Construction of measurement index system of basketball players’ specific physical fitness training based on AI intelligence and neural network
Abstract
The development of modern basketball has improved the ability of basketball to compete, and the competition is becoming increasingly intense. Both in attack and defense, they are more active and fiercer. Therefore, higher requirements have been put forward for the physical fitness of basketball players. If good physical fitness cannot be guaranteed, the development of various sports skills will become very difficult. In the actual training of basketball, the specialized physical training of basketball players has received widespread attention and is regarded as the main purpose and way to develop basketball. The status and role of specialized physical training for basketball players in physical education are receiving increasing attention, and specialized physical training has also attracted the attention of coaches. It is necessary to use a sports measurement index system as an objective basis for the testing and evaluation of athletes’ specialized physical training. It is particularly important to improve the training level and establish a scientific and reasonable comprehensive quality evaluation index system for basketball players. Based on neural networks, this article constructs a specialized physical training index system for basketball players and studies the measurement of specialized physical training for basketball players. The experimental data was collected from 100 outstanding basketball players and analyzed using a neural network model. Based on a combination of agility, strength, and endurance tests, the model successfully predicted a 6.68% improvement in performance for special physical training. The method used in this article employs advanced machine learning techniques, and the results demonstrate the potential of neural networks in sports science research.
References
1. Liu SY, Wang ZH. Research on the effect of Hosting Big Sports Events to the GNH and the Construction of Interrelated Measurement Index Systems. Journal of Beijing Sport University. 2018; 45(2): 96–102.
2. Ning YL, Zhan XC. Study on the Index System for Evaluating the Physical and Mental Quality of Carrier Based Pilots. Journal of Military Physical Education and Sports. 2019; 12(6): 580–586.
3. Revel GM, Arnesano M. Measuring overall thermal comfort to balance energy use in sports facilities. Measurement. 2014; 55: 382–393. doi: 10.1016/j.measurement.2014.05.027
4. Xia J. Research on the indicators of physical fitness testing for young children. Innovation and Practice of Teaching Methods. 2021; 4 (12): 127–129.
5. Connolly CP, Coe DP, Kendrick JM, et al. Accuracy of Physical Activity Monitors in Pregnant Women. Medicine & Science in Sports & Exercise. 2011; 43(6): 1100–1105. doi: 10.1249/mss.0b013e3182058883
6. Liang MTC, Braun W, Bassin SL, et al. Effect of High-Impact Aerobics and Strength Training on BMD in Young Women Aged 20–35 Years. International Journal of Sports Medicine. 2010; 32(02): 100–108. doi: 10.1055/s-0030-1268503
7. Vanderstukken F, Jansen N, Mertens T, et al. Elite male field hockey players have symmetric isokinetic glenohumeral strength profiles, but show asymmetry in scapular muscle strength. Journal of Sports Sciences. 2018; 37(5): 484–491. doi: 10.1080/02640414.2018.1507238
8. Otkydach VS, Korchagin MV. The Influence of Military-Sports All-Around Training on the Special Physical Training Indicators of Cadets in Military Educational Institutions. Med Sci Sports Exerc. 2021; 85(4): 611–617.
9. Khatsaiuk O, Medvid M, Maksymchuk B, et al. Preparing Future Officers for Performing Assigned Tasks through Special Physical Training. Revista Romaneasca pentru Educatie Multidimensionala. 2021; 13(2): 457–475. doi: 10.18662/rrem/13.2/431
10. Aabuwarda K. The Effectiveness of Cross Training on Mental Toughness, Development of Special Physical Abilities, and The Degree of Skilled Performance in Aerobic Gymnastics. International Journal of Sports Science and Arts. 2020; 016(016): 122–140. doi: 10.21608/eijssa.2020.43401.1049
11. Kolesnichenko D, Gordeev D, Dodonkin B. Features of increase of physical endurance in system of special applied physical training of employees of Department of Internal Affairs. Revista romaneasca pentru educatie multidimensionala—Journal for Multidimensional Education. 2019; 56(7): 1132–1139.
12. Qin YK, Peng L, Shi, QHU. The Effect of Functional Physical Training on Special Quality of Youngster Rhythmic Gymnasts. Journal of Southwest China Normal University (Natural Science Edition). 2020; 78(12): 189–195.
13. Wood DE, Swain DP. The Physical Parameters of Tactical Climbing and Performance Characteristics of Naval Special Warfare Operators. The Journal of Strength and Conditioning Research. 2021; 67(2): 283–291.
14. Vatansever Ş, Gezen M. Examination of the relationship between physical activity level and quality of life of barrier-free life special. International Journal of Sport, Exercise & Training Sciences. 2020; 251–259. doi: 10.18826/useeabd.632767
15. Druml T, Horna M, Grilz-Seger G, et al. Association of body shape with amount of Arabian genetic contribution in the Lipizzan horse. Archives Animal Breeding. 2018; 61(1): 79–85. doi: 10.5194/aab-61-79-2018
16. Astutik S, Mahardika IK, Indrawati, et al. HOTS student worksheet to identification of scientific creativity skill, critical thinking skill and creative thinking skill in physics learning. Journal of Physics: Conference Series. 2020; 1465(1): 012075. doi: 10.1088/1742-6596/1465/1/012075
17. Wang D, Huang L, Tang L, et al. Generalized pinning synchronization of delayed Cohen–Grossberg neural networks with discontinuous activations. Neural Networks. 2018; 104: 80–92. doi: 10.1016/j.neunet.2018.04.006
18. Kartal S, Oral M, Ozyildirim BM. Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map. International Journal of Applied Mathematics and Computer Science. 2018; 28(2): 411–424. doi: 10.2478/amcs-2018-0031
19. Sontheimer-Phelps A, Chou DB, Tovaglieri A, et al. Human Colon-on-a-Chip Enables Continuous In Vitro Analysis of Colon Mucus Layer Accumulation and Physiology. Cellular and Molecular Gastroenterology and Hepatology. 2020; 9(3): 507–526. doi: 10.1016/j.jcmgh.2019.11.008
20. Sulheim E, Kim J, van Wamel A, et al. Multi-modal characterization of vasculature and nanoparticle accumulation in five tumor xenograft models. Journal of Controlled Release. 2018; 279: 292–305. doi: 10.1016/j.jconrel.2018.04.026
Copyright (c) 2024 Xiaoning Yang
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
