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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/13018

Title: A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment
Authors: Yu, Miao
Rhuma, Adel
Naqvi, Syed M.
Wang, Liang
Chambers, Jonathon
Keywords: Assistive living
Directed acyclic graph support vector machine (DAGSVM) system integration
Fall detection
Health care
Multiclass classification
Issue Date: 2012
Publisher: © IEEE
Citation: YU, M. ... et al., 2012. A 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.
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 postprocessing. Information from ellipse fitting and a projection histogram along the axes of the ellipse is used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine 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.
Description: This article was published in the IEEE Transactions on Information Technology in Biomedicine [© IEEE] and the definitive version is available at: http://dx.doi.org/10.1109/TITB.2012.2214786
Version: Accepted for publication
DOI: 10.1109/TITB.2012.2214786
URI: https://dspace.lboro.ac.uk/2134/13018
Publisher Link: http://dx.doi.org/10.1109/TITB.2012.2214786
ISSN: 1089-7771
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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