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A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment

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posted on 23.08.2013 by Miao Yu, Adel Rhuma, Mohsen Naqvi, Liang Wang, Jonathon Chambers
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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

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.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2012

Notes

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

ISSN

1089-7771

Language

en

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