2026

CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems

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

Toshihiro Hirano, Hitoshi Yoshihara, Yichen Peng, Chen-Chieh Liao, Erwin Wu, Hideki Koike

Institute of Science Tokyo, Tokyo, Japan

Keywords

skiing, coaching, 3d body pose, foot pressure, insole sensor

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.

Moticon's Summary

In this publication, researchers developed SoleCoach, an AI-driven system for alpine skiing coaching. Moticon OpenGo sensor insoles were used to collect foot pressure distribution and IMU data at 50 FPS. This data was leveraged to estimate 3D body posture and feed a multimodal large language model (mllm) to generate actionable training feedback. The integration of Moticon insoles provided an effective, camera-free approach for assessing athletic movement.

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