| 研究実績の概要 |
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. Our computational framework analyzes complex dance motions, understanding motor mechanisms in dance genres, providing insights into movement dynamics/music relationships, with applications in dance research, performance analysis, training, and injury prevention. We simulated deep reinforcement learning models reproducing dance skills, analyzing them kinematically/kinetically, demonstrating our methods' effectiveness for studying AI-generated dance movements.
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