Elucidation of the adaptive mechanism of intricate human motion imitated by deep reinforcement learning

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K20519
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0403:Biomedical engineering and related fields
• Research Institution: National Institute of Advanced Industrial Science and Technology
• Principal Investigator: Shen Keli 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 産総研特別研究員 (80965179)
• Project Period (FY): 2022-08-31 – 2024-03-31
• Keywords: Dance adpative skills / Motor coordination / Sports biomechanics / Beat detection / Complexity analysis / Deep RL / Computational modeling / Beat-aligned synergies

Outline of Final Research Achievements

We developed a TD-PCA approach to extract beat-aligned motor synergies from street dance datasets, leveraging the first synergy to improve kinematic beat detection and enable accurate beat alignment with music. The enhancement was verified through cross-validation. We simulated deep reinforcement learning models reproducing dance skills, analyzing them kinematically/kinetically, demonstrating our methods' effectiveness for studying AI- generated dance movements.

Free Research Field

神経計算、ロボット工学、バイオメカニクス

Academic Significance and Societal Importance of the Research Achievements

我々の計算フレームワークは、複雑なダンスモーションを分析し、ダンスジャンルの運動メカニズムを理解し、動きのダイナミクスと音楽の関係についての洞察を提供し、ダンス研究、パフォーマンス分析、トレーニング、怪我の予防に応用できる。