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Related Experiment Video

Updated: May 31, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Expert Comment Generation Considering Sports Skill Level Using a Large Multimodal Model with Video and

Tatsuki Seino¹, Naoki Saito², Takahiro Ogawa³

¹Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This study introduces a new method for generating personalized sports feedback using a Large Multimodal Model (LMM) and motion analysis. It tailors expert comments to athlete skill levels for improved performance.

Keywords:

expert comment generation large multimodal model spatial-temporal attention graph convolutional network sports skill level

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Last Updated: May 31, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Published on: May 7, 2019

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Published on: December 15, 2023

Area of Science:

Sports Science
Artificial Intelligence
Biomechanics

Background:

Personalized skill assessment and feedback are vital for athletic improvement.
Existing methods often overlook spatial-temporal movement data and skill-level tailoring in feedback generation.

Purpose of the Study:

To develop a novel approach for generating skill-level-aware expert comments for athletes.
To enhance the personalization and actionability of feedback in sports training.

Main Methods:

Utilized a Spatial-Temporal Attention Graph Convolutional Network (STA-GCN) to extract motion features and classify skill levels.
Integrated skill level classification and motion features into a Large Multimodal Model (LMM) for comment generation.
Combined video analysis with spatial-temporal motion dynamics for comprehensive feedback.

Main Results:

The proposed method successfully generates detailed, context-specific expert comments.
Feedback is tailored to the learner's specific skill level, addressing limitations of prior research.
The integration of motion features enhances the LMM's ability to provide actionable insights.

Conclusions:

The novel approach effectively generates expert comments, offering valuable guidance for athletes.
This method represents a significant advancement in personalized sports training and skill acquisition.
The skill-level-aware feedback system has broad applicability across various sports and skill levels.