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

doi: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

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	767-778
Number of pages	12
Volume	5768 LNCS
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-04274-4_79
Publication status	Published - 2009
Event	19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol Duration: 2009 Sept 14 → 2009 Sept 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5768 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	19th International Conference on Artificial Neural Networks, ICANN 2009
City	Limassol
Period	09/9/14 → 09/9/17

Keywords

Ambulating PC
Bayesian network
Committee SVM array
HMM
Multimodal beliefs

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-642-04274-4_79

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 et al.
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 proceeding › Conference 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). doi: 10.1007/978-3-642-04274-4_79

Matsuyama, Yasuo ; Matsushima, Fumiya ; Nishida, Youichi et al. / 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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Multimodal belief integration by HMM/SVM-embedded bayesian network: Applications to ambulating pc operation by body motions and brain signals

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