Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
Matubara, T., Satoh, H., Takeda, F.: Proposal of an awaking detection system adopting neural network in hospital use. In: World Automation Congress (2008)
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)
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)
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)
Yoshua, B., Pascal, L., Dan, P., Hugo, L.: Greedy layer-wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153–160 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Satoh, H., Shibata, K. (2019). A Study for Adapting the Monitoring System in Order to Prevent Fall Down from a Bed. In: Ahram, T., Karwowski, W., Taiar, R. (eds) Human Systems Engineering and Design. IHSED 2018. Advances in Intelligent Systems and Computing, vol 876. Springer, Cham. https://doi.org/10.1007/978-3-030-02053-8_154
Download citation
DOI: https://doi.org/10.1007/978-3-030-02053-8_154
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02052-1
Online ISBN: 978-3-030-02053-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)