Sequential Human Behavior Recognition for Cooking-Support Robots

Tsukasa Fukuda; Yasushi Nakauchi; Katsunori Noguchi; Takashi Matsubara

doi:10.20965/jrm.2005.p0717

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JRM Vol.17 No.6 pp. 717-724

doi: 10.20965/jrm.2005.p0717

(2005)

Paper:

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Sequential Human Behavior Recognition for Cooking-Support Robots

Tsukasa Fukuda^, Yasushi Nakauchi^, Katsunori Noguchi^,
and Takashi Matsubara^

^*College of Eng. Syst., Grad. School of Syst. and Info. Eng., University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan

^**Dept. of Computer Science, National Defense Academy, Yokosuka 239-8686, Japan

Received:

January 26, 2005

Accepted:

May 17, 2005

Published:

December 20, 2005

Keywords:

intelligent environment, data mining, mobile robot, human-robot interaction

Abstract

Recent advances in information technology are making electric household appliances computerized and networked. If our environments could intuit our activities, e.g., by sensors, novel services taking anticipated actions into account would become possible. We propose activity recognition that infers a subject’s next action based on previously observed behaviors. We developed a cooking-support robot that suggests by voice and gesture what the subject may want to do next. Experimental results confirmed feasibility of the inference and the quality of support.

Cite this article as:

T. Fukuda, Y. Nakauchi, K. Noguchi, and T. Matsubara, “Sequential Human Behavior Recognition for Cooking-Support Robots,” J. Robot. Mechatron., Vol.17 No.6, pp. 717-724, 2005.

Data files:

References

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[11] D. J. Moore, I. A. Essa, and M. H. Hayes III, “Exploiting Human Actions and Object Context for Recognition Tasks,” Proc. of The 7th IEEE International Conference on Computer Vision, pp. 80-86, 1999.
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[13] Y. Nakauchi et al., “Vivid Room: Human Intention Detection and Activity Support Environment for Ubiquitous Autonomy,” Proc. of IROS2003, pp. 773-778, 2003.
[14] J. Pei et al., “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” Proc. of International Conference of Data Engineering, pp. 215-224, 2001.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] K. Asaki, Y. Kishimoto, T. Sato, and T. Mori, “One-Room-Type Sensing System for Recognition and Accumulation of Human Behavior –Proposal of Behavior Recognition Techniques–,” Proc. of JSME ROBOMEC’00, 2P1-76-119, 2000.

[2] [2] R. Agrawal, and R. Strikant, “Fast algorithms for mining association rules,” Proc. of the 20th International Conference on Very Large Databases, pp. 487-499, 1994.

[3] [3] B. Brumitt et al., “Easy Living: Technologies for Intelligent Environments,” Proc. of International Symposium on Handheld and Ubiquitous Computing, 2000.

[4] [4] B. Brumitt, B. Meyers, J. Krumm, A. Kern, and S. Shafer, “EasyLiving: Technologies for Intelligent Environments,” Proc. of International Symposium on Handheld and Ubiquitous Computing, pp. 12-29, 2000.

[5] [5] I. A. Essa, “Ubiquitous sensing for smart and aware environments: technologies towards the building on an aware home,” Position Paper for the DARPA/NFS/NIST workshop on Smart Environment, 1999.

[6] [6] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, “Advances in Knowledge Discovery and Data Mining,” MIT Press, 1996.

[7] [7] H. Ishiguro, T. Ono, M. Imai, T. Maeda, T. Kanda, and R. Nakatsu, “Robovie: A robot generates episode chains in our daily life,” Proc. of Int. Symposium on Robotics, pp. 1356-1361, 2001.

[8] [8] J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale, and S. Shafer, “Multi-Camera Multi-Person Tracking for Easy Living,” Proc. of 3rd IEEE International Workshop on Visual Surveillance, pp. 3-10, 2000.

[9] [9] J. Lee, N. Ando, and H. Hashimoto, “Design Policy for Intelligent Space,” Proc. of IEEE International Conference on System, Man and Cybernetics (SMC’99), pp. 12-15, 1999.

[10] [10] D. J. Moore, I. A. Essa, and M. H. Hayes III, “ObjectSpaces: Context Management for Human Activity Recognition,” Georgia Institute of Technology, Graphics, Visualization and Usability Center, Technical Report, #GIT-GVU-98-26, 1998.

[11] [11] D. J. Moore, I. A. Essa, and M. H. Hayes III, “Exploiting Human Actions and Object Context for Recognition Tasks,” Proc. of The 7th IEEE International Conference on Computer Vision, pp. 80-86, 1999.

[12] [12] T. Mori, T. Sato et al., “One-Room-Type Sensing System for Recognition and Accumulation of Human Behavior,” Proc. of IROS2000, pp. 345-350, 2000.

[13] [13] Y. Nakauchi et al., “Vivid Room: Human Intention Detection and Activity Support Environment for Ubiquitous Autonomy,” Proc. of IROS2003, pp. 773-778, 2003.

[14] [14] J. Pei et al., “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” Proc. of International Conference of Data Engineering, pp. 215-224, 2001.

[15] [15] A. Pentland, “Smart Rooms,” Scientific American, pp. 54-62, 1996.

[16] [16] A. Pentland, R. Picard, and P. Maes, “Smart Rooms, Desks, and Clothes: Toward Seamlessly Networked Living,” British Telecommunications Engineering, Vol.15, pp. 168-172, July, 1996.

[17] [17] T. Sato, Y. Nishida, and H. Mizoguchi, “Robotic Room: Symbiosis with human through behavior media,” Robotics and Autonomous Systems 18 International Workshop on Biorobotics: Human-Robot Symbiosis, Elsevier, pp. 185-194, 1996.

[18] [18] M. Weiser, “The Computing for the Twenty-First Century,” Scientific American, pp. 94-104, September, 1991.

Sequential Human Behavior Recognition for Cooking-Support Robots

Tsukasa Fukuda*, Yasushi Nakauchi*, Katsunori Noguchi**, and Takashi Matsubara**

Tsukasa Fukuda^, Yasushi Nakauchi^, Katsunori Noguchi^,
and Takashi Matsubara^