A fall detection system using k-nearest neighbor classifier

Chien-Liang Liu*, Chia-Hoang Lee, Ping Min Lin

*此作品的通信作者

研究成果: Article同行評審

150 引文 斯高帕斯(Scopus)

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.

原文American English
頁(從 - 到)7174-7181
頁數8
期刊Expert Systems with Applications
37
發行號10
DOIs
出版狀態Published - 10月 2010

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