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JRM Vol.24 No.5 pp. 754-765
doi: 10.20965/jrm.2012.p0754
(2012)

Paper:

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Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection

Taketoshi Mori, Takahito Ishino, Hiroshi Noguchi,
Tomomasa Sato, Yuka Miura, Gojiro Nakagami,
Makoto Oe, and Hiromi Sanada

*The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Received:
March 30, 2012
Accepted:
June 21, 2012
Published:
October 20, 2012
Creative Commons License
Keywords:
MIMAMORI, intelligent environments, pyroelectric sensor, behavior model, single life
Abstract
A life pattern estimation method and its application to anomaly detection of a single elderly are proposed. Our observation system deploys some pyroelectric sensors in an elderly’s house and monitors and measures activities 24 hours a day to grasp residents’ life patterns. Activity data is successively forwarded to the nurse operation center and displayed to nurses at the center. The system reports status related to anomalies together with the basic activities of elderly residents to the nurses, who decide whether recent accumulated data expresses an anomaly or not based on suggestions from the system. In the system, residents whose lifestyle features resemble each other are categorized into the same group. Anomalies that occurred in the past are shared in the group and utilized in an anomaly detection algorithm. This algorithm is based on an “anomaly score.” The score is figured out by utilizing the activeness of the house’s elderly resident. This activeness is approximately proportional to the frequency of sensor response within one minute. The anomaly score is calculated from the difference between activeness in the present and in the past averaged over the long term. The score is thus positive if activeness in the present is greater than the average in the past, and the score is negative if the value in the present is less than average. If the score exceeds a certain threshold, it means that an anomaly event has occurred. An activity estimation algorithm is also developed that estimates the basic activities of residents such as getting up in the morning, or going out. The estimation is also shown to nurses with the anomaly score of residents. Nurses can understand the condition of elderly residents’ health by combining the information and planning the most appropriate way to respond.
Cite this article as:
T. Mori, T. Ishino, H. Noguchi, T. Sato, Y. Miura, G. Nakagami, M. Oe, and H. Sanada, “Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection,” J. Robot. Mechatron., Vol.24 No.5, pp. 754-765, 2012.
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References
  1. [1] N. Noury, T. Herve, V. Riallé, G. Virone, E. Mercier, G. Morey, A. Moro, and T. Porcheron, “Monitoring Behavior in Home Using a Smart Fall Sensor and Position Sensors,” Annual Conf. on Microtechnologies in Medicine and Biology, pp. 607-610, 2000.
  2. [2] A. Sixsmith and N. Johnson, “A Smart Sensor to Detect the Falls of the Elderly,” IEEE Pervasive Computing, pp. 42-47, 2004.
  3. [3] D. K. Cory, R. J. Orr, G. D. Abowd, C. G. Atkeson, I. A. Essa, B. MacIntyre, E. Mynatt, T. E. Starner, and W. Newstetter, “The Aware Home: A Living Laboratory for Ubiquitous Computing Research,” The Second Int. Workshop on Cooperative Buildings, pp. 191-198, 1999.
  4. [4] T. Yamazaki, “Ubiquitous Home: Real-life Testbed for Home Context-Aware Service,” First Int. Conf. on Testbeds and Reserch Infrastructures for the Development of NeTworks and COMmunities, pp. 54-59, 2005.
  5. [5] K. L. Stephen, S. Intille, E. M. Tapia, J. S. Beaudin, P. Kaushik, J. Nawyn, and R. Rockinson, “Using a Live-In Laboratory for Ubiquitous Computing Research,” Parvasive 2006, pp. 349-365, 2006.
  6. [6] C.-H. Lu and L.-C. Fu, “Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home,” IEEE Trans. on Automation Science and Engineering, Vol.6, pp. 598-609, 2009.
  7. [7] J. Lee, K. Morioka, and H. Hashimoto, “Mobile Robot Control in Intelligent Space for People Support,” J. of Robotics and Mechatronics, Vol.14, No.4, pp. 390-399, 2002.
  8. [8] Y. Nakauchi, K. Noguchi, P. Somwong, and T. Matsubara, “Human Intention Detection and Activity Support System for Ubiquitous Sensor Room,” J. of Robotics and Mechatronics, Vol.16, No.5, pp. 545-551, 2004.
  9. [9] T. Sasaki, Y. Toshima, and H. Hashimoto, “Design and Implementation of Basic Framework for Integration of Robot Technology Elements in Intelligent Space,” J. of Robotics and Mechatronics, Vol.23, No.4, pp. 523-531, 2011.
  10. [10] M. Niitsuma and H. Hashimoto, “Observation of Human Activities Based on Spatial Memory in Intelligent Space,” J. of Robotics and Mechatronics, Vol.21, No.4, pp. 515-523, 2009.
  11. [11] N. Noury, T. Herve, V. Rialle, G. Virone, E. Mercier, G. Morey, A. Moro, and T. Porcheron, “Monitoring behavior in home using a smart fall sensor and position sensors,” First Annual Int. Conf. on Microtechnologies in Medicine and Biology, pp. 607-610, 2000.
  12. [12] S. Ohta, H. Nakamoto, Y. Shinagawa, and T. Tanikawa, “A Health Monitoring System for Elderly People Living Alone,” J. Telemed Telecare, Vol.8, No.3, pp. 151-156, 2002.
  13. [13] T. Mori, R. Urushibata, M. Shimosaka, H. Noguchi, and T. Sato, “Anomaly Detection Algorithm Based on Life Pattern Extraction from Accumulated Pyroelectric Sensor Data,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2545-2552, 2008.
  14. [14] K. Oshima, R. Urushibata, A. Fujii, H. Noguchi, M. Shimosaka, T. Sato, and T. Mori, “Behavior Labeling Algorithms from Accumulated Sensor DataMatched to Usage of Livelihood SupportApplication,” in The 18th IEEE Int. Symp. on Robot and Human Interactive Communication, pp. 822-828, 2009.
  15. [15] T. Mori, R. Urushibata, H. Noguchi, H. Sanada, and T. Sato, “Sensor Arrangement for Life Activity Classification with Pyroelectric Sensors – Arrangement to Save Sensors and to Quasi-Maximize the Classification Precision –,” J. of Robotics and Mechatronics, Vol.23, No.4, pp. 494-504, 2011.
  16. [16] M. Shimosaka, T. Mori, A. Fujii, and T. Sato, “Discriminative Data Visualization for Daily Behavior Modeling,” Advanced Robotics, Vol.23, pp. 429-441, 2009.

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