Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters
This study aimed to identify key biomechanical and physiological parameters affecting cross-country skiing performance and develop a neural network model for predicting skiing speed. Biomechanical attributes (cycle length and rate, vertical displacement of the center of mass, and angular kinematics)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11488 |
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| author | Huijuan Shi Xiaolan Zhu Shuang Zhao Hans-Christer Holmberg Hui Liu |
| author_facet | Huijuan Shi Xiaolan Zhu Shuang Zhao Hans-Christer Holmberg Hui Liu |
| author_sort | Huijuan Shi |
| collection | DOAJ |
| description | This study aimed to identify key biomechanical and physiological parameters affecting cross-country skiing performance and develop a neural network model for predicting skiing speed. Biomechanical attributes (cycle length and rate, vertical displacement of the center of mass, and angular kinematics) and physiological factors (maximal oxygen uptake, 30 s anaerobic power), along with physical fitness (standing long jump, pull-ups) were assessed for 82 cross-country skiers (52 men and 30 women). Random forest analysis was utilized to identify the most influential parameters on skiing speed, which were subsequently used as input parameters to develop a neural network aimed at predicting this speed. The findings identified the primary predictors of skiing speed as the cycle length on both flat and uphill terrains, vertical displacement of the center of mass during the poling phase on uphill terrain, maximal oxygen uptake, and 30 s anaerobic power. The developed neural network model demonstrated high precision in predicting skiing speeds, evidenced by a strong correlation with actual speeds (correlation coefficient of 0.953) and 97.1% of predictions falling within the 95% Bland–Altman agreement limits, affirming the model’s reliability and effectiveness in forecasting skiing performance. |
| format | Article |
| id | doaj-art-6fb9cb0f12ee4f0399c06dae277e0ffb |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6fb9cb0f12ee4f0399c06dae277e0ffb2024-12-27T14:07:25ZengMDPI AGApplied Sciences2076-34172024-12-0114241148810.3390/app142411488Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological ParametersHuijuan Shi0Xiaolan Zhu1Shuang Zhao2Hans-Christer Holmberg3Hui Liu4Biomechanics Laboratory, College of Human Movement Science, Beijing Sport University, Beijing 100084, ChinaBiomechanics Laboratory, College of Human Movement Science, Beijing Sport University, Beijing 100084, ChinaBiomechanics Laboratory, College of Human Movement Science, Beijing Sport University, Beijing 100084, ChinaDepartment of Health Sciences, Mid Sweden University, 85170 Östersund, SwedenBiomechanics Laboratory, College of Human Movement Science, Beijing Sport University, Beijing 100084, ChinaThis study aimed to identify key biomechanical and physiological parameters affecting cross-country skiing performance and develop a neural network model for predicting skiing speed. Biomechanical attributes (cycle length and rate, vertical displacement of the center of mass, and angular kinematics) and physiological factors (maximal oxygen uptake, 30 s anaerobic power), along with physical fitness (standing long jump, pull-ups) were assessed for 82 cross-country skiers (52 men and 30 women). Random forest analysis was utilized to identify the most influential parameters on skiing speed, which were subsequently used as input parameters to develop a neural network aimed at predicting this speed. The findings identified the primary predictors of skiing speed as the cycle length on both flat and uphill terrains, vertical displacement of the center of mass during the poling phase on uphill terrain, maximal oxygen uptake, and 30 s anaerobic power. The developed neural network model demonstrated high precision in predicting skiing speeds, evidenced by a strong correlation with actual speeds (correlation coefficient of 0.953) and 97.1% of predictions falling within the 95% Bland–Altman agreement limits, affirming the model’s reliability and effectiveness in forecasting skiing performance.https://www.mdpi.com/2076-3417/14/24/11488skiing performancekinematicsevaluationneural network |
| spellingShingle | Huijuan Shi Xiaolan Zhu Shuang Zhao Hans-Christer Holmberg Hui Liu Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters Applied Sciences skiing performance kinematics evaluation neural network |
| title | Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters |
| title_full | Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters |
| title_fullStr | Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters |
| title_full_unstemmed | Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters |
| title_short | Predicting the Speed of Chinese Elite Cross-Country Skiers: A Neural Network Approach Based on the Measurement of Key Biomechanical and Physiological Parameters |
| title_sort | predicting the speed of chinese elite cross country skiers a neural network approach based on the measurement of key biomechanical and physiological parameters |
| topic | skiing performance kinematics evaluation neural network |
| url | https://www.mdpi.com/2076-3417/14/24/11488 |
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