Bio-signal integration for humanoid operation: gesture and brain signal recognition by HMM/SVM-embedded BN

Authors:
Yasuo Matsuyama;Fumiya Matsushima;Youichi Nishida;Takashi Hatakeyama;Koji Sawada;Takatoshi Kato
Affiliations:
Department of Computer Science and Engineering, Waseda University, Tokyo, Japan;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan;Department of Computer Science and Engineering, Waseda University, Tokyo, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

Citing 2
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Multimodal human-humanoid interaction using motions, brain nirs and spike trains

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Abstract

Joint recognition of bio-signals emanated from human(s) is discussed. The bio-signals in this paper include camera-captured gestures and brain signals of hemoglobin change ΔO2Hb. The recognition of the integrated data is applied to the operation of a biped humanoid. Hidden Markov Models (HMMs) and Support Vector Machines (SVMs) undertake the first stage recognition of individual signal. These subsystems are regarded as soft command issuers. Then, such low-level commands are integrated by a Bayesian Network (BN). Therefore, the total system is a novel HMM/SVM-embedded BN. Using this new recognition system, human operators can control the biped humanoid through the network by realizing more motion classes than methods of HMM-alone, SVM-alone and BN-alone.