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

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Date Issued
2014Author
Catalbas, B.
Yucesoy, B.
Secer G.
Aslan, M.
Please cite this item using this persistent URL
http://hdl.handle.net/11693/27585Journal
2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
Published as
http://dx.doi.org/10.1109/SIU.2014.6830479Collections
- 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.