| Project/Area Number |
11555113
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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 | 展開研究 |
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| Research Field |
Control engineering
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| Research Institution | HIROSHIMA UNIVERSITY |
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Principal Investigator |
TSUJI Toshio Hiroshima University, Graduate School of Engineering, Professor, 大学院・工学研究科, 教授 (90179995)
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| Co-Investigator(Kenkyū-buntansha) |
KATO Takashi Graduate School for International Development and Cooperation, Research Associate, 大学院・国際協力研究科, 助手 (00284232)
OTSUKA Akira Hiroshima Prefectural college of Health Sciences, Professor, 理学療法学科, 教授 (50280194)
KANEKO Makoto Hiroshima University, Graduate School of Engineering, Professor, 大学院・工学研究科, 教授 (70224607)
HARADA Kensuke National Institute of Advanced Industrial Science and Technology, 研究員 (50294533)
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| Project Period (FY) |
1999 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥13,800,000 (Direct Cost: ¥13,800,000)
Fiscal Year 2002: ¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2001: ¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2000: ¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 1999: ¥4,000,000 (Direct Cost: ¥4,000,000)
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| Keywords | Support system / robot / EMG / neural network / meal assistance / interface / the handicapped / 筋電位 / インタフェース / 義手 |
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| Research Abstract |
The purpose of this research is to develop a new support system consisting of an interface device and a human assisting manipulator for the physically handicapped. The obtained results throughout this research can be summarized as follows:
(1) A new neural network called Recurrent Log-Linearized Gaussian Mixture Network (R-LLGMN) was proposed to realize an adaptive learning ability to a variety of EMG signals measured from the handicapped. The R-LLGMN was developed on the basis of the Gaussian mixture model and the hidden Markov model, and it was shown from experiments that the R-LLGMN has higher pattern recognition abilities than conventional neural networks.
(2) Using R-LLGMN, an algorithm to recognize motions of forearm and hand from measured EMG signals of operators was developed to realize the EMG-based interface.
(3) A new robot control system manipulated through EMG signals measured from the operator was then developed, and incorporated into a human assisting manipulator. Also, to use it in daily activities, specific knowledge concerning about each task is modeled and incorporated into the control system of the manipulator.
(4) As an example of application of the developed EMG-controlled interface, a new robot system for meal assistance is developed. From experimental results by the physically handicapped, a possibility to come into practical use was shown.
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