Show simple item record

dc.contributor.authorHarrou, Fouzi
dc.contributor.authorZerrouki, Nabil
dc.contributor.authorSun, Ying
dc.contributor.authorHouacine, Amrane
dc.date.accessioned2017-12-12T08:06:50Z
dc.date.available2017-12-12T08:06:50Z
dc.date.issued2017-12-06
dc.identifier.citationHarrou F, Zerrouki N, Sun Y, Houacine A (2017) Vision-based fall detection system for improving safety of elderly people. IEEE Instrumentation & Measurement Magazine 20: 49–55. Available: http://dx.doi.org/10.1109/mim.2017.8121952.
dc.identifier.issn1094-6969
dc.identifier.doi10.1109/mim.2017.8121952
dc.identifier.urihttp://hdl.handle.net/10754/626355
dc.description.abstractRecognition of human movements is very useful for several applications, such as smart rooms, interactive virtual reality systems, human detection and environment modeling. The objective of this work focuses on the detection and classification of falls based on variations in human silhouette shape, a key challenge in computer vision. Falls are a major health concern, specifically for the elderly. In this study, the detection is achieved with a multivariate exponentially weighted moving average (MEWMA) monitoring scheme, which is effective in detecting falls because it is sensitive to small changes. Unfortunately, an MEWMA statistic fails to differentiate real falls from some fall-like gestures. To remedy this limitation, a classification stage based on a support vector machine (SVM) is applied on detected sequences. To validate this methodology, two fall detection datasets have been tested: the University of Rzeszow fall detection dataset (URFD) and the fall detection dataset (FDD). The results of the MEWMA-based SVM are compared with three other classifiers: neural network (NN), naïve Bayes and K-nearest neighbor (KNN). These results show the capability of the developed strategy to distinguish fall events, suggesting that it can raise an early alert in the fall incidents.
dc.description.sponsorshipThe research reported in this publication was supported by funding from 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/8121952/
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.titleVision-based fall detection system for improving safety of elderly people
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalIEEE Instrumentation & Measurement Magazine
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Sciences and Technology Houari Boumédienne (USTHB), LCPTS, Faculty of Electronics and Computer Science, Algiers, Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2018-06-13T13:08:58Z
dc.date.published-online2017-12-06
dc.date.published-print2017-12


Files in this item

Thumbnail
Name:
IEEEIMM paper.pdf
Size:
1.016Mb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record