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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), DOAJ, Electronic Journals Library, Engineering & Technology Digital Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2013 Vol.1(4): 350-355 ISSN: 2301-3559
DOI: 10.7763/LNSE.2013.V1.75

Posture Recognition Based Fall Detection System

Adel Rhuma, Miao Yu, and Jonathon Chambers
Abstract—In this paper, we introduce a video-based fall detection system for an elderly person in a realhome environment. We extract global (ellipse) and local (shape context) features from static postures and an improved Directed Acyclic Graphic Support Vector Machine (DAGSVM) is applied for posture classification. After classifying different postures, certain rules are set to detect falls. This fall detection system is shown by evaluation on real datasets to achieve a good fall detection performance.

Index Terms—Posture classification, improved (DAGSVM), shape context, fall detection.

The authors are with Advanced Signal Processing Group, School of Electronic, Electrical and Systems Engineering, Loughborough University, UK (e-mail: A.Rhuma@lboro.ac.uk, M. Yu@lboro.ac.uk, J. A. Chambers@lboro.ac.uk).


Cite: Adel Rhuma, Miao Yu, and Jonathon Chambers, "Posture Recognition Based Fall Detection System," Lecture Notes on Software Engineering vol. 1, no. 4, pp. 350-355, 2013.

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