2020 Fiscal Year Final Research Report
Development of a motor learning support algorithm based on the functional brain network analysis during cooperative motor learning
Project/Area Number |
17KK0064
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Research Category |
Fund for the Promotion of Joint International Research (Fostering Joint International Research)
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Allocation Type | Multi-year Fund |
Research Field |
Developmental mechanisms and the body works
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Research Institution | Tokyo University of Agriculture and Technology |
Principal Investigator |
KONDO Toshiyuki 東京農工大学, 工学(系)研究科(研究院), 教授 (60323820)
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Project Period (FY) |
2018 – 2020
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Keywords | ロボット / 協調運動学習 / エージェント / 脳波解析 |
Outline of Final Research Achievements |
When learning a new skill through unknown environment, it is helpful to have an expert guidance. It is fundamentally based on the mutual interactions. From the perspective of the beginner, one needs to face dual unknown dynamics of environment and motor coordination of the expert. This inevitably involves the process of adaptation to the partner. Within a cooperative visuo-haptic motor task, we asked the novice participants to control the virtual mass toward the specified target under unknown external force field as an individual or with an expert or another novice. Experimental results suggested; (1) Peer-to-peer interactions among beginners to achieve a common goal enhanced the motor learning most, (2) Individuals practicing on their own showed the better motor learning than practicing under the expert’s guidance. Regarding the adaptability, peer-to-peer interactions induced higher adaptability to a new partner than the novice-to-expert interactions.
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Free Research Field |
知能情報学
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Academic Significance and Societal Importance of the Research Achievements |
ロボットリハビリテーションのように人がロボットと協調して未知なる運動課題を経験・学習する場合,学習者は環境(課題)と協調相手(ロボット)という二つの未知ダイナミクスが重ね合わされた対象の下で運動学習する必要がある.本研究の結果,まずロボットのダイナミクスに習熟した後に二重ダイナミクスを経験する手順をとることで,学習後に両者を分離して獲得できたことから,ロボットリハビリテーションにおいても,ロボットの支援戦略を先に体得させた後にリハビリ課題の難易度を高めることが有効であると考えられる.また,運動下の脳波解析法はリハビリ下の患者の状態を類型化して運動支援戦略を変更できる可能性がある.
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