@article{ref1,
title="Posture recognition based fall detection system for monitoring an elderly person in a smart home environment",
journal="IEEE transactions on information technology in biomedicine",
year="2012",
author="Yu, M. and Rhuma, A. and Naqvi, S. and Wang, L. and Chambers, J.",
volume="16",
number="6",
pages="1274-1286",
abstract="We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.<p /> <p>Language: en</p>",
language="en",
issn="1089-7771",
doi="10.1109/TITB.2012.2214786",
url="http://dx.doi.org/10.1109/TITB.2012.2214786"
}