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Deep learning for sports motion recognition with a high-precision framework for performance enhancement.

Yang Yang1, Fallah Mohammadzadeh2, Mohammad Khishe3

  • 1School of Physical Education, Suzhou University, Suzhou, Anhui, China.

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Summary

This study introduces an Evolved Parallel Recurrent Network (EPRN) with wavelet transforms for superior sports motion recognition. The EPRN model significantly improves accuracy and robustness, outperforming traditional deep learning methods for performance analysis and injury prevention.

Keywords:
Computational efficiencyDeep learningEvolved Parallel Recurrent Networks (EPRNs)Sports motion recognitionWavelet transform

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Area of Science:

  • Sports Science
  • Biomechanical Engineering
  • Artificial Intelligence

Background:

  • Traditional deep learning models like LSTM and Transformers face challenges in accurately recognizing sports motion due to long-term dependencies and input noise.
  • Effective sports motion recognition is crucial for athlete performance analysis, injury prevention, and monitoring.

Purpose of the Study:

  • To develop a novel deep learning framework, the Evolved Parallel Recurrent Network (EPRN), integrated with wavelet transforms for high-precision sports motion recognition.
  • To address the limitations of existing models in capturing complex motion dynamics and noise.

Main Methods:

  • Proposed an Evolved Parallel Recurrent Network (EPRN) architecture featuring parallel recurrent pathways for enhanced temporal modeling.
  • Implemented wavelet-based feature extraction to preserve fine-grained motion details across multiple resolutions.
  • Evaluated the EPRN model on benchmark sports motion datasets, comparing its performance against LSTM, GRU, and CNN models.

Main Results:

  • The EPRN model demonstrated superior performance, reducing Root Mean Squared Error (RMSE) by 23.5% and increasing Structural Similarity Index (SSIM) by 12.7% compared to other architectures.
  • Residual analysis indicated that EPRN exhibits lower error variability and reduced sensitivity to abrupt motion transitions, signifying enhanced robustness.
  • The combination of wavelet-transform-based feature extraction and recurrent deep learning significantly boosted motion recognition accuracy.

Conclusions:

  • The Evolved Parallel Recurrent Network (EPRN) offers a more accurate and robust solution for sports motion recognition compared to traditional deep learning models.
  • This approach holds significant potential for real-life applications including sports performance analysis, real-time motion tracking, and rehabilitation systems.
  • Future research directions include multimodal data fusion and the development of lightweight EPRN variants for real-time applications.