A comparative study of classification methods for fall detection [Düşme tespiti için siniflandirma yöntemlerinin karşilaştirilmasi]
2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
IEEE Computer Society
1315 - 1318
MetadataShow full item record
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27585
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.