Personalized Motion Guidance Framework for Athlete-Centric Coaching

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.

💡 Research Summary

The paper addresses a central problem in sports science: the disconnect between group‑level findings from controlled experiments and the need for individualized coaching that respects each athlete’s unique movement patterns. To bridge this gap, the authors introduce the Personalized Motion Guidance Framework (PMGF), a generative‑AI system that creates athlete‑specific motion‑refinement guides. The core of PMGF is a “vertical” autoencoder that processes three‑dimensional motion capture sequences frame by frame, encoding each frame into a latent vector that captures the athlete’s personal biomechanical signature. Because each athlete’s latent space is learned separately, the representation preserves individual nuances while remaining compatible with a shared decoder that can reconstruct realistic motion.

Two manipulation strategies are explored. The first, called observational learning via interpolation, linearly blends the learner’s latent vector (L) with a target expert vector (T) using a parameter α∈