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JACIII Vol.14 No.3 pp. 309-315
doi: 10.20965/jaciii.2010.p0309
(2010)

Paper:

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Human Behavior Measurement Based on Sensor Network and Robot Partners

Naoyuki Kubota*, Takenori Obo*, and Honghai Liu**

*Dept. of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

**University of Portsmouth, Eldon Building, Winston Churchill Avenue, Portsmouth, England PO1 2DJ, UK

Received:
December 18, 2009
Accepted:
January 6, 2010
Published:
April 20, 2010
Creative Commons License
Keywords:
human interaction, behavior understanding, sensor networks, robot partners, evolutionary computation
Abstract
This paper proposes to measure human behavior based on a sensor network. It starts by explaining informationally structured space, robot partners, and sensor networks developed in this study. Next, it discusses the applicability of sensor network and robot partner to rehabilitation. Growing neural gas is applied to extract an image of a person from a background image and a steady-state genetic algorithm is used to extract human movement from threedimensional distance images. We confirm the effectiveness of our proposal through experiments.
Cite this article as:
N. Kubota, T. Obo, and H. Liu, “Human Behavior Measurement Based on Sensor Network and Robot Partners,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.3, pp. 309-315, 2010.
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References
  1. [1] S. F. Szekeres, M. Ylvisaker, and S. B. Cohen, “A Framework for Cognitive Rehabilitation Therapy, Community Re-entry for Head Injured Adults,” M. Ylvisaker and E. M. R. Gobble (Ed.), Butterworth-Heinemann, pp. 87-136, 1987.
  2. [2] S. Attygalle, M. Duff, T. Rikakis, and H. Jiping, “Low-cost, athome assessment system with Wii Remote based motion capture,” In Proc. of the Conf. on Virtual Rehabilitation, pp. 168-174, 2008.
  3. [3] M. Duff, S. Attygalle, H. Jiping, and T. Rikakis, “A Portable, Low-cost Assessment Device for Reaching Times,” Engineering in Medicine and Biology Conf. (EMBC2008), pp. 4150-4153, 2008.
  4. [4] M. Brady and R. Paul, “Robotics Research: The First International Symposium,” The MIT Press, 1984.
  5. [5] N. Kubota and Y. Shimomura, “Human-Friendly Networked Partner Robots toward Sophisticated Services for A Community,” Proc. of SICE-ICCAS 2006, pp. 4861-4866, 2006.
  6. [6] N. Kubota and K. Nishida, “Cooperative Perceptual Systems for Partner Robots Based on Sensor Network,” Int. J. of Computer Science and Network Security, Vol.6, No.11, pp. 19-28, 2006.
  7. [7] N. Kubota, T. Obo, and T. Fukuda, “An Intelligent Monitoring System based on Emotional Model in Sensor Networks,” The 18th IEEE Int. Symposium on Robot and Human Interactive Communication, pp. 346-351, 2009.
  8. [8] C. Corderio and D. Agrawal, “Ad Hoc & Sensor Networks,” World Scientific, 2006.
  9. [9] I. Khemapech, I. Duncan, and A. Miller, “A Survey of Wireless Sensor Networks Technology,” Proc. of The 6th Annual PostGraduate Symposium on The Convergence of Telecommunications, Networking and Broadcasting, 2005.
  10. [10] J-H. Kim, Y-D. Kim, and K-H. Lee, “The Third Generation of Robotics: Ubiquitous Robot,” Proc. of 2nd Int. Conf. on Autonomous Robots and Agents, 2004, pp. 1-12.
  11. [11] M. Satomi, H. Masuta, and N. Kubota, “Hierarchical Growing Neural Gas for Information Structured Space,” IEEE Symposium Series on Computational Intelligence 2009, 2009.
  12. [12] B. Fritzke, “A growing neural gas network learns topologies,” Advances in Neural Information Processing Systems, Vol.7, pp. 625-632, 1995.
  13. [13] B. Fritzke, “Growing self-organizing networks? why?” European Symposium on Artificial Neural Networks, pp. 61-72, 1996.
  14. [14] G. Syswerda, “A Study of Reproduction in Generational and Steady-State Genetic Algorithms,” In Foundations of Genetic Algorithms, Morgan Kaufmann Publishers, Inc., 1991.
  15. [15] N. Kubota and K. Nishida, “Perceptual Control Based on Prediction for Natural Communication of A Partner Robot,” IEEE Trans. on Industrial Electronics, Vol.54, No.2, pp. 866-877, April 2007.
  16. [16] N. Kubota, “Computational Intelligence for Structured Learning of A Partner Robot Based on Imitation,” Information Sciences, No.171, pp. 403-429, May 2005.
  17. [17] N. Kubota and K. Tomoda, “Behavior Coordination of A Partner Robot based on Imitation,” Proc. of 2nd Int. Conf. on Autonomous Robots and Agents, pp. 164-169, 2004.
  18. [18] N. Kubota, H. Sotobayashi, and T. Obo, “Human Interaction and Behavior Understanding Based on Sensor Network with iPhone for Rehabilitation,” Proc. of Int. Workshop on Advanced Computational Intelligence and Intelligent Informatics, 2009.
  19. [19] T. Kohonen, “Self-Organizing Maps,” The Third Edition, Springer, 2000.
  20. [20] B. Fritzke, “Unsupervised clustering with growing cell structures,” Neural Networks, Vol.2, pp. 531-536, 1991.
  21. [21] B. Fritzke, “Growing cell structures? a self organizing network in k dimensions,” Artificial Neural Networks, Vol.2, No.2, pp. 1051-1056, 1992.

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