Personalized Motion Guidance Framework for Athlete-Centric Coaching
This addresses the problem of providing personalized coaching for athletes by bridging group-level insights with individual needs, though it appears incremental as it builds on existing generative AI techniques.
The study tackled the challenge of personalizing sports coaching by developing a Personalized Motion Guidance Framework (PMGF) that uses generative AI to create individualized motion-refinement guides for athletes, with validation on 51 baseball pitchers showing it generated smooth transitions across all 1,275 pairs and induced biomechanically plausible improvements like increased stride length and knee extension linked to higher ball velocity.
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.