| Project/Area Number |
21K17789
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61020:Human interface and interaction-related
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
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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| Project Period (FY) |
2021-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2023: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | speed of motor learning / manipulability ellipsoid / target distribution / muscle co-contraction / stroke rehabilitation / manipulability ellipse / motor learning |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
The main objective of this project is to identify factors that can be exploited to increase the speed of learning a motor task. I previously developed a computational model that makes predictions about the speed of learning in isometric and dynamic arm reaching tasks. I have now extended the computational model to include muscle-actuated joints. The model predicts that the levels of muscle co-contraction during an arm-reaching task influence the speed of learning. This offers an alternative explanation for existing experimental observations.
Additionally, I have started to explore applications for the proposed computational framework. In stroke rehabilitation protocols based on myo-electric training, our framework shows, theoretically, that muscle pairs for training can be optimally selected to maximize the speed of rehabilitation, or learning. These pairs differ from the muscle pairs usually chosen in practice. Therefore, our model could be used to improve these protocols.
Finally, I have also developed a theoretical framework to model tasks involving the EMG space similarity feedback, which we have shown experimentally to allow subjects to learn expert-like muscle activation patterns. This framework will allow me to decrease the experimental workload for improving the EMG feedback.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
My research has mostly progressed according to the timeframe that I initially proposed. However, the publication of the results is slightly delayed due to delays in the writing of results and reasons out of my control, such as the internal operation of journals.
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| Strategy for Future Research Activity |
As this is the last year of the project, the future plan for progress mostly involves writing up and presenting theoretical and experimental results that were obtained in the previous year.
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