Top > JRM > Predicting Behaviors of Residents by Modeling Preceding Action ...

single-rb.php

JRM Vol.24 No.2 pp. 320-329
doi: 10.20965/jrm.2012.p0320
(2012)

Paper:

Views over last 60 days: 673

Predicting Behaviors of Residents by Modeling Preceding Action Transition from Trajectories

Taketoshi Mori, Shoji Tominaga, Hiroshi Noguchi,
Masamichi Shimosaka, Rui Fukui, and Tomomasa Sato

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

Received:
October 11, 2011
Accepted:
January 16, 2012
Published:
April 20, 2012
Creative Commons License
Keywords:
behavior modeling, eventmining, time-series association rule
Abstract
The smooth provision of support to residents by information display systems or robots will essentially require that their behaviors be appropriately grasped and that predictions be made that allow some margin for preparations. In this paper, we offer new perspectives by proposing a novel method to predict residents’ behaviors. The proposed method mainly consists of the following two phases: (1) to grasp the chains of residents’ potential actions from their trajectories, and then, (2) to identify the rules of association between residents’ behaviors, subject behavior to support and their last actions. In order to verify the performance of the proposedmethod in predicting residents’ behaviors, we have conducted experiments using two residents’ trajectories that have been tracked for around one year.
Cite this article as:
T. Mori, S. Tominaga, H. Noguchi, M. Shimosaka, R. Fukui, and T. Sato, “Predicting Behaviors of Residents by Modeling Preceding Action Transition from Trajectories,” J. Robot. Mechatron., Vol.24 No.2, pp. 320-329, 2012.
Data files:
References
  1. [1] B. Binmitt, B. Meyers, J. Krumm, A. Kern, and S. Shafer, “EasyLiving: Technologies for Intelligent Environments,” In Proc. of the 2nd Int. Symposium on Handheld and Ubiquitous Computing, pp. 12-29, 2000.
  2. [2] C. D. Kidd, 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,” In Proc. of the 2nd Int. Workshop on Cooperative Buildings, pp. 191-198, 1999.
  3. [3] J. H. Lee and H. Hashimoto, “Intelligent space,” In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Vol.2, pp. 1358-1363, 2000.
  4. [4] T. Mori, H. Noguchi, A. Takada, and T. Sato, “Sensing Room: Distributed Sensor Environment for Measurement of Human Daily Behavior,” In 1st Int. Workshop on Networked Sensing Systems, pp. 40-43, 2004.
  5. [5] Y. Nakauchi, K. Noguchi, P. Somwong, T. Matsubara, and A. Namatame, “Vivid room: human intention detection and activity support environment for ubiquitous autonomy,” In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Vol.1, pp. 773-778, 2003.
  6. [6] H. Noguchi, R. Urushibata, T. Sato, T. Mori, and T. Sato, “System for Tracking Human Position by Multiple Laser Range Finders Deployed in Existing Home Environment,” Aging Friendly Technology for Health and Independence, pp. 226-229, 2010.
  7. [7] T. Kanda, D. F. Glas, M. Shiomi, H. Ishiguro, and N. Hagita, “Who will be the customer?: A social robot that anticipates people’s behavior from their trajectories,” In Proc. of the 10th Int. Conf. on Ubiquitous Computing, pp. 380-389, 2008.
  8. [8] T. Sasaki, D. Brscic, and H. Hashimoto, “Human-Observation-Based Extraction of Path Patterns for Mobile Robot Navigation,” IEEE Trans. on Industrial Electronics, Vol.57, No.4, pp. 1401-1410, 2010.
  9. [9] B. D. Ziebart, N. Ratliff, G. Gallagher, C. Mertz, K. Peterson, J. A. Bagnell, M. Hebert, A. K. Dey, and S. Srinivasa, “Planning-based prediction for pedestrians,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3931-3936, 2009.
  10. [10] M. Isard and A. Blake, “Condensation-Conditional density propagation for visual tracking,” Int. J. on Computer Vision, Vol.29, No.1, pp. 5-28, 1998.
  11. [11] T. Katoh, H. Arimura, and K. Hirata, “Mining Frequent Bipartite Episode from Event Sequences,” In Discovery Science, Lecture Notes in Computer Science, Vol.5808, pp. 136-151, Springer, 2009.
  12. [12] S. K. Harms and J. S. Deogun, “Sequential Association Rule Mining with Time Lags,” J. of Intelligent Information Systems, Vol.22, pp. 7-22, 2004.
  13. [13] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, “PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth,” In Proc. of the 17th Int. Conf. on Data Engineering, pp. 215-224, 2001.
  14. [14] T. Uno, T. Asai, Y. Uchida, and H. Arimura, “An Efficient Algorithm for Enumerating Closed Patterns in Transaction Databases,” In Proc. of Discovery Science, pp. 16-31, 2004.
  15. [15] R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” In Proc. of the 5th Int. Conf. on Extending Database Technology: Advances in Database Technology, pp. 3-17, 1996.
  16. [16] H. Ohtani, T. Kida, T. Uno, and H. Arimura, “Efficient serial episode mining with minimal occurrences,” In Proc. of the 3rd Int. Conf. on Ubiquitous Information Management and Communication, pp. 457-464, 2009.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

Copyright© 2012 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.

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

Last updated on Oct. 11, 2024