HMM-embedded Bayesian network for heterogeneous command integration: Applications to biped humanoid operation over the network

Yasuo Matsuyama, Youichi Nishida

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    A method to combine a Bayesian Network (BN) and Hidden Markov Models (HMMs) is presented. This compound system is applied to robot operations. The addressed problem and presented methods are novel with the following features: (1) BN and HMMs make a total decision system by accepting evidences from HMMs to the BN. (2) The HMM-embedded BN is applied to the human motion recognition for the biped humanoid operation. (3) Besides the motion recognition, the image recognition is incorporated by adding a BN subsystem. Thus, the total HMM-embedded BN can be regarded as an integrator of heterogeneous commands. (4) The human operator and the biped humanoid can be located on the other side of the network each other. (5) The piped humanoid follows various commands of human motions without falling down by showing better sophistication and operation success than HMM-alone and BN-alone systems. In addition to the above, an information supply to the BN from brain signals is realized through a combination with a Support Vector Machine (SVM).

    Original languageEnglish
    Title of host publication5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings
    Pages138-145
    Number of pages8
    DOIs
    Publication statusPublished - 2008
    Event5th International Conference on Soft Computing As Transdisciplinary Science and Technology, CSTST '08 - Cergy-Pontoise
    Duration: 2008 Oct 282008 Oct 31

    Other

    Other5th International Conference on Soft Computing As Transdisciplinary Science and Technology, CSTST '08
    CityCergy-Pontoise
    Period08/10/2808/10/31

    Keywords

    • Bayesian network
    • Brain signal
    • Hidden Markov model
    • Human motion
    • Humanoid
    • Learning
    • Support vector machine

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Theoretical Computer Science

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  • Cite this

    Matsuyama, Y., & Nishida, Y. (2008). HMM-embedded Bayesian network for heterogeneous command integration: Applications to biped humanoid operation over the network. In 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings (pp. 138-145) https://doi.org/10.1145/1456223.1456256