| 研究課題/領域番号 |
21K17789
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61020:ヒューマンインタフェースおよびインタラクション関連
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| 研究機関 | 東京工業大学 |
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研究代表者 |
Barradas Victor 東京工業大学, 科学技術創成研究院, 特任助教 (70883908)
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| 研究期間 (年度) |
2021-04-01 – 2024-03-31
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| 研究課題ステータス |
交付 (2022年度)
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| 配分額 *注記 |
2,860千円 (直接経費: 2,200千円、間接経費: 660千円)
2023年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2022年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2021年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
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| キーワード | speed of motor learning / manipulability ellipse / target distribution / muscle co-contraction / motor learning |
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| 研究開始時の研究の概要 |
Learning motor skills can be time-consuming. In this project we will find methods to enhance learning by reshaping task goals and body aspects like posture or muscle activations. This research will be useful for the training of athletes and technicians, and the rehabilitation of motor impairments.
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| 研究実績の概要 |
The main objective of this project is to identify intrinsic and extrinsic factors that can be exploited to increase the speed of learning a motor task. Following the success of my computational model to enhance the speed of learning in an isometric task, I have now applied the model to a more complex dynamic reaching task. In this task, the factors that influence the speed of learning are the dynamic manipulability ellipse of the arm, and the distribution of arm states during the task. This allows to make predictions about the effect of variables such as arm posture and arm mass in the speed of learning a dynamic task. In parallel, I have also developed a system that allows the directed learning of desired muscle activations via EMG-biofeedback in a virtual polishing task.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
My research is progressing smoothly according to the timeframe that I initially proposed. However, the publication of the results is taking significantly longer than expected due to reasons out of my control. According to the editors of the journals I have submitted to, it has taken an unusually long time to secure reviewers for the peer review process for my studies.
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| 今後の研究の推進方策 |
As defined in the project timeline, the current phase of the project is 2) the kinematic/dynamic phase. I have developed the computational theory for this phase, and now plan to go on to the experimental phase. For the remaining year in the project, I plan to combine the experimental aspect of the kinematic/dynamic phase with the next phase of the project: 3) tool design phase. This is because the computational principles of the model allow to work on both phases in parallel.
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