Novel Video Sensor Based Fall Detection of the Elderly Using Double-Difference Image and Temporal Templates
In recent years, fall incident detection is one of the major health care issues because unexpected falls cause serious injuries in elderly people. Several related studies based on video sensor have tried to detect falls, however, their methods offer low fall detection rates in general. To overcome this problem, we propose a novel approach to detect falls using a weighted subtraction between consecutive difference images and a motion history image on temporal templates in real time. As a result, the proposed algorithm obtains the successful rate of 96% even though the video sequence is obtained by an USB PC camera sensor. In addition, the sensitivity and specificity of our system are 95% and 97.2%, respectively. Experimental result also shows advanced reliability to discriminate rapid sitting down from an abrupt fall which is most difficult to detect. Therefore, Our PC camera sensor-based algorithm could be used for fall detection and activity monitoring in elderly people with high reliability and resolution.
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Document Type: Short Communication
Publication date: 2008-04-01
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