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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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5 Author(s)
Miao Yu ; Adv. Signal Process. Group, Loughborough Univ., Loughborough, UK ; Rhuma, A. ; Naqvi, S.M. ; Liang Wang
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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.

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Information Technology in Biomedicine, IEEE Transactions on  (Volume:16 ,  Issue: 6 )