Recognizing Falls from Silhouettes

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Recognizing Falls from Silhouettes

Please use this identifier to cite or link to this item: http://hdl.handle.net/10355/9705

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Title: Recognizing Falls from Silhouettes
Author: Anderson, Derek T., 1979-; Keller, James M.; Skubic, Marge; Chen, Xi; He, Zhihai, 1973-
Date: 2006-08
Publisher: IEEE
Citation: Anderson D, Keller J, Skubic M, Chen X & He Z, "Recognizing Falls From Silhouettes," Proceedings, IEEE 28th Annual International Conference of the Engineering in Medicine and Biology Society, New York, NY, August 30-September 3, 2006, p 6388-6391.
Abstract: A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind.
URI: http://hdl.handle.net/10355/9705
ISSN: 1-4244-0033-3/06/$20.00

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