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dc.contributor.authorZerrouki, Nabil
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorHouacine, Amrane
dc.date.accessioned2017-01-02T08:42:35Z
dc.date.available2017-01-02T08:42:35Z
dc.date.issued2016-10-29
dc.identifier.citationZerrouki N, Harrou F, Sun Y, Houacine A (2016) Accelerometer and Camera-Based Strategy for Improved Human Fall Detection. Journal of Medical Systems 40. Available: http://dx.doi.org/10.1007/s10916-016-0639-6.
dc.identifier.issn0148-5598
dc.identifier.issn1573-689X
dc.identifier.doi10.1007/s10916-016-0639-6
dc.identifier.urihttp://hdl.handle.net/10754/622168
dc.description.abstractIn this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow’s. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.
dc.description.sponsorshipWe would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality. The authors (Nabil Zerrouki and Amrane Houacine) would like to thank the LCPTS laboratory, Faculty of Electronics and Informatics, University of Sciences and Technology HOUARI BOUMEDIENE (USTHB) for the continued support during the research. This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007%2Fs10916-016-0639-6
dc.subjectFall detection and classification
dc.subjectVisiosurvaillance
dc.subjectTri axial accelerometer
dc.subjectAnomaly detection
dc.subjectSupport vector machine
dc.titleAccelerometer and Camera-Based Strategy for Improved Human Fall Detection
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalJournal of Medical Systems
dc.contributor.institutionLCPTS, Faculty of Electronics and Computer ScienceUniversity of Sciences and Technology Houari Boumédienne (USTHB)AlgiersAlgeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582.


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