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Updated: Sep 11, 2025

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Physical function evaluation in volleyball training based on intelligent GRNN.

Kaiyuan Dong1, Borhannudin Bin Abdullah2, Hazizi Bin Abu Saad3

  • 1Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia.

Scientific Reports
|August 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Variable-Structure Generalized Regression Neural Network (VSGRNN) for enhanced real-time monitoring of athletes' physical functions during volleyball training, significantly improving accuracy and feedback capabilities.

Keywords:
Generalized regression neural networkMulti- kernel adaptive modelingPhysical function assessmentVolleyball training

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Area of Science:

  • Sports Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Monitoring athletes' physical function is crucial for optimizing training and preventing injuries.
  • Traditional methods often lack real-time feedback and struggle with complex physiological data.
  • Existing neural network models may not adequately capture dynamic changes in athlete performance.

Purpose of the Study:

  • To develop an advanced neural network model for accurate and real-time evaluation of physical function changes in volleyball players.
  • To enhance the model's ability to handle nonlinear physiological signals and dynamic fluctuations.
  • To improve the efficiency and adaptability of the model for practical deployment in sports training.

Main Methods:

  • Development of a Variable-Structure Generalized Regression Neural Network (VSGRNN) incorporating heterogeneous kernel functions (Gaussian, radial basis, Matern).
  • Implementation of a dynamic adjustment mechanism for smoothing factors based on local gradient perturbation for response compression.
  • Integration of structure embedding mapping and multi-scale linear compression for high-dimensional indicator reconstruction and feature reduction.

Main Results:

  • VSGRNN achieved a goodness-of-fit R² of 0.927, with a Root Mean Square Error (RMSE) of 1.68 and Symmetric Mean Absolute Percentage Error (SMAPE) of 8.21%.
  • Demonstrated superior peak response deviation (6.7%) compared to Long Short-Term Memory (LSTM)+Attention (8.5%) and Tabular Data Network (TabNet) (9.8%).
  • Achieved a 46.1% reduction in inference time with only a 7.9% increase in error when features were compressed to 30%.

Conclusions:

  • VSGRNN significantly outperforms traditional models in accuracy, robustness, structural compression adaptability, and real-time feedback for volleyball training.
  • The proposed model offers an engineerable structure-response modeling approach for intelligent physical function evaluation.
  • This research holds high practical application value for optimizing athlete performance and training strategies.