Physical function evaluation in volleyball training based on intelligent GRNN
Abstract This study aims to improve both the evaluation accuracy and the real-time feedback capability in monitoring athletes’ physical function changes during volleyball training. Firstly, based on the framework of the generalized regression neural network, a variable-structure generalized regressi...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16240-w |
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| author | Kaiyuan Dong Borhannudin bin Abdullah Hazizi bin Abu Saad Chenxi Lu |
| author_facet | Kaiyuan Dong Borhannudin bin Abdullah Hazizi bin Abu Saad Chenxi Lu |
| author_sort | Kaiyuan Dong |
| collection | DOAJ |
| description | Abstract This study aims to improve both the evaluation accuracy and the real-time feedback capability in monitoring athletes’ physical function changes during volleyball training. Firstly, based on the framework of the generalized regression neural network, a variable-structure generalized regression neural network (VSGRNN) is proposed and developed. Three heterogeneous kernel functions, namely Gaussian kernel, radial basis kernel, and Matern kernel, are introduced, and a local weighted response mechanism is constructed to enhance the expression ability of nonlinear physiological signals. Second, a dynamic adjustment mechanism for smoothing factors based on local gradient perturbation is proposed, enabling the model to have response compression capability in high-fluctuation samples. Finally, combining the structure embedding mapping mechanism with a multi-scale linear compression framework, the reconstruction of high-dimensional physiological indicators and the elimination of redundant features are achieved, improving model deployment efficiency. Comparative experiments conducted on training data of a high-level university men’s volleyball team show that VSGRNN has a goodness-of-fit R2 = 0.927 on the validation set, with a Root Mean Square Error (RMSE) only 1.68 and Symmetric Mean Absolute Percentage Error (SMAPE) controlled at 8.21%. Within the local perturbation interval, the peak response deviation is 6.7%, far better than the comparative models (Long Short-Term Memory (LSTM) + Attention at 8.5% and Tabular Data Network (TabNet) at 9.8%). When compressed to 30% of the original feature dimension, the error only increases by 7.9%, and the inference time is shortened by 46.1%. The research conclusion shows that VSGRNN outperforms traditional models in terms of accuracy, robustness, structural compression adaptability, and real-time feedback capability. This study provides an engineerable structure-response modeling method for the intelligent evaluation of physical functions in volleyball-specific training, which has high practical application value. |
| format | Article |
| id | doaj-art-b7e9baa379c94bf49743ed32ffcd5654 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b7e9baa379c94bf49743ed32ffcd56542025-08-20T04:03:18ZengNature PortfolioScientific Reports2045-23222025-08-0115111110.1038/s41598-025-16240-wPhysical function evaluation in volleyball training based on intelligent GRNNKaiyuan Dong0Borhannudin bin Abdullah1Hazizi bin Abu Saad2Chenxi Lu3Department of Sports Studies, Faculty of Educational Studies, Universiti Putra MalaysiaDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra MalaysiaDepartment of Nutrition, Faculty of Medicine and Health Sciences, Universiti Putra MalaysiaDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra MalaysiaAbstract This study aims to improve both the evaluation accuracy and the real-time feedback capability in monitoring athletes’ physical function changes during volleyball training. Firstly, based on the framework of the generalized regression neural network, a variable-structure generalized regression neural network (VSGRNN) is proposed and developed. Three heterogeneous kernel functions, namely Gaussian kernel, radial basis kernel, and Matern kernel, are introduced, and a local weighted response mechanism is constructed to enhance the expression ability of nonlinear physiological signals. Second, a dynamic adjustment mechanism for smoothing factors based on local gradient perturbation is proposed, enabling the model to have response compression capability in high-fluctuation samples. Finally, combining the structure embedding mapping mechanism with a multi-scale linear compression framework, the reconstruction of high-dimensional physiological indicators and the elimination of redundant features are achieved, improving model deployment efficiency. Comparative experiments conducted on training data of a high-level university men’s volleyball team show that VSGRNN has a goodness-of-fit R2 = 0.927 on the validation set, with a Root Mean Square Error (RMSE) only 1.68 and Symmetric Mean Absolute Percentage Error (SMAPE) controlled at 8.21%. Within the local perturbation interval, the peak response deviation is 6.7%, far better than the comparative models (Long Short-Term Memory (LSTM) + Attention at 8.5% and Tabular Data Network (TabNet) at 9.8%). When compressed to 30% of the original feature dimension, the error only increases by 7.9%, and the inference time is shortened by 46.1%. The research conclusion shows that VSGRNN outperforms traditional models in terms of accuracy, robustness, structural compression adaptability, and real-time feedback capability. This study provides an engineerable structure-response modeling method for the intelligent evaluation of physical functions in volleyball-specific training, which has high practical application value.https://doi.org/10.1038/s41598-025-16240-wVolleyball trainingPhysical function assessmentGeneralized regression neural networkMulti- kernel adaptive modeling |
| spellingShingle | Kaiyuan Dong Borhannudin bin Abdullah Hazizi bin Abu Saad Chenxi Lu Physical function evaluation in volleyball training based on intelligent GRNN Scientific Reports Volleyball training Physical function assessment Generalized regression neural network Multi- kernel adaptive modeling |
| title | Physical function evaluation in volleyball training based on intelligent GRNN |
| title_full | Physical function evaluation in volleyball training based on intelligent GRNN |
| title_fullStr | Physical function evaluation in volleyball training based on intelligent GRNN |
| title_full_unstemmed | Physical function evaluation in volleyball training based on intelligent GRNN |
| title_short | Physical function evaluation in volleyball training based on intelligent GRNN |
| title_sort | physical function evaluation in volleyball training based on intelligent grnn |
| topic | Volleyball training Physical function assessment Generalized regression neural network Multi- kernel adaptive modeling |
| url | https://doi.org/10.1038/s41598-025-16240-w |
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