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    A comparative study of classification methods for fall detection [Düşme tespiti için siniflandirma yöntemlerinin karşilaştirilmasi]

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    Date Issued
    2014
    Author
    Catalbas, B.
    Yucesoy, B.
    Secer G.
    Aslan, M.
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    Please cite this item using this persistent URL
    http://hdl.handle.net/11693/27585
    Journal
    2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
    Published as
    http://dx.doi.org/10.1109/SIU.2014.6830479
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    • Conference Paper [2294]
    Publisher
    IEEE Computer Society
    Abstract
    A comparative study of various fall detection algorithms based upon measurements of a wearable tri-axial accelerometer unit is presented in this paper. Least squares support vector machine, neural network and rule-based classifiers are evaluated in the scope of this paper. Training and testing data sets, which are necessary for design and testing of the classifiers, respectively, are collected from 7 people. Each subject exercised simulated falls and other daily life activities such as walking, sitting etc. Among three methods, support vector machine-based classifier is found to be superior in terms of both correct detection and false alarm ratio as 87,76% precision and 89.47% specifity. Meanwhile, best sensitivity is achieved with rule-based classifiers. © 2014 IEEE.

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    BİLKENT UNIVERSITY

    Copyright © Bilkent University - Library Technical Services | 06800 Bilkent, Ankara TURKEY
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