A critical challenge in contemporary sports science lies in filling the gap between group-level insights derived from controlled hypothesis-driven experiments and the real-world need for personalized coaching tailored to individual athletes' unique movement patterns. This study developed a Personalized Motion Guidance Framework (PMGF) to enhance athletic performance by generating individualized motion-refinement guides using generative artificial intelligence techniques. PMGF leverages a vertical autoencoder to encode motion sequences into athlete-specific latent representations, which can then be directly manipulated to generate meaningful guidance motions. Two manipulation strategies were explored: (1) smooth interpolation between the learner's motion and a target (e.g., expert) motion to facilitate observational learning, and (2) shifting the motion pattern in an optimal direction in the latent space using a local optimization technique. The results of the validation experiment with data from 51 baseball pitchers revealed that (1) PMGF successfully generated smooth transitions in motion patterns between individuals across all 1,275 pitcher pairs, and (2) the features significantly altered through PMGF manipulations reflected known performance-enhancing characteristics, such as increased stride length and knee extension associated with higher ball velocity, indicating that PMGF induces biomechanically plausible improvements. We propose a future extension called general-PMGF to enhance the applicability of this framework. This extension incorporates bodily, environmental, and task constraints into the generation process, aiming to provide more realistic and versatile guidance across diverse sports contexts.

翻译：当代体育科学面临的一个关键挑战在于，如何弥合从受控的假设驱动实验中获得的群体层面洞察与现实中针对运动员独特运动模式进行个性化指导的需求之间的差距。本研究开发了一个个性化运动指导框架，旨在通过生成式人工智能技术生成个体化的运动优化指导，以提升运动表现。该框架利用垂直自编码器将运动序列编码为运动员特定的潜在表征，随后可直接操纵这些表征以生成有意义的指导性运动。我们探索了两种操纵策略：(1) 在学员运动与目标（如专家）运动之间进行平滑插值，以促进观察学习；(2) 使用局部优化技术在潜在空间中沿最优方向移动运动模式。基于51名棒球投手数据的验证实验结果表明：(1) 该框架成功在所有1,275对投手组合之间生成了个体间运动模式的平滑过渡；(2) 通过框架操纵显著改变的特征反映了已知的提升表现的特征，例如与更高球速相关的步幅增加和膝关节伸展，表明该框架能够诱导出生物力学上合理的改进。我们提出了一个名为通用个性化运动指导框架的未来扩展方向，以增强该框架的适用性。该扩展将身体、环境和任务约束纳入生成过程，旨在为多样化的运动场景提供更真实、更通用的指导。