@inproceedings{fe531fabc87741d381acccab74582b0f,
title = "Automatic fall detection for elderly by using features extracted from skeletal data",
abstract = "Automatic detection of unusual events such as falls is very important especially for elderly people living alone. Realtime detection of these events can reduce the health risks associated with a fall. In this paper, we propose a novel method for automatic detection of fall event by using depth cameras. Depth images generated by these cameras are used in computing the skeletal data of a person. Our contribution is to use features extracted from the skeletal data to form a strong set of features which can help us achieve an increased precision at low redundancy. Our findings indicate that our features, which are derived from skeletal data, are moderately powerful for detecting unusual events such as fall.",
keywords = "event detection, fall detection",
author = "Amir Davari and Tarkan Aydin and Tanju Erdem",
year = "2013",
doi = "10.1109/ICECCO.2013.6718245",
language = "English",
isbn = "9781479933433",
series = "2013 International Conference on Electronics, Computer and Computation, ICECCO 2013",
publisher = "IEEE Computer Society",
pages = "127--130",
booktitle = "2013 International Conference on Electronics, Computer and Computation, ICECCO 2013",
note = "2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 ; Conference date: 07-11-2013 Through 08-11-2013",
}