SoleCoach: Sole Pressure and IMU-based MLLMs for Skill Coaching

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Authors: Teruaki Hirano, Hitoshi Yoshihara, Yichen Peng, Chen-Chieh Liao, Erwin Wu, Hideki Koike
Cited by: 1
References: 50

Abstract

In sports training, individualized skill assessment and feedback are essential for athletes to master complex movements and enhance performance. Existing approaches for generating coaching comments primarily rely on externally captured pose information, which limits their applicability in outdoor sports such as skiing that involve large-scale movement. To address this challenge, we propose a method for presenting athletes’ postures and generating coaching feedback solely based on foot pressure and IMU data collected from insole sensors. In our approach, a large language model directly interprets foot pressure signals to provide actionable coaching, thereby supporting independent practice. Through model evaluation and user studies, we demonstrate that the proposed method generates expert-level feedback and outperforms pose-based approaches. Furthermore, the user study shows that the feedback helps athletes identify body parts requiring correction and enhances their motivation for training.

Fields of Study & Subjects

Human Pose and Action Recognition Sports Performance and Training Winter Sports Injuries and Performance

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–17
Publication date
13 April 2026