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Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology
1School of Science of Physical Culture and Sports, Kunsan University, Kunsan, 54150, Korea.
Scientific Reports
|April 1, 2024
Summary
This study integrates boxer psychology with AI to create an advanced boxing action recognition model. The novel BERT-ResNet approach enhances classification accuracy by considering psychological states and physical actions.
Area of Science:
- Sports Science
- Artificial Intelligence
- Psychology
Background:
- Understanding boxer psychology is crucial for performance.
- Existing boxing action recognition models lack psychological integration.
Purpose of the Study:
- To investigate the psychological states of boxers before competition.
- To develop an AI-driven boxing action classification and recognition model incorporating psychological data.
Main Methods:
- Systematic measurement of psychological dimensions (anxiety, self-confidence, etc.) using scale surveys.
- Construction of a novel BERT-ResNet model fusing psychological data with action characteristics.
- Performance evaluation against traditional models using loss value, accuracy, and F1 score.
Main Results:
- The proposed BERT-ResNet model significantly outperforms traditional models.
- Achieved a high accuracy of 96.86% in boxing action classification.
- Demonstrated the effectiveness of integrating psychological insights into AI models.
Conclusions:
- The study successfully developed an AI model for boxing action classification that considers boxer psychology.
- This model offers robust support for psychological training and action analysis in boxing.
- Highlights the synergy between psychology and deep learning for enhanced sports performance analysis.
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