期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2014
卷号：3
期号：8
页码：2820-2826
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Falling is known as a major health risk to cause injuries and even death among seniors. An early fall detection system is therefore extremely important to reduce the serious consequences of fall. In this study we aim to design and implement a practical three-stage video-based system for detecting fall events in elderly living alone at home. A common camera is used in the first stage to capture the video of a person doing his/her daily activities and then transfer to the following processing unit. In the second stage, from each frame of the video, we first separate the interested person from the background using adaptive background Gaussian Mixture Model; then the extracted object is converted into a five-dimensional feature vector using ellipse model; and finally we analyze those extracted features to recognize a fall using Hidden Markov Model trained by a challenging stimulated fall/non-fall database. The final stage is to immediately convey an SMS alert message to the assigned phone number to ask the timely medical assistance as soon as fall detected. Experimental result through the real-life video captured within one month in a crowded public area gives the average fall recognition rate of 97.47% with the delay time from 1 to 5 seconds, providing the high applicability of the system in real world.
关键词：Fall detection; Gaussian Mixture Model ; (GMM); ellipse model; Hidden Markov Model (HMM).
