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2023 Fiscal Year Research-status Report

Enhancing motor skill learning by manipulating extrinsic and intrinsic components of the motor task

Research Project

Project/Area Number 21K17789
Research InstitutionTokyo Institute of Technology

Principal Investigator

Barradas Victor  東京工業大学, 科学技術創成研究院, 特任助教 (70883908)

Project Period (FY) 2021-04-01 – 2025-03-31
Keywordsspeed of motor learning / manipulability ellipsoid / target distribution / muscle co-contraction / stroke rehabilitation
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.

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.

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.

Causes of Carryover

I requested a one-year extension of the project as some results remain that need to be written up and published. The budget will be used for publication and conference expenses.

  • Research Products

    (3 results)

All 2024 2023

All Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (2 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks2024

    • Author(s)
      Barradas Victor R.、Koike Yasuharu、Schweighofer Nicolas
    • Journal Title

      Neural Networks

      Volume: 170 Pages: 376~389

    • DOI

      10.1016/j.neunet.2023.10.049

    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Hyper-Adaptability for Overcoming Body-Brain Dysfunction: Integration of Empirical and System Theoretical Approaches2023

    • Author(s)
      An Qi, Ota Jun, Imamizu Hiroshi, Barradas Victor R, Bian Lingbin
    • Organizer
      45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    • Int'l Joint Research
  • [Presentation] The role of manipulability ellipsoids in the speed of learning inverse models of arm reaching2023

    • Author(s)
      Barradas Victor R., Schweighofer Nicolas, Koike Yasuharu
    • Organizer
      第17回Motor Control研究会

URL: 

Published: 2024-12-25  

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