Davari, AmirAydin, TErdem, Tanju2016-06-302016-06-302013http://hdl.handle.net/10679/4241https://doi.org/10.1109/ICECCO.2013.6718245Due to copyright restrictions, the access to the full text of this article is only available via subscription.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.engrestrictedAccessconferenceObject12713000033661650003310.1109/ICECCO.2013.6718245Fall detectionEvent detection2-s2.0-84894115180