A Study for Adapting the Monitoring System in Order to Prevent Fall Down from a Bed

  • Hironobu SatohEmail author
  • Kyoko Shibata
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 876)


In hospitals, older people often fall down from a bed. This accident causes a decline in the quality of life of due to an injury. Therefore, the researchers develop a monitoring system which avoid falling down from a bed with Deep Belief Network. However, the proposed monitoring system is not able to individual differences. The proposed is a new learning method to adapt the proposed system for individual difference of behaviors. An experiment was conducted to verify the effectiveness of the proposed learning method. From the experimental result, the proposed learning method has the ability of adapting the proposed system to the individual difference of a behavior.


Awaking behavior Monitoring system Deep Learning Kinect 


  1. 1.
    Ikeda, R., Satoh, H., Takeda, F.: Development of awaking behavior detection system nursing inside the house. In: International Conference on Intelligent Technology, pp. 65–70 (2006)Google Scholar
  2. 2.
    Satoh, H., Takeda, F., Shiraishi, Y., Ikeda, R.: Development of a awaking behavior detection system using a neural network. IEEJ Trans. EIS 128(11), 1649–1656 (2008)CrossRefGoogle Scholar
  3. 3.
    Yamanaka, N., Satoh, H., Shiraishi, Y., Matsubara, T., Takeda, F.: Proposal of the awakening detection system using neural network and it’s verification. In: The 52nd the Institute of Systems, Control and Information Engineers (2008)Google Scholar
  4. 4.
    Matubara, T., Satoh, H., Takeda, F.: Proposal of an awaking detection system adopting neural network in hospital use. In: World Automation Congress (2008)Google Scholar
  5. 5.
    Satoh, H., Takeda, F.: Verification of the effectiveness of the online tuning system for unknown person in the awaking behavior detection system. In: Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, IWANN 2009, pp. 272–279 (2009)CrossRefGoogle Scholar
  6. 6.
    Satoh, H., Shibata, K., Masaki, S.: Development of an awaking behavior detection system with Kinect. In: HCI International 2014 - Poster’s Extended Abstracts, Proceedings, Part II, pp. 496–500, pp. 272–279 (2014)CrossRefGoogle Scholar
  7. 7.
    Satoh, H., Shibata, K.: Development of human behavior recognition for avoiding fall down from a bed by deep learning. In: International Conference on Brain Informatics & Health (2017)Google Scholar
  8. 8.
    Yoshua, B., Pascal, L., Dan, P., Hugo, L.: Greedy layer-wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153–160 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Institute of Information and Communications TechnologyKoganeiJapan
  2. 2.Kochi University of TechnologyKamiJapan

Personalised recommendations