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

Yasuo Matsuyama*, Youichi Nishida

*この研究の対応する著者

    研究成果

    3 被引用数 (Scopus)

    抄録

    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).

    本文言語English
    ホスト出版物のタイトル5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings
    ページ138-145
    ページ数8
    DOI
    出版ステータスPublished - 2008
    イベント5th International Conference on Soft Computing As Transdisciplinary Science and Technology, CSTST '08 - Cergy-Pontoise
    継続期間: 2008 10 282008 10 31

    Other

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

    ASJC Scopus subject areas

    • 計算理論と計算数学
    • 理論的コンピュータサイエンス

    フィンガープリント

    「HMM-embedded Bayesian network for heterogeneous command integration: Applications to biped humanoid operation over the network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

    引用スタイル