NDLI: Deep learning for posture analysis in fall detection

Content Provider	IEEE Xplore Digital Library
Author	Pengming Feng Miao Yu Naqvi, S.M. Chambers, J.A.
Copyright Year	2014
Description	Author affiliation: Electron. & Electr. Eng. Dept., Loughborough Univ., Loughborough, UK (Pengming Feng; Miao Yu; Naqvi, S.M.; Chambers, J.A.)
Abstract	We propose a novel computer vision based fall detection system using deep learning methods to analyse the postures in a smart home environment for detecting fall activities. Firstly, background subtraction is employed to extract the foreground human body. Then the binary human body images form the input to the classifier. Two deep learning approaches based on a Boltzmann machine and deep belief network are compared with a support vector machine approach. The final decision on the occurrence of a fall is made on the basis of combining the classifier output with certain contextual rules. Evaluations are performed on recordings from a real home care environment, in which 15 people create 2904 postures.
Starting Page	12
Ending Page	17
File Size	1285924
Page Count	6
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900806
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Digital signal processing Signal processing algorithms Training Feature extraction Cameras Senior citizens Learning systems multiclass classification Fall detection deep learning Boltzmann machine deep belief network support vector machine (SV M)
Content Type	Text
Resource Type	Article

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