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RETRACTED: Human swimming posture recognition combining improved 3D convolutional network and attention residual

Menglu Li¹, Changfeng Ning¹

¹Institute of Physical Education, Yancheng Institute of Technology, Yancheng, China.

|December 2, 2025

Summary

This study introduces a novel human swimming posture recognition model using a 3D convolutional neural network. The model achieves high accuracy (95%) and efficiency, crucial for improving training and reducing injuries.

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Area of Science:

Sports Science
Computer Vision
Biomechanical Analysis

Background:

Accurate human swimming posture recognition is vital for enhancing training and preventing sports injuries.
Existing methods struggle with precise recognition in underwater environments.

Purpose of the Study:

To develop a novel human swimming posture recognition model with improved accuracy and efficiency.
To address the limitations of current techniques in underwater swimming analysis.

Main Methods:

Utilized a 3D convolutional neural network as the foundational model.
Incorporated global average pooling and batch normalization for optimization.
Integrated full pre-activation residual network and a three-branch convolutional attention mechanism for enhanced feature extraction.

Main Results:

Achieved a highest recognition accuracy of 95%, recall of 93.26%, and F1 score of 92.87%.
Demonstrated low pose recognition errors for freestyle (4.7%), breaststroke (4.9%), butterfly (2.1%), and backstroke (6.6%).
Achieved the shortest recognition time of 6.78 seconds for freestyle, outperforming existing models.

Conclusions:

The proposed model offers significant advantages in recognition accuracy and computational efficiency.
This advancement provides effective support for analyzing athletes' swimming postures.
The model has the potential to revolutionize swimming training and injury prevention strategies.