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, Trabzon, Türkiye, 23 - 25 Nisan 2014, ss.1315-1318, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu.2014.6830479
- Basıldığı Şehir: Trabzon
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.1315-1318
- Anahtar Kelimeler: accelerometer, fall detection, neural networks, support vector machines
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
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.