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

Research Project

Project/Area Number 21K17789
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 61020:Human interface and interaction-related
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
Project Status Granted (Fiscal Year 2023)
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)
Keywordsspeed of motor learning / manipulability ellipsoid / target distribution / muscle co-contraction / stroke rehabilitation / manipulability ellipse / motor learning
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.

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.

Report

(3 results)
  • 2023 Research-status Report
  • 2022 Research-status Report
  • 2021 Research-status Report
  • Research Products

    (8 results)

All 2024 2023 2022 2021

All Journal Article (3 results) (of which Int'l Joint Research: 3 results,  Peer Reviewed: 3 results,  Open Access: 3 results) Presentation (5 results) (of which Int'l Joint Research: 4 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

    • Related Report
      2023 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] EMG space similarity feedback promotes learning of expert-like muscle activation patterns in a complex motor skill2023

    • Author(s)
      Barradas Victor R.、Cho Woorim、Koike Yasuharu
    • Journal Title

      Frontiers in Human Neuroscience

      Volume: 16 Pages: 805867-805867

    • DOI

      10.3389/fnhum.2022.805867

    • Related Report
      2022 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Design of an isometric end-point force control task for EMG normalization and muscle synergy extraction from the upper limb without MVC2022

    • Author(s)
      W Cho, VR Barradas, N Schweighofer, Y Koike
    • Journal Title

      Frontiers in Human Neuroscience

      Volume: 160 Pages: 0-0

    • Related Report
      2021 Research-status Report
    • 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
    • Related Report
      2023 Research-status Report
    • 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
    • Related Report
      2023 Research-status Report
  • [Presentation] Control of interaction torques during single-joint arm movements in stroke survivors2022

    • Author(s)
      Darmon Y, Loeb GE, Barradas VR, Winstein CJ, Rosario ER, Schweighofer N
    • Organizer
      Society for Neuroscience
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] Necessary plastic processes to account for the Brunnstrom stages of recovery post-stroke in isometric arm tasks2022

    • Author(s)
      Lee K, Barradas VR, Schweighofer N
    • Organizer
      Society for Neuroscience
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] Computational limits on the speed of learning internal models for arm reaching2021

    • Author(s)
      Victor R. Barradas
    • Organizer
      Society for Neuroscience Global Connectome
    • Related Report
      2021 Research-status Report
    • Int'l Joint Research

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Published: 2021-04-28   Modified: 2024-12-25  

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