single-rb.php

JRM Vol.19 No.6 pp. 667-675
doi: 10.20965/jrm.2007.p0667
(2007)

Paper:

An Ultrasonic 3D Tag System for Evidence-Based Nursing Care Support

Toshio Hori*,** and Yoshifumi Nishida*,**

*Digital Human Research Center, National Institute of Advanced Industrial Science and Technology (AIST)

**CREST, Japan Science and Technology Agency (JST)

Received:
April 5, 2007
Accepted:
August 17, 2007
Published:
December 20, 2007
Keywords:
ultrasonic location sensor, activity of daily living, nursing care support
Abstract
This paper introduces a pervasive sensor system for nursing homes, where daily activities of inhabitants are monitored by pervasive sensors. Deterioration in the quality of nursing care for old people has become one of the serious problems in aging societies and the authors have been challenging the problem by sensors embedded in a nursing room. The system employs an ultrasonic 3D tag system developed by the authors to record position information of the wheelchair of a subject and the information is utilized to provide prompt assistance to the subject and also to log their movement over their daily life. In our experiments, we obtained the subject’s position data for a month and a half in a nursing home in Tokyo and analyzed the subject’s activity transitions, the similarity between the activity and a common home schedule, and other important factors for nursing care. This paper presents the concept of the system, overview of the current system and experimental results obtained.
Cite this article as:
T. Hori and Y. Nishida, “An Ultrasonic 3D Tag System for Evidence-Based Nursing Care Support,” J. Robot. Mechatron., Vol.19 No.6, pp. 667-675, 2007.
Data files:
References
  1. [1] http://www.mhlw.go.jp/toukei/saikin/hw/fukushi/04/
    (in Japanese).
  2. [2] V. Stanford, “Using Pervasive Computing to Deliver Elder Care,” IEEE Pervasive Computing, Vol.1, No.1, pp. 10-13, 2002.
  3. [3] A. G. Hauptmann, J. Gao, R. Yan, Y. Qi, J. Yang, and H. D. Wactlar, “Automated Analysis of Nursing Home Observations,” IEEE Pervasive Computing, Vol.3, No.2, pp. 15-21, 2004.
  4. [4] D. Chen, J. Yang, and H. D. Wactlar, “Towards Automatic Analysis of Social Interaction Patterns in a Nursing Home Environment from Video,” 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, in Proc. of ACM Multimedia 2004, pp. 283-290, 2004.
  5. [5] T. Harada, Y. Kawano, S. Otani, T. Mori, and T. Sato, “Construction of Wireless Ad Hoc Network for Lifelog based Physical & Informational Support System,” Proc. of the 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 89-95, 2005.
  6. [6] T. Mori, A. Takada, H. Noguchi, T. Harada, and T. Sato, “Behavior Prediction Based on Daily-Life Record Database in Distributed Sensing Space,” Proc. of the 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1833-1839, 2005.
  7. [7] P. Harmo, T. Taipalus, J. Knuuttila, J. Vallet, and A. Halme, “Needs and Solutions – Home Automation and Service Robots for the Elderly and Disabled,” Proc. of the 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2721-2726, 2005.
  8. [8] T. Hori, Y. Nishida, H. Aizawa, and N. Yamasaki, “Networked Sensors for Monitoring Human Behavior,” Proc. of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 900-905, 2003.
  9. [9] Y. Nishida, H. Aizawa, T. Hori, N. H. Hoffman, T. Kanade, and M. Kakikura, “3D Ultrasonic Tagging System for Observing Human Activity,” Proc. of the 2003 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 785-791, 2003.
  10. [10] T. Hori, Y. Nishida, H. Aizawa, S. Murakami, and H. Mizoguchi, “Sensor Network for Supporting Elderly Care Home,” Proc. of the Third IEEE Int. Conf. on Sensors 2004, pp. 575-578, 2004.
  11. [11] Y. Nishida, S. Murakami, T. Hori, and H. Mizoguchi, “Minimally Privacy-Violative Human Location Sensor by Ultrasonic Radar Embedded on Ceiling,” Proc. of the Third IEEE Int. Conf. on Sensors 2004, pp. 433-436, 2004.
  12. [12] Y. Nishida, K. Kitamura, T. Hori, A. Nishitani, T. Kanade, and H. Mizoguchi, “Quick Realization of Function for Detecting Human Activity Events by Ultrasonic 3D Tag and Stereo Vision,” Second IEEE Int. Conf. on Pervasive Computing and Communications, pp. 43-54, 2004.
  13. [13] M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Communications of the ACM, Vol.24, No.6, pp. 381-395, 1981.

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

Last updated on Dec. 02, 2024