Multimodal belief integration by HMM/SVM-embedded bayesian network: Applications to ambulating pc operation by body motions and brain signals

Yasuo Matsuyama, Fumiya Matsushima, Youichi Nishida, Takashi Hatakeyama, Nimiko Ochiai, Shogo Aida

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

    3 Citations (Scopus)

    Abstract

    Methods to integrate multimodal beliefs by Bayesian Networks (BNs) comprising Hidden Markov Models (HMMs) and Support Vector Machines (SVMs) are presented. The integrated system is applied to the operation of ambulating PCs (biped humanoids) across the network. New features in this paper are twofold. First, the HMM/SVM-embedded BN for the multimodal belief integration is newly presented. Its subsystem also has a new structure such as a committee SVM array. Another new fearure is with the applications. Body and brain signals are applied to the ambulating PC operation by using the recognition of multimodal signal patterns. The body signals here are human gestures. Brain signals are either HbO2 of NIRS or neural spike trains. As for such ambulating PC operation, the total system shows better performance than HMM and BN systems alone.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages767-778
    Number of pages12
    Volume5768 LNCS
    EditionPART 1
    DOIs
    Publication statusPublished - 2009
    Event19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol
    Duration: 2009 Sep 142009 Sep 17

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 1
    Volume5768 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other19th International Conference on Artificial Neural Networks, ICANN 2009
    CityLimassol
    Period09/9/1409/9/17

    Fingerprint

    Bayesian networks
    Hidden Markov models
    Bayesian Networks
    Markov Model
    Support vector machines
    Brain
    Support Vector Machine
    Motion
    Near-infrared Spectroscopy
    Integrated System
    Gesture
    Spike
    Subsystem
    Integrate
    Beliefs

    Keywords

    • Ambulating PC
    • Bayesian network
    • Committee SVM array
    • HMM
    • Multimodal beliefs

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Matsuyama, Y., Matsushima, F., Nishida, Y., Hatakeyama, T., Ochiai, N., & Aida, S. (2009). Multimodal belief integration by HMM/SVM-embedded bayesian network: Applications to ambulating pc operation by body motions and brain signals. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 5768 LNCS, pp. 767-778). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5768 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-04274-4_79

    Multimodal belief integration by HMM/SVM-embedded bayesian network : Applications to ambulating pc operation by body motions and brain signals. / Matsuyama, Yasuo; Matsushima, Fumiya; Nishida, Youichi; Hatakeyama, Takashi; Ochiai, Nimiko; Aida, Shogo.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5768 LNCS PART 1. ed. 2009. p. 767-778 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5768 LNCS, No. PART 1).

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

    Matsuyama, Y, Matsushima, F, Nishida, Y, Hatakeyama, T, Ochiai, N & Aida, S 2009, Multimodal belief integration by HMM/SVM-embedded bayesian network: Applications to ambulating pc operation by body motions and brain signals. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 5768 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5768 LNCS, pp. 767-778, 19th International Conference on Artificial Neural Networks, ICANN 2009, Limassol, 09/9/14. https://doi.org/10.1007/978-3-642-04274-4_79
    Matsuyama Y, Matsushima F, Nishida Y, Hatakeyama T, Ochiai N, Aida S. Multimodal belief integration by HMM/SVM-embedded bayesian network: Applications to ambulating pc operation by body motions and brain signals. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 5768 LNCS. 2009. p. 767-778. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-04274-4_79
    Matsuyama, Yasuo ; Matsushima, Fumiya ; Nishida, Youichi ; Hatakeyama, Takashi ; Ochiai, Nimiko ; Aida, Shogo. / Multimodal belief integration by HMM/SVM-embedded bayesian network : Applications to ambulating pc operation by body motions and brain signals. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5768 LNCS PART 1. ed. 2009. pp. 767-778 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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    abstract = "Methods to integrate multimodal beliefs by Bayesian Networks (BNs) comprising Hidden Markov Models (HMMs) and Support Vector Machines (SVMs) are presented. The integrated system is applied to the operation of ambulating PCs (biped humanoids) across the network. New features in this paper are twofold. First, the HMM/SVM-embedded BN for the multimodal belief integration is newly presented. Its subsystem also has a new structure such as a committee SVM array. Another new fearure is with the applications. Body and brain signals are applied to the ambulating PC operation by using the recognition of multimodal signal patterns. The body signals here are human gestures. Brain signals are either HbO2 of NIRS or neural spike trains. As for such ambulating PC operation, the total system shows better performance than HMM and BN systems alone.",
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