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Wireless Communications and Mobile Computing
Volume 2017, Article ID 9474806, 16 pages
https://doi.org/10.1155/2017/9474806
Research Article

Vision-Based Fall Detection with Convolutional Neural Networks

1DeustoTech, University of Deusto, Avenida de las Universidades, No. 24, 48007 Bilbao, Spain
2Department of Computer Science and Artificial Intelligence, Basque Country University, P. Manuel Lardizabal 1, 20018 San Sebastian, Spain
3Ikerbasque, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain
4Donostia International Physics Center (DIPC), P. Manuel Lardizabal 4, 20018 San Sebastian, Spain

Correspondence should be addressed to Adrián Núñez-Marcos; se.otsued@zenun.nairda

Received 14 July 2017; Revised 26 September 2017; Accepted 9 November 2017; Published 6 December 2017

Academic Editor: Wiebren Zijlstra

Copyright © 2017 Adrián Núñez-Marcos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [16 citations]

The following is the list of published articles that have cited the current article.

  • Yu Gu, Yifan Zhang, Mengmeng Huang, and Fuji Ren, “Your WiFi Knows You Fall: A Channel Data-driven Device-free Fall Sensing System,” 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 943–947, . View at Publisher · View at Google Scholar
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  • Sarah Almeida Cameiro, Gabriel Pellegrino da Silva, Guilherme Vieira Leite, Ricardo Moreno, Silvio Jamil F. Guimaraes, and Helio Pedrini, “Multi-Stream Deep Convolutional Network Using High-Level Features Applied to Fall Detection in Video Sequences,” 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 293–298, . View at Publisher · View at Google Scholar
  • Ying Sun, Nabil Zerrouki, Fouzi Harrou, and Amrane Houacine, “Vision-Based Human Action Classification Using Adaptive Boosting Algorithm,” IEEE Sensors Journal, vol. 18, no. 12, pp. 5115–5121, 2018. View at Publisher · View at Google Scholar
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  • Cristi Iuga, Paul Drăgan, and Lucian Bușoniu, “Fall monitoring and detection for at-risk persons using a UAV,” IFAC-PapersOnLine, vol. 51, no. 10, pp. 199–204, 2018. View at Publisher · View at Google Scholar
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  • Yujia Zheng, Siyi Liu, Zairong Wang, and Yunbo Rao, “ReFall: Real-Time Fall Detection of Continuous Depth Maps with RFD-Net,” Image and Graphics Technologies and Applications, vol. 1043, pp. 659–673, 2019. View at Publisher · View at Google Scholar
  • Shabnam Ezatzadeh, and Mohammad Reza Keyvanpour, “ViFa: an analytical framework for vision-based fall detection in a surveillance environment,” Multimedia Tools and Applications, 2019. View at Publisher · View at Google Scholar
  • Lourdes Martínez-Villaseñor, Hiram Ponce, Jorge Brieva, Ernesto Moya-Albor, José Núñez-Martínez, and Carlos Peñafort-Asturiano, “UP-Fall Detection Dataset: A Multimodal Approach,” Sensors, vol. 19, no. 9, pp. 1988, 2019. View at Publisher · View at Google Scholar
  • Glenn Forbes, Stewart Massie, and Susan Craw, “Fall prediction using behavioural modelling from sensor data in smart homes,” Artificial Intelligence Review, 2019. View at Publisher · View at Google Scholar
  • Na Lu, Li Feng, Jinbo Song, and Yidan Wu, “Deep learning for fall detection: Three-dimensional CNN Combined with LSTM on video kinematic data,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 1, pp. 314–323, 2019. View at Publisher · View at Google Scholar
  • Yan Zhang, and Heiko Neumann, “An Empirical Study Towards Understanding How Deep Convolutional Nets Recognize Falls,” Computer Vision – ECCV 2018 Workshops, vol. 11134, pp. 112–127, 2019. View at Publisher · View at Google Scholar