Wushu Routine Movement and Diagnosis Based on Deep Learning and Symmetric Difference Algorithm

Area of Science:

• Sports Science
• Artificial Intelligence
• Algorithm Analysis

Background:

• Wushu is a traditional Chinese martial art and popular sport.
• Increasingly competitive Wushu requires precise routine analysis for performance enhancement.
• Traditional analysis methods lack quantitative indicators for technical training.

Purpose of the Study:

• To investigate the analysis and diagnosis of Wushu routines.
• To apply deep learning and symmetric difference algorithms for Wushu routine assessment.
• To identify key factors influencing Wushu athletes' competition performance.

Main Methods:

• Utilized deep learning algorithms for complex pattern recognition in Wushu movements.
• Employed the symmetric difference algorithm to quantify routine accuracy and identify deviations.
• Integrated both algorithms to provide a comprehensive analysis of Wushu routines.

Main Results:

• The study established a novel method for quantitative analysis of Wushu routines.
• Deep learning and symmetric difference algorithms effectively diagnosed technical aspects of Wushu movements.
• Wushu routine proficiency was identified as the most critical factor for competition success.

Conclusions:

• The level of Wushu routines has the greatest influence on competition performance.
• The developed approach offers valuable quantitative data for Wushu training and coaching.
• Technological integration in Wushu analysis enhances athlete development and competitive outcomes.