標題: A fall detection system using k-nearest neighbor classifier
作者: Liu, Chien-Liang
Lee, Chia-Hoang
Lin, Ping-Min
資訊工程學系
Department of Computer Science
關鍵字: Artificial intelligence;Machine learning;kNN;Fall detection
公開日期: 1-Oct-2010
摘要: The main purpose of this paper is to use off-the-shelf devices to develop a fall detection system. In human body identification, human body silhouette is adopted to improve privacy protection, and vertical projection histograms of the silhouette image and statistical scheme are used to reduce the effect of human body upper limb activities. The kNN classification algorithm is used to classify the postures using the ratio and difference of human body silhouette bounding box height and width. Meanwhile, since time difference is a vital factor to differentiate fall incident event and lying down event, the critical time difference is obtained from the experiment and verified by statistical hypothesis testing. With the help of the kNN classifier and the critical time difference, a fall incident detection system is developed to detect fall incident events. The experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection. (C) 2010 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2010.04.014
http://hdl.handle.net/11536/32136
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2010.04.014
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 37
Issue: 10
起始頁: 7174
結束頁: 7181
Appears in Collections:Articles


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