2016.7819182.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE 14th International Conference on Industrial Informatics (INDIN)
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
20-Jan-2017
DOI:
10.1109/INDIN.2016.7819182
Type:
Conference Paper
Sponsors:
This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
Additional Links:
http://ieeexplore.ieee.org/document/7819182/
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorZerrouki, Nabilen
dc.contributor.authorSun, Yingen
dc.contributor.authorHouacine, Amraneen
dc.date.accessioned2017-01-29T08:49:33Z-
dc.date.available2017-01-29T08:49:33Z-
dc.date.issued2017-01-20en
dc.identifier.citationHarrou F, Zerrouki N, Sun Y, Houacine A (2016) A simple strategy for fall events detection. 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). Available: http://dx.doi.org/10.1109/INDIN.2016.7819182.en
dc.identifier.doi10.1109/INDIN.2016.7819182en
dc.identifier.urihttp://hdl.handle.net/10754/622745-
dc.description.abstractThe paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart. The MEWMA fall detection scheme has been successfully applied to two publicly available fall detection databases, the UR fall detection dataset (URFD) and the fall detection dataset (FDD). The monitoring strategy developed was able to provide early alert mechanisms in the event of fall situations.en
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7819182/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectFeature extractionen
dc.subjectGravityen
dc.subjectImage segmentationen
dc.subjectMonitoringen
dc.subjectSensorsen
dc.subjectTestingen
dc.subjectVideo sequencesen
dc.titleA simple strategy for fall events detectionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2016 IEEE 14th International Conference on Industrial Informatics (INDIN)en
dc.eprint.versionPost-printen
dc.contributor.institutionUniversity of Sciences and Technology Houari Boum├ędienne Algeria, LCPTS, Faculty of Electronics and Computer Scienceen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
kaust.grant.numberOSR-2015-CRG4-2582en
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