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    Vision-based fall detection system for improving safety of elderly people

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    IEEEIMM paper.pdf
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    Type
    Article
    Authors
    Harrou, Fouzi cc
    Zerrouki, Nabil
    Sun, Ying cc
    Houacine, Amrane
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2017-12-06
    Online Publication Date
    2017-12-06
    Print Publication Date
    2017-12
    Permanent link to this record
    http://hdl.handle.net/10754/626355
    
    Metadata
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    Abstract
    Recognition 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.
    Citation
    Harrou 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.
    Sponsors
    The 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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Instrumentation & Measurement Magazine
    DOI
    10.1109/mim.2017.8121952
    Additional Links
    http://ieeexplore.ieee.org/document/8121952/
    ae974a485f413a2113503eed53cd6c53
    10.1109/mim.2017.8121952
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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