JACIII Vol.25 No.4 pp. 423-431
doi: 10.20965/jaciii.2021.p0423


An Elderly Monitoring System Based on Multiple Ultra-Sensitive Vibration and Pneumatic Sensors

Shuai Shao, Kouhei Yamamoto, and Naoyuki Kubota

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

March 1, 2021
April 30, 2021
July 20, 2021
sensor networks, elderly monitoring system, non-contact sensors, autocorrelation coefficient
In recent years, aging of the population has become a major social problem. Various types of sensor networks have been applied to elderly monitoring for addressing the care problems of the elderly living alone. We have also proposed elderly monitoring systems based on wireless sensor network devices. However, vision-based sensors can also cause a mental burden on the elderly’s privacy. Furthermore, the number of sensors must be reduced, if possible. Therefore, this study proposes an elderly monitoring system composed of two vibration sensors placed on the floor and a pneumatic sensor placed on the bed. Because both sensors include considerable measurement noise, we propose a human behavior estimation method that includes anomaly detection from time-series measurement data using an autocorrelation coefficient. Finally, we discuss the effectiveness and usability of the proposed system through several experimental results. The accuracy of walking detection reaches 94.4%, while the error of heartbeat detection is 3.01 bpm.
Multi-sensor based monitoring system

Multi-sensor based monitoring system

Cite this article as:
S. Shao, K. Yamamoto, and N. Kubota, “An Elderly Monitoring System Based on Multiple Ultra-Sensitive Vibration and Pneumatic Sensors,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.4, pp. 423-431, 2021.
Data files:
  1. [1] Cabinet Office, “Aged Society White Paper, Whole Edition,” (in Japanese) [accessed March 1, 2021]
  2. [2] Cabinet Office, “Results of Awareness Survey on Elderly People Living Alone,” 2002, [accessed March 1, 2021]
  3. [3] M. Al-Khafajiy et al., “Remote health monitoring of elderly through wearable sensors,” Multimedia Tools and Applications, Vol.78, No.17, pp. 24681-24706, 2019.
  4. [4] F. Harrou et al., “Vision-based fall detection system for improving safety of elderly people,” IEEE Instrumentation & Measurement Magazine, Vol.20, No.6, pp. 49-55, 2017.
  5. [5] K. C. Tseng, C.-L. Hsu, and Y.-H. Chuang, “Designing an intelligent health monitoring system and exploring user acceptance for the elderly,” J. of Medical Systems, Vol.37, No.6, pp. 1-18, 2013.
  6. [6] R. Steele et al., “Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare,” Int. J. of Medical Informatics, Vol.78, No.12, pp. 788-801, 2009.
  7. [7] K. Kim, A. Jalal, and M. Mahmood, “Vision-based human activity recognition system using depth silhouettes: A smart home system for monitoring the residents,” J. of Electrical Engineering & Technology, Vol.14, No.6, pp. 2567-2573, 2019.
  8. [8] Y.-S. Hong, “Smart care beds for elderly patients with impaired mobility,” Wireless Communications and Mobile Computing, 1780904, 2018.
  9. [9] S. Misaki, K. Umakoshi, T. Matsui, H. Choi, M. Fujimoto, and K. Yasumoto, “Non-Contact In-Home Activity Recognition System Utilizing Doppler Sensors,” Adjunct Proc. of the 2021 Int. Conf. on Distributed Computing and Networking (ICDCN’21), pp. 169-174, 2021.
  10. [10] T. Obo et al., “A fuzzy spiking neural network using optical oscillosensor and pneumatic sensor for human state estimation,” 2010 World Automation Congress, pp. 1-6, 2010.
  11. [11] mircon, Monitoring Body Movement and Condition, (in Japanese) [accessed November 1, 2013]
  12. [12] S. Shao, N. Kubota, K. Hotta, and T. Sawayama, “Behavior Estimation Based on Multiple Vibration Sensor for Elderly Monitoring System,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.4, 2021.
  13. [13] 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, pp. 54-59, 2009.
  14. [14] N. K. Suryadevara and S. C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly,” IEEE Sensors J., Vol.12, No.6, pp. 1965-1972, 2012.
  15. [15] R. Gorrepotu et al., “Sub-1GHz miniature wireless sensor node for IoT applications,” Internet of Things, Vols.1-2, pp. 27-39, 2018.
  16. [16] D. Tang, Y. Yoshihara, T. Obo, T. Takeda, J. Botzheim, and N. Kubota, “Social rhythm management support system based on Informationally Structured Space,” 2016 9th Int. Conf. on Human System Interactions (HSI), pp. 429-434, 2016.
  17. [17] T. Obo, N. Kubota, and B. Lee, “Localization of human in informationally structured space based on sensor networks,” Int. Conf. on Fuzzy Systems (FUZZ-IEEE 2021), pp. 1-7, 2010.
  18. [18] A. Getis, “Spatial autocorrelation,” M. M. Fischer and A. Getis (Eds.), “Handbook of applied spatial analysis,” pp. 255-278, Springer, 2010.
  19. [19] A. Martini, A. Rivola, and M. Troncossi, “Autocorrelation analysis of vibro-acoustic signals measured in a test field for water leak detection,” Applied Sciences, Vol.8, No.12, p. 2450, 2018.
  20. [20] Y. Xu et al., “Autocorrelated Envelopes for early fault detection of rolling bearings,” Mechanical Systems and Signal Processing, Vol.146, 106990, 2021.
  21. [21] A. Hagiwara et al., “Validity and Reliability of the Physical Activity Scale for the Elderly (PASE) in Japanese Elderly People,” Geriatrics & Gerontology Int., Vol.8, No.3, pp. 143-151, 2008.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 23, 2024