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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorZerrouki, Nabil
dc.contributor.authorSun, Ying
dc.contributor.authorHouacine, Amrane
dc.date.accessioned2017-01-29T08:49:33Z
dc.date.available2017-01-29T08:49:33Z
dc.date.issued2017-01-20
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.
dc.identifier.doi10.1109/INDIN.2016.7819182
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.
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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7819182/
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.
dc.subjectFeature extraction
dc.subjectGravity
dc.subjectImage segmentation
dc.subjectMonitoring
dc.subjectSensors
dc.subjectTesting
dc.subjectVideo sequences
dc.titleA simple strategy for fall events detection
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journal2016 IEEE 14th International Conference on Industrial Informatics (INDIN)
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Sciences and Technology Houari Boumédienne Algeria, LCPTS, Faculty of Electronics and Computer Science
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2018-06-13T16:50:32Z


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