| Project/Area Number |
15300077
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Waseda University |
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Principal Investigator |
MATSUYAMA Yasuo Waseda University, Department of Computer Science, Professor, 理工学術院, 教授 (60125804)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAJIMA Tatsuo Waseda University, Department of Computer Science, Professor, 理工学術院, 教授 (10251977)
KATSUMATA Naoto Waseda University, Department of Computer Science, Research Assistant, 理工学術院, 助手 (90367045)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥16,800,000 (Direct Cost: ¥16,800,000)
Fiscal Year 2004: ¥7,100,000 (Direct Cost: ¥7,100,000)
Fiscal Year 2003: ¥9,700,000 (Direct Cost: ¥9,700,000)
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| Keywords | humanoid motion control / motion recognition / unification of human motion and humanoid / machine independence / network environment / independent component analysis / 脳情報 / ヒューマノイド / エージェント / 一般化された対数 |
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| Research Abstract |
This grant was applied to the unification of the human movement, the animation, and the humanoid over the computer network. The injection of the brain signal to the humanoid is another objective. The following results were obtained.
(1)This research group was able to find the method to unify the human body motion, the cartoon character and the humanoid over the network environment. The designed system includes the recognition of human body motions. The system finds the body motion's abstract expression in a language level. The APNNA Best Paper Award for Application Oriented Research was given in 2004 to the research paper on this method,
(2)Because of the abstraction to the language level, the human body motion can be transmitted and used as a command to different humanoids and other robots. In other words, the machine independence between humanoids was obtained.
(3)It is not yet possible to obtain granular commands from brain signals because the resolution is still low by the contemporary technology. But, this study found that the abstract commands of (2)can be combined with the overwriting urgent signal from the brain. This method was found useful.
(4)For the estimation method of active states of the brain, this study developed the f-ICA which includes the conventional ICA method as a special case. The new method is applicable to a wide class of information sources ; not limited to the brain signal. These targets include digital images and DNA segments, for which successful results were obtained.
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