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A study on the optimization of track and field training strategies under the integration of sports science and information technology

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Nov 05, 2024

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Dietze-Hermosa, M., Montalvo, S., Gonzalez, M. P., Rodriguez, S., Cubillos, N. R., & Dorgo, S. (2021). Association and predictive ability of jump performance with sprint profile of collegiate track and field athletes. Sports Biomechanics, 1-20. Search in Google Scholar

Adamchuk, V., Shchepotina, N., Kostiukevych, V., Vozniuk, T., Kulchytska, I., Didyk, T., & Poliak, V. (2021). Technological Aspects of Introduction of 8-Week Model at the Phase of Direct Training for Competitions of Highly Qualified Multi-Sport Athletes in Track-And-Field Athletics. Physical Education Theory and Methodology, 21(3), 200-210. Search in Google Scholar

Guo, X. (2022). Research on Improving the Psychological Adaptability of Sports Athletes in the Process of Track and Field Training. Revista de Psicología del Deporte (Journal of Sport Psychology), 31(2), 75-82. Search in Google Scholar

Viktor, P., Vladyslav, R., Andrii, Y., Yelena, P., Yaroslav, K., & Svetlana, P. (2022). Special coordination exercises in the track and field athletics training program for pupils with special needs. Journal of Physical Education and Sport, 22(3), 645-651. Search in Google Scholar

DeWeese, B. H., Hornsby, G., Stone, M., & Stone, M. H. (2015). The training process: Planning for strength–power training in track and field. Part 2: Practical and applied aspects. Journal of sport and health science, 4(4), 318-324. Search in Google Scholar

Méline, T., Mathieu, L., Borrani, F., Candau, R., & Sanchez, A. M. (2019). Systems model and individual simulations of training strategies in elite short-track speed skaters. Journal of sports sciences, 37(3), 347-355. Search in Google Scholar

Anousaki, E., Zaras, N., Stasinaki, A. N., Panidi, I., Terzis, G., & Karampatsos, G. (2021). Effects of a 25-week periodized training macrocycle on muscle strength, power, muscle architecture, and performance in well-trained track and field throwers. The Journal of Strength & Conditioning Research, 35(10), 2728-2736. Search in Google Scholar

Zhang, Y. (2023). Track and field training state analysis based on acceleration sensor and deep learning. Evolutionary Intelligence, 16(5), 1627-1636. Search in Google Scholar

Peeling, P., Castell, L. M., Derave, W., de Hon, O., & Burke, L. M. (2019). Sports foods and dietary supplements for optimal function and performance enhancement in track-and-field athletes. International journal of sport nutrition and exercise metabolism, 29(2), 198-209. Search in Google Scholar

Yuan, Y. (2024). Exploration of Teaching and Training Methods for Track and Field Sports in School Physical Education. Journal of Human Movement Science, 5(2), 9-15. Search in Google Scholar

Strüder, H., Jonath, U., & Scholz, K. (2023). Track & Field: Training & Movement Science-Theory and Practice for All Disciplines. Meyer & Meyer Sport. Search in Google Scholar

Yuan, Z., Liao, K., Zhang, Y., Han, M., Bishop, C., Chen, Z., ... & Li, Y. (2023). Optimal velocity loss threshold for inducing post activation potentiation in track and field athletes. Biology of sport, 40(2), 603-609. Search in Google Scholar

Han, D. (2021). Data collection and analysis of track and field athletes’ behavior based on edge computing and reinforcement Learning. Mobile Information Systems, 2021(1), 9981767. Search in Google Scholar

Yao, Q., & Zheng, Y. (2021, December). Athlete Detection and Shadow Removal Algorithm in Track and Field Competition Based on Intelligent Optimization Algorithm. In 2021 International Conference on Aviation Safety and Information Technology (pp. 351-355). Search in Google Scholar

Li, G. (2022). Machine Leaning‐Based Optimization Algorithm for Myocardial Injury under High‐ Intensity Exercise in Track and Field Athletes. Computational Intelligence and Neuroscience, 2022(1), 7792958. Search in Google Scholar

Wang, W. (2022). Analysis of Teaching Tactics Characteristics of Track and Field Sports Training in Colleges and Universities Based on Deep Neural Network. Computational Intelligence and Neuroscience, 2022(1), 1932596. Search in Google Scholar

Howatson, G., Brandon, R., & Hunter, A. M. (2016). The response to and recovery from maximum-strength and-power training in elite track and field athletes. International Journal of Sports Physiology and Performance, 11(3), 356-362. Search in Google Scholar

Witard, O. C., Garthe, I., & Phillips, S. M. (2019). Dietary protein for training adaptation and body composition manipulation in track and field athletes. International journal of sport nutrition and exercise metabolism, 29(2), 165-174. Search in Google Scholar

Qin, Y., & Wu, W. (2021). Optimized allocation of resources for intelligent construction of training venues for track and field teams. Mobile Information Systems, 2021(1), 4704838. Search in Google Scholar

Jing, R., Wang, Z., & Suo, P. (2024). Optimization of track and field training methods based on SSA-BP and its effect on athletes’ explosive power. Heliyon, 10(3). Search in Google Scholar

Shanshan Liu. (2024). Application of entertainment E-learning mode based on Apriori algorithm in intelligent English reading assistance mode. Entertainment Computing100744-. Search in Google Scholar

Ping Hsun Lu,Chien Cheng Lai,Ling Ya Chiu,I Hsin Lin,Chih Chin Iou & Po Hsuan Lu. (2024). An Apriori algorithm-based association rule analysis to identify acupoint combinations for treating uremic pruritus.. Tzu chi medical journal(2),195-202. Search in Google Scholar

An Yingbo & Zhou Huasen. (2022). Short term effect evaluation model of rural energy construction revitalization based on ID3 decision tree algorithm. Energy Reports(S4),1004-1012. Search in Google Scholar

Lei QunBi. (2023). Design of an Instant Data Analysis System for Sports Training Based on Data Mining Technology. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT)(2),1-15. Search in Google Scholar

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English