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Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network.

Chien-Chang Chen1, Cheng-Shian Lin1, Yen-Ting Chen1

  • 1Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan.

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Summary

This study introduces a video-based system using graph-matching networks to analyze rowing posture similarity. It accurately pairs rowers for improved team performance by analyzing joint point data.

Keywords:
OpenPosegraph neural network

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Area of Science:

  • Sports Science
  • Computer Vision
  • Biomechanics

Background:

  • Consistent rowing strokes are crucial for optimal team performance in rowing competitions.
  • Current motion analysis methods often rely on wearable sensors, causing inconvenience for athletes.

Purpose of the Study:

  • To develop a novel video-based system for analyzing rowing posture similarity between paired rowers.
  • To improve team performance by effectively pairing rowers based on synchronized movements.

Main Methods:

  • Utilized the OpenPose system to detect human joint points from video footage.
  • Applied a graph embedding model (GEM) and a graph-matching network to analyze posture similarities.
  • Incorporated time-period similarity processing for enhanced accuracy in pairing.

Main Results:

  • The proposed system successfully analyzed similarities in rowing postures between paired athletes.
  • Accurate pairing was achieved by detecting consistent starting points in rowing postures.
  • The 2D graph-embedding model (GEM) with time-period similarity processing yielded the best pairing results.

Conclusions:

  • A novel video-based system effectively analyzes rowing posture synchronization using graph-matching networks.
  • This approach offers a non-invasive alternative to wearable sensors for performance analysis.
  • The system demonstrates potential for optimizing rower pairings and enhancing team performance.