Behavior Estimation Based on Multiple Vibration Sensors for Elderly Monitoring Systems
Shuai Shao*, Naoyuki Kubota*, Kazutaka Hotta**, and Takuya Sawayama***
*Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
**Kansai Electric Power Co., Inc.
3-11-20 Nakouji, Amagasaki, Hyogo 661-0974, Japan
***New Sensor Incorporated
1-24-3 Yuyamadai, Kawanishi, Hyogo 666-0137, Japan
Aging has become a global social issue nowadays. We want to provide an elderly care system for older people who live alone. Based on the perspective of an informationally structured space (ISS), we have developed a monitoring system by using high-precision vibration sensors. In preliminary experiments, we observed that the autocorrelation coefficient reflected periodic human activities to a certain extent. Therefore, we propose a time delay neural network (TDNN) with autocorrelation as the input to analyze the vibration data. The system can estimate the current state of the elderly. When the system observes any abnormal situation of the elderly, the system can confirm by voice or notify the caregiver, if necessary. In the experiments, we compared the proposed method with traditional TDNNs using raw data as the input. The results demonstrated that proposed methods had performed well when using vibration sensors to measure user behaviors in the bathroom and living room.
-  Cabinet Office, “Aging Society White Paper (whole edition) (2017),” 2018, https://www8.cao.go.jp/kourei/whitepaper/w-2017/html/zenbun/index.html (in Japanese) [accessed June 29, 2021]
-  S. Y. Y. Tun, S. Madanian, and F. Mirza, “Internet of things (IoT) applications for elderly care: a reflective review,” Aging Clinical and Experimental Research, Vol.33, pp. 855-867, 2021.
-  H. Yang, W. Lee, and H. Lee, “IoT smart home adoption: the importance of proper level automation,” J. of Sensors, Vol. 2018, Article No.6464036, 2018.
-  A. Khan, A. Al-Zahrani, S. Al-Harbi et al., “Design of an IoT smart home system,” 2018 15th Learning and Technology Conf. (L&T), 2018.
-  S. Zheng, N. Apthorpe, M. Chetty et al., “User perceptions of smart home IoT privacy,” Proc. of the ACM on Human-Computer Interaction, Vol.2, Article No.200, 2018.
-  R. Steele, A. Lo, C. Secombe 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.
-  B. E. Buthelezi, M. Mphahlele, D. D. DuPlessis et al., “ZigBee healthcare monitoring system for ambient assisted living environments,” Int. J. of Communication Networks and Information Security, Vol.11, No.1, pp. 85-92, 2019.
-  J. Heikenfeld, A. Jajack, J. Rogers et al., “Wearable sensors: modalities, challenges, and prospects,” Lab on a Chip, Vol.18, No.2, pp. 217-248, 2018.
-  K. Nisar, A. A. A. Ibrahim, Y. J. Park et al., “Indoor roaming activity detection and analysis of elderly people using RFID technology,” 2019 1st Int. Conf. on Artificial Intelligence and Data Sciences (AiDAS), pp. 174-179, 2019.
-  F. Harrou, N. Zerrouki, Y. Sun 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.
-  E. Jeong and D. Kim, “Estimating human walking pace and direction using vibration signals,” J. of Institute of Control, Robotics and Systems, Vol.20, No.5, pp. 481-485, 2014 (in Korean).
-  F. Li, J. Clemente, M. Valero, Z. Tse, S. Li, and W. Song, “Smart home monitoring system via footstep-induced vibrations,” IEEE Systems J., Vol.14, No.3, pp. 3383-3389, 2020.
-  M. Daher, A. Diab, M. E. B. El Najjar et al., “Elder tracking and fall detection system using smart tiles,” IEEE Sensors J., Vol.17, No.2, pp. 469-479, 2016.
-  J. Clemente, F. Li, M. Valero, and W. Song, “Smart seismic sensing for indoor fall detection, location, and notification,” IEEE J. of Biomedical and Health Informatics, Vol.24, No.2, pp. 524-532. 2020.
-  A. Fehske, J. Gaeddert, and J. H. Reed, “A new approach to signal classification using spectral correlation and neural networks,” 1st IEEE Int. Symp. on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005), pp. 144-150, 2005.
-  L. Muda, M. Begam, and I. Elamvazuthi, “Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques,” arXiv preprint, arXiv:1003.4083, 2010.
-  D. Shi, H. Zhang, and L. Yang, “Time-delay neural network for the prediction of carbonation tower’s temperature,” IEEE Trans. on Instrumentation and Measurement, Vol.52, No.4, pp. 1125-1128, 2003.
-  V. Peddinti, D. Povey, and S. Khudanpur, “A time delay neural network architecture for efficient modeling of long temporal contexts,” 16th Annual Conf. of the Int. Speech Communication Association, 2015.
-  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.
-  N. Kubota and Y. Shimomura, “Human-friendly networked partner robots toward sophisticated services for a community,” 2006 SICE-ICASE Int. Joint Conf., pp. 4861-4866, 2006.
-  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.
-  R. Gorrepotu, N. S. Korivi, K. Chandu et al., “Sub-1GHz miniature wireless sensor node for IoT applications,” Internet of Things, Vol.1-2, pp. 27-39, 2018.
-  D. Tang, Y. Yoshihara, T. Obo et al., “Social rhythm management support system based on Informationally Structured Space,” 2016 9th Int. Conf. on Human System Interactions (HSI), pp. 429-434, 2016.
-  T. Obo, N Kubota, and B. H. Lee, “Localization of human in informationally structured space based on sensor networks,” Int. Conf. on Fuzzy Systems, pp. 1-7, 2010.
-  A. Waibel, T. Hanazawa, G. Hinton et al., “Phoneme recognition using time-delay neural networks,” IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.37, No.3, pp. 328-339, 1989.
-  S. Shao, J. Woo, K. Yamamoto et al., “Elderly Health Care System Based on High Precision Vibration Sensor,” 2019 Int. Conf. on Machine Learning and Cybernetics (ICMLC), pp. 1-6, 2019.