@inproceedings{7984502e321447c39aa1b06db291aa59,
title = "Dalgacik d{\"o}n{\"u}s{\"u}m{\"u}ne dayali d{\"u}sme sezme",
abstract = "Falls are identified as a major health risk not only for the elderly but also for people with cognitive diseases and are considered as a major obstacle to independent living. Fast detection of falls would not only decrease the health risks by enabling quick medical response; but also make independent living a safe option for the elderly. In this paper, we propose a Wavelet Transform based fall detector using wearable accelerometers, and we explain the experiments we have conducted in order to observe the effects of several factors, such as fall properties, sensor platform and the selection of mother wavelet, on the fall detection performance. Our experimental results indicate that the wavelet transform based fall detection approach is robust with high fall detection performance.",
author = "Yavuz, \{G{\"o}khan Remzi\} and H{\"u}lya Yal{\c c}in and Lale Akarun and Cem Ersoy",
year = "2011",
doi = "10.1109/SIU.2011.5929604",
language = "T{\"u}rk{\c c}e",
isbn = "9781457704635",
series = "2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011",
pages = "142--145",
booktitle = "2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011",
note = "2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 ; Conference date: 20-04-2011 Through 22-04-2011",
}