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

Institute of Science Tokyo, CHI '26

Overview of SoleCoach. SoleCoach generates coaching feedback directly from insole sensor data (foot pressure and IMUs) using a multimodal large language model, without requiring external cameras or body-mounted motion capture. We collected a dataset of 26 alpine skiers with 387 expert coaching comments and built a training and generation pipeline to enable accurate and context-aware feedback.

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.

Overview of the proposed insole-based coaching system. The video introduces the background of skiing coaching, the collected foot pressure and IMU dataset, and the overall pipeline for generating coaching feedback using a large language model.

BibTeX

@inproceedings{hirano2026solecoach,
  author    = {Hirano, Toshihiro and Yoshihara, Hitoshi and Peng, Yichen and Liao, Chen-Chieh and Wu, Erwin and Koike, Hideki},
  title     = {SoleCoach: Sole Pressure and IMU-based MLLMs for Skill Coaching},
  booktitle = {Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)},
  year      = {2026},
  address   = {Barcelona, Spain},
  publisher = {Association for Computing Machinery},
  doi       = {10.1145/3772318.3791181}
}