Automatic fall detection for elderly by using features extracted from skeletal data

Name: Automatic fall detection for elderly by using features extracted from skeletal data
Author: Davari, Amir, Aydin, T, Erdem, Tanju

İsim	Automatic fall detection for elderly by using features extracted from skeletal data
Yazar	Davari, Amir, Aydin, T, Erdem, Tanju
Basım Tarihi:	2013
Basım Yeri	- IEEE
Konu	Fall detection, Event detection
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2-s2.0-84894115180
Kayıt Numarası	e8efc460-0804-4c53-a604-e49b4b0bcf9d
Lokasyon	Computer Science
Tarih	2013
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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.
DOI	10.1109/ICECCO.2013.6718245

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Automatic fall detection for elderly by using features extracted from skeletal data

Yazar Davari, Amir, Aydin, T, Erdem, Tanju

Basım Tarihi 2013

Basım Yeri - IEEE

Konu Fall detection, Event detection

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2-s2.0-84894115180

Kayıt Numarası e8efc460-0804-4c53-a604-e49b4b0bcf9d

Lokasyon Computer Science

Tarih 2013

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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.

DOI 10.1109/ICECCO.2013.6718245