article

Fall detection based on the gravity vector using a wide-angle camera

Authors:

Marc Bosch-Jorge,

Antonio-José Sánchez-Salmerón,

Carlos Ricolfe-VialaAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 41, Issue 17

Pages 7980 - 7986

https://doi.org/10.1016/j.eswa.2014.06.045

Published: 01 December 2014 Publication History

Abstract

Falls in elderly people are becoming an increasing healthcare problem, since life expectancy and the number of elderly people who live alone have increased over recent decades. If fall detection systems could be installed easily and economically in homes, telecare could be provided to alleviate this problem. In this paper we propose a low cost fall detection system based on a single wide-angle camera. Wide-angle cameras are used to reduce the number of cameras required for monitoring large areas. Using a calibrated video system, two new features based on the gravity vector are introduced for fall detection. These features are: angle between the gravity vector and the line from feet to head of the human and size of the upper body. Additionally, to differentiate between fall events and controlled lying down events the speed of changes in the features is also measured. Our experiments demonstrate that our system is 97% accurate for fall detection.

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Cited By

Li SMan CShen AGuan ZMao WLuo SZhang RYu H(2022)A Fall Detection Network by 2D/3D Spatio-temporal Joint Models with Tensor Compression on EdgeACM Transactions on Embedded Computing Systems10.1145/353100421:6(1-19)Online publication date: 12-Dec-2022
https://dl.acm.org/doi/10.1145/3531004
Fan KWang PZhuang S(2019)Human fall detection using slow feature analysisMultimedia Tools and Applications10.1007/s11042-018-5638-978:7(9101-9128)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11042-018-5638-9
Urbauer PFrohner MDavid VSauermann S(2018)Wearable Activity Trackers Supporting Elderly Living IndependentlyProceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion10.1145/3218585.3218679(302-309)Online publication date: 20-Jun-2018
https://dl.acm.org/doi/10.1145/3218585.3218679
Show More Cited By

Fall detection based on the gravity vector using a wide-angle camera
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 41, Issue 17

December, 2014

357 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd © 2014.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 December 2014

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Cited By

Li SMan CShen AGuan ZMao WLuo SZhang RYu H(2022)A Fall Detection Network by 2D/3D Spatio-temporal Joint Models with Tensor Compression on EdgeACM Transactions on Embedded Computing Systems10.1145/353100421:6(1-19)Online publication date: 12-Dec-2022
https://dl.acm.org/doi/10.1145/3531004
Fan KWang PZhuang S(2019)Human fall detection using slow feature analysisMultimedia Tools and Applications10.1007/s11042-018-5638-978:7(9101-9128)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11042-018-5638-9
Urbauer PFrohner MDavid VSauermann S(2018)Wearable Activity Trackers Supporting Elderly Living IndependentlyProceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion10.1145/3218585.3218679(302-309)Online publication date: 20-Jun-2018
https://dl.acm.org/doi/10.1145/3218585.3218679
Min WYao LLin ZLiu L(2018)Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angleIET Computer Vision10.1049/iet-cvi.2018.532412:8(1133-1140)Online publication date: 30-Jul-2018
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5324
Lu NRen XSong JWu Y(2017)Visual guided deep learning scheme for fall detection2017 13th IEEE Conference on Automation Science and Engineering (CASE)10.1109/COASE.2017.8256202(801-806)Online publication date: 20-Aug-2017
https://dl.acm.org/doi/10.1109/COASE.2017.8256202
Madarshahian RCaicedo JArocha Zambrana D(2016)Benchmark problem for human activity identification using floor vibrationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.06.02762:C(263-272)Online publication date: 15-Nov-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.06.027
Principi ESquartini SBonfigli RFerroni GPiazza F(2015)An integrated system for voice command recognition and emergency detection based on audio signalsExpert Systems with Applications: An International Journal10.1016/j.eswa.2015.02.03642:13(5668-5683)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.eswa.2015.02.036

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