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

Çatalbaş B., Yucesoy B., Secer G., Aslan M.

2014 22nd Signal Processing and Communications Applications Conference, SIU 2014, Trabzon, Turkey, 23 - 25 April 2014, pp.1315-1318 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2014.6830479
  • City: Trabzon
  • Country: Turkey
  • Page Numbers: pp.1315-1318
  • Keywords: accelerometer, fall detection, neural networks, support vector machines
  • Yıldız Technical University Affiliated: Yes


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.