Modeling of hyper-adaptability to altered musculoskeletal system

2023 Fiscal Year Final Research Report

• Project Area: Hyper-adaptability for overcoming body-brain dysfunction: Integrated empirical and system theoretical approaches
• Project/Area Number: 19H05728
• Research Category: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
• Allocation Type: Single-year Grants
• Review Section: Complex systems
• Research Institution: Tokyo Institute of Technology
• Principal Investigator: Koike Yasuharu 東京工業大学, 科学技術創成研究院, 教授 (10302978)
• Co-Investigator(Kenkyū-buntansha): 舩戸 徹郎 電気通信大学, 大学院情報理工学研究科, 准教授 (40512869)
• Project Period (FY): 2019-06-28 – 2024-03-31
• Keywords: 腱付け替え / 仮想手術 / 筋骨格系モデル / 筋シナジー

Outline of Final Research Achievements

In the construction of a musculoskeletal model to simulate muscle rearrangement in monkeys, we analyzed bacterial activity data obtained from monkey experiments and explained the adaptation process that occurred during the recovery process as changes in muscle synergy. In addition, a musculoskeletal model was created for computational simulation, and a model was created to the extent that muscle activity could be explained. In computational modeling research, a musculoskeletal system model was constructed to mathematically reproduce the physical transformation caused by muscle rearrangement, and the modes of learning and control to adapt to the force field were clarified. Furthermore, we developed a theoretical framework to explain the learning rate.

Free Research Field

計算論的神経科学

Academic Significance and Societal Importance of the Research Achievements

運動学習・適応過程を筋シナジーを元にして解析する手法を確立した。また、計算機シミュレーションのための筋骨格系モデルを構築し、実際の筋活動や適応過程、その内部構造を明らかにした。さらに、運動学習の速度を説明できる理論的な枠組みを構築した。
これらの結果は、運動学習やリハビリテーションにおいて、適応の難しさの定量化、学習時間の短縮、効率的な学習を促すフィードバック情報の作成などに利用できる。これにより、効果的なリハビリテーション手法の開発などにも応用が可能となる。