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Application of Unsupervised Migration Method Based on Deep Learning Model in Basketball Training.

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  • 1Physical Education Department, Dongguan City College, Dongguan 523419, Guangdong, China.

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Summary

This study introduces a deep learning system for real-time basketball training analysis. The advanced machine learning model achieved 97.7% accuracy in recognizing player actions and correcting errors.

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Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • The Chinese sports industry requires scientific methods to enhance athlete training efficiency.
  • Machine learning offers a promising avenue for optimizing sports training processes.

Purpose of the Study:

  • To investigate the application of deep learning for real-time analysis of basketball sports data.
  • To develop a system for recognizing and analyzing basketball stance actions.

Main Methods:

  • Utilizing scientific reporting, audio/video analysis, experimental research, and mathematical statistics.
  • Implementing a two-segment system: bottom-up joint location estimation and posture sequence extraction.
  • Employing a Support Vector Machine (SVM) algorithm with a deep learning space-time graph for action classification.

Main Results:

  • The system accurately recognizes and extracts basketball activities from segmented posture sequences.
  • The deep learning approach demonstrated a 97.7% accuracy rate in evaluating basketball training data.
  • The auxiliary method effectively corrected player errors and improved training outcomes compared to standard methods.

Conclusions:

  • Deep learning provides a scientifically validated approach to enhance basketball training.
  • The developed system aids players in rectifying technical errors and improving overall performance.
  • Accurate real-time analysis of sports data can significantly boost athlete development.