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

Title	Automatic fall detection for elderly by using features extracted from skeletal data
Author	Davari, Amir, Aydin, T, Erdem, Tanju
Publication Date:	2013
Publication Place	- IEEE
Subject	Fall detection, Event detection
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2-s2.0-84894115180
Record ID	e8efc460-0804-4c53-a604-e49b4b0bcf9d
Library Location	Computer Science
Date	2013
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	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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Özyeğin University

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

Author Davari, Amir, Aydin, T, Erdem, Tanju

Publication Date 2013

Publication Place - IEEE

Subject Fall detection, Event detection

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2-s2.0-84894115180

Record ID e8efc460-0804-4c53-a604-e49b4b0bcf9d

Library Location Computer Science

Date 2013

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

Sample Text 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