Physical function evaluation in volleyball training based on intelligent GRNN
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Research
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120295/ http://psasir.upm.edu.my/id/eprint/120295/1/120295.pdf |
| _version_ | 1848868156301377536 |
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| author | Kaiyuan, Dong Abdullah, Borhannudin Abu Saad, Hazizi Chenxi, Lu |
| author_facet | Kaiyuan, Dong Abdullah, Borhannudin Abu Saad, Hazizi Chenxi, Lu |
| author_sort | Kaiyuan, Dong |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T14:47:55Z |
| format | Article |
| id | upm-120295 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:47:55Z |
| publishDate | 2025 |
| publisher | Nature Research |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1202952025-09-30T00:48:12Z http://psasir.upm.edu.my/id/eprint/120295/ Physical function evaluation in volleyball training based on intelligent GRNN Kaiyuan, Dong Abdullah, Borhannudin Abu Saad, Hazizi Chenxi, Lu 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. Nature Research 2025 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/120295/1/120295.pdf Kaiyuan, Dong and Abdullah, Borhannudin and Abu Saad, Hazizi and Chenxi, Lu (2025) Physical function evaluation in volleyball training based on intelligent GRNN. Scientific Reports, 15 (1). art. no. 30124. pp. 1-11. ISSN 2045-2322 https://www.nature.com/articles/s41598-025-16240-w?error=cookies_not_supported&code=258d0844-485b-4e60-966c-f52d1e36c233 10.1038/s41598-025-16240-w |
| spellingShingle | Kaiyuan, Dong Abdullah, Borhannudin Abu Saad, Hazizi Chenxi, Lu Physical function evaluation in volleyball training based on intelligent GRNN |
| 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 |
| url | http://psasir.upm.edu.my/id/eprint/120295/ http://psasir.upm.edu.my/id/eprint/120295/ http://psasir.upm.edu.my/id/eprint/120295/ http://psasir.upm.edu.my/id/eprint/120295/1/120295.pdf |