JACIII Vol.23 No.3 pp. 528-535
doi: 10.20965/jaciii.2019.p0528


A Fuzzy Inference-Based Spiking Neural Network for Behavior Estimation in Elderly Health Care System

Shuai Shao and Naoyuki Kubota

Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

November 29, 2018
December 27, 2018
May 20, 2019
population aging, sensor network, information structure space, spiking neural network, fuzzy inference system

In recent years, population aging has become an important social issue. We hope to achieve an elderly health care system through technical means. In this study, we developed an elderly health care system. We chose to use environmental sensors to estimate the behavior of older adults. We found that traditional methods have difficulty solving the problem of excessive indoor environmental differences in different households. Therefore, we provide a fuzzy spike neural network. By modifying the sensitivity of input using a fuzzy inference system, we can solve the problem without additional training. In the experiment, we used temperature and humidity data to make an estimation of behavior in the bathroom. The results show that the system can estimate behavior with 97% accuracy and 78% sensitivity.

Behavior estimation system in bathroom

Behavior estimation system in bathroom

Cite this article as:
S. Shao and N. Kubota, “A Fuzzy Inference-Based Spiking Neural Network for Behavior Estimation in Elderly Health Care System,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.3, pp. 528-535, 2019.
Data files:
  1. [1] Cabinet Office, Aged Society White Paper (whole edition), 2017, [accessed April 19, 2019]
  2. [2] J. Nobel, “Japan: ‘Lonely Deaths’ Rise Among Unemployed, Elderly,” TIME, 2010.
  3. [3] N. Yoshioka, T. Chiba, M. Yamauchi, T. Monma, and K. Yoshizaki, “Forensic consideration of death in the bathtub,” Legal Medicine, Vol.5, Suppl. 1, S375-S38, 2003.
  4. [4] B. Zhang, “Health care applications based on ZigBee standard,” 2010 Int. Conf. On Computer Design and Applications, 2010.
  5. [5] J. K. Wu, L. Dong, and W. Xiao, “Real-time Physical Activity classification and tracking using wearble sensors,” 2007 6th Int. Conf. on Information, Communications and Signal Processing, 2007.
  6. [6] Y. Hong, I. Kim, S. Ahn, and H. Kim, “Activity Recognition Using Wearable Sensors for Elder Care,” 2008 2nd Int. Conf. on Future Generation Communication and Networking, 2008.
  7. [7] A. Nasution and S. Emmanuel, “Intelligent Video Surveillance for Monitoring Elderly in Home Environments,” 2007 IEEE 9th Workshop on Multimedia Signal Processing, 2007.
  8. [8] G. Blumrosen, Y. Miron, N. Intrator, and M. Plotnik, “A Real-Time Kinect Signature-Based Patient Home Monitoring System,” Sensors, Vol.16, No.11, p. 1965, 2016.
  9. [9] N. Suryadevara and S. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly,” IEEE Sensors J., Vol.12, No.6, pp. 1965-1972, 2012.
  10. [10] T. Obo, N. Kubota, and B. Lee, “Localization of human in informationally structured space based on sensor networks,” Int. Conf. on Fuzzy Systems, 2010.
  11. [11] N. Kubotal and Y. Shimomura, “Human-Friendly Networked Partner Robots toward Sophisticated Services for A Community,” 2006 SICE-ICASE Int. Joint Conf., 2006.
  12. [12] C. Cordeiro and D. Agrawal, “Ad hoc & sensor networks,” World Scientific, 2006.
  13. [13] I. Khemapech, I. Duncan, and A. Miller, “A Survey of Wireless Sensor Networks Technology,” Proc. of The 6th Annual Post Graduate Symp. on The Convergence of Telecommunications, Networking and Broadcasting, 2005.
  14. [14] J. Lu, A. Van Den Bossche, and E. Campo, “An IEEE 802.15.4 Based Adaptive Communication Protocol in Wireless Sensor Network: Application to Monitoring the Elderly at Home,” Wireless Sensor Network, Vol.6, No.9, pp. 192-204, 2014.
  15. [15] M. Satomi, H. Masuta, and N. Kubota, “Hierarchical growing neural gas for information structured space,” 2009 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, 2009.
  16. [16] D. Tang, Y. Yoshihara, T. Obo, T. Takeda, J. Botzheim, and N. Kubota, “Evolution strategy for anomaly detection in daily life monitoring of elderly people,” 2016 55th Annual Conf. of the Society of Instrument and Control Engineers of Japan (SICE), 2016.
  17. [17] A. Sixsmith and N. Johnson, “A smart sensor to detect the falls of the elderly,” IEEE Pervasive Computing, Vol.3, Issue 2, pp. 42-47, 2004.
  18. [18] D. Tang, J. Botzheim, N. Kubota, and T. Yamaguchi, “Estimation of human transport modes by fuzzy spiking neural network and evolution strategy in informationally structured space,” Genetic and Evolutionary Fuzzy Systems (GEFS) 2013 IEEE Int. Workshop on, pp. 36-43, 2013.
  19. [19] W. Maass, “Networks of spiking neurons: The third generation of neural network models,” Neural Networks, Vol.10, No.9, pp. 1659-1671, 1997.
  20. [20] P. O’Connor, D. Neil, S. Liu, T. Delbruck, and M. Pfeiffer, “Real-time classification and sensor fusion with a spiking deep belief network,” Frontiers in Neuroscience, Vol.7, 2013.
  21. [21] N. Kubota, D. Tang, T. Obo, and S. Wakisaka, “Localization of human based on fuzzy spiking neural network in informationally structured space,” Int. Conf. on Fuzzy Systems, 2010.
  22. [22] S. Shao, N. Shuo, and N. Kubota, “A Fuzzy Spiking Neural Network for Behavior Estimation by Multiple Environmental Sensors,” IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC2018), pp. 1514-1519, 2018.
  23. [23] C. Aliustaoglu, H. Ertunc, and H. Ocak, “Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system,” Mechanical Systems and Signal Processing, Vol.23, No.2, pp. 539-546, 2009.
  24. [24] L. Bergasa, J. Nuevo, M. Sotelo, R. Barea, and M. Lopez, “Real-Time System for Monitoring Driver Vigilance,” IEEE Trans. on Intelligent Transportation Systems, Vol.7, No.1, pp. 63-77, 2006.

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