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Warianty tytułu
Języki publikacji
Abstrakty
In order to realize a system that supports human actions timely, the system must have a certain model of human actions. Therefore, we propose a modeling method of human actions. In this method, it is supposed that a person changes his action according to the situation around him, and the causality between the situation around a person and the change of a human action is modeled. This causality is expressed by an If-then-Rule style where a human action and the situation around a human are expressed by a discrete event and time series data respectively. Moreover, as the necessary function for human support systems, an estimation method of the next human action and its execution timing is consisted based on the proposed modeling method. The usefulness of the proposed modeling and estimation methods is examined through the estimation experiment of next human action and its execution timing with a radio-controlled vehicle.
Wydawca
Rocznik
Tom
Strony
223--233
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
autor
- Department of Electrical Engineering and Computer Science, Nagoya University, C3-1(631), Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8603, Japan
autor
- Department of Mechanical Engineering, Aichi Institute of Technology, 1247 Yachigusa Yakusa-cho, Toyota, Aichi, 470-0392, Japan
autor
- Department of Electrical Engineering and Computer Science, Nagoya University, C3-1(631), Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8603, Japan
Bibliografia
- [1] S.Kurihara, ”Human Behavior Mining Using Sensing Network”, Transactions of the Japanese Society for Artificial Intelligence(in Japanese), Vol.23, No.5, 2008, pp.611-616.
- [2] W.Takano, Y.Nakamura, Humanoid Robot’s Autonomous Acquisition of Proto-Symbols through Motion Segmentation?h, IEEE-RAS International Conference on Humanoid Robots, 2006, pp.425-431.
- [3] N. Suzuki, K. Hirasawa, K. Tanaka, Y. Kobayashi, Y. Sato and Y. Fujino, ”Learning Motion Patterns and Anomaly Detection by Human Trajectory Analysis”, International Conference on Systems, Man and Cybernetics, 2007, pp.498-503.
- [4] Y.Ishii, ”A Framework for Suspicious Action Detection with Mixture Distributions of Action Primitives”, Proceedings of the Pacific Rim Symposium on Advances in Image and Video Technology, 2008, pp.519-530.
- [5] H.Ouchi, Y.Nishida, K.Kimu, Y.Motomura, H.Mizoguchi, ”Detecting and Modeling Play Behavior Using Sensor-Embedded Rock-climbing Equipment”, Proc. of the International Conference on Interaction Design and Children, 2010, pp.118-127.
- [6] Y.Yamada, T.Yamamoto, T.Sakai, T.Morizono, Y.Umetani, ”Human Error Recovery by a Maintainable Human/Robot Parts Conveyance System”, Journal of the Robotics Society of Japan(in Japanese), Vol.21, No.4, 2003, pp.420-426.
- [7] K.Hattori, ”Advanced IE method using a behavior tracking system”, SICE Annual Conference, 2005.
- [8] S.Nishio, H.Okamoto, N.Babaguchi, ”Hierarchical Abnormality Detection based on Situation”, Proc. of International Conference on Pattern Recognition, 2010, pp. 1108-1111.
- [9] T.Fukuda, Y.Nakauchi, K.Noguchi, T.Matsubara, ”Time Series Action Support by Mobile Robot in Intelligent Environment”, Proc. of IEEE International Conference on Robotics and Automation, 2005, pp.2908-2913.
- [10] S.Sekizawa, S.Inagaki, T.Suzuki, S.Hayakawa, N.Tsuchida, T.Tsuda, H.Fujinami, ”Modeling and Recognition of Driving Behavior Based on Stochastic Switched ARX Model”, IEEE Trans. on Intelligent Transportation Systems, Vol. 8, No. 4, 2007, pp. 593-606.
- [11] K.Inata, Pongsathorn Raksincharoensak, M.Nagai, ”Driver Behavior Modeling Based on Database of Personal Mobility Driving in Urban Area”, Proceedings of International Conference on Control, Automation and Systems, 2008, pp.2902-2907.
- [12] K.Hashimoto, K.Doki, S.Doki, S.Okuma, ”Study on modeling and recognition of human behaviors by IF-Then-Rules with HMM”, Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, 2009, pp.3446-3451.
- [13] K.Doki, K.Hashimoto, S.Doki, S.Okuma, T.Ohtsuka, ”Estimation of Next Behavior and its Timing based on Human Behavior Model with Time Series Signal”, Proc. of the IEEE Symposium Series on Computational Intelligence, 2011, pp.102-107.
- [14] S. Nakagawa ”A Connected SpokenWord Recognition Method by O(n) Dynamic Programming Pattern Matching Algorithm”, Proc. of International Conference on IEEE Acoustics Speech and Signal Processing, 1983, pp.296-299.
- [15] S.Uchida, A.Mori, R.Kurazume, R.Taniguchi, T.Hasegawa, ”Logical DP Matching for Detecting Similar Subsequence”, 8th Asian Conference on Computer Vision LNCS, vol.4843, 2007, pp.628-637.
- [16] A.Imamura, ”Telephony Speech Spotting based on HMM”, Institute of Electronics, Information and Communication Engineers Technical Report. SP(in Japanese), Vol.90, No.18, 1990, pp.73-80.
- [17] W.Takano, A.Matsushita, K.Iwao, Y.Nakamura, ”Recognition of Human Driving Behaviors based on Stochastic Symbolization of Time Series Signal”, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp.167-172.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-693a3752-2cc2-463b-8b73-2fec2895ae39