Multimodal Belief Integration by HMM/SVM-Embedded Bayesian Network: Applications to Ambulating PC Operation by Body Motions and Brain Signals

Authors:
Yasuo Matsuyama;Fumiya Matsushima;Youichi Nishida;Takashi Hatakeyama;Nimiko Ochiai;Shogo Aida
Affiliations:
Department of Computer Science and Engineering, Waseda University, Tokyo, Japan 169-8555;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan 169-8555;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan 169-8555;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan 169-8555;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan 169-8555;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan 169-8555
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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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.