Posture recognition based fall detection system for monitoring an elderly person in a smart home environment

Yu, Miao, Rhuma, Adel, Naqvi, Syed Mohsen, Wang, Liang and Chambers, Jonathan (2012) Posture recognition based fall detection system for monitoring an elderly person in a smart home environment. IEEE Transactions on Information Technology in Biomedicine, 16 (6). pp. 1274-1286. ISSN 1089-7771

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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.

Keywords:Health care, assistive living, fall detection, multi-class classi?cation, DAGSVM, system integration
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G120 Applied Mathematics
Divisions:College of Science > School of Computer Science
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ID Code:26775
Deposited On:29 Mar 2017 10:03

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