Evaluation of the influence of athlete neural activity patterns on the dynamic index of leaping ability using data mining techniques
Abstract
There are many dynamic indicators of jumping ability, and the athlete’s neural activity pattern is an important factor in regulating limb activities. This article uses data mining technology to collect, preprocess, data modeling and analysis, and data visualization of dynamic index data of athletes’ neural activity patterns of jumping ability. The results show that the jumping ability of athletes with higher neural activity intensity increased significantly after training to around 40 cm–50 cm, while athletes with lower neural activity intensity did not change significantly and remained around 30 cm–35 cm. The overall learning ability of athletes with higher levels of neural activity improved by about 10 cm, and the base of neural activity also increased significantly. It shows that there is a significant correlation between the intensity of neural activity and dynamic indicators of jumping ability, which is the main driving force for athletes’ jumping explosive power. The research results can help formulate reasonable and scientific training methods to improve athletes’ jumping ability and overall sports level.
References
1. Maciejewska-Skrendo A, Leznicka K, Leonska-Duniec A, et al. Genetics of muscle stiffness, muscle elasticity and explosive strength. Journal of human kinetics. 2020; 74(1): 143–159. doi: 10.2478/hukin-2020-0027
2. Kamandulis S, Janusevicius D, Snieckus A, et al. High-velocity elastic-band training improves hamstring muscle activation and strength in basketball players. The Journal of sports medicine and physical fitness. 2020; 60(3): 380–387. doi: 10.23736/s0022-4707.19.10244-7
3. Turrini S, Bevacqua N, Cataneo A, et al. Neurophysiological markers of premotor–motor network plasticity predict motor performance in young and older adults. Biomedicines, 2023, 11(5): 1464–1477. doi: 10.3390/biomedicines11051464
4. Nash D, Hughes M G, Butcher L, et al. IL‐6 signaling in acute exercise and chronic training: Potential consequences for health and athletic performance. Scandinavian Journal of Medicine & Science in Sports. 2023; 33(1): 4–19. doi: 10.1111/sms.14241
5. Kalkhoven JT, Watsford ML. The relationship between mechanical stiffness and athletic performance markers in sub-elite footballers. Journal of Sports Sciences. 2018; 36(9): 1022–1029. doi: 10.1080/02640414.2017.1349921
6. Fink A, Bay JU, Koschutnig K, et al. Brain and soccer: Functional patterns of brain activity during the generation of creative moves in real soccer decision-making situations. Human brain mapping. 2019; 40(3): 755–764. doi: 10.1002/hbm.24408
7. Clark MD, Varangis EML, Champagne AA, et al. Effects of career duration, concussion history, and playing position on white matter microstructure and functional neural recruitment in former college and professional football athletes. Radiology. 2018; 286(3): 967–977. doi: 10.1148/radiol.2017170539
8. Hatfield, Bradley D. Brain dynamics and motor behavior: A case for efficiency and refinement for superior performance. Kinesiology Review. 2018; 7(1): 42–50. doi: 10.1123/kr.2017-0056
9. Bhatia M. IoT-inspired framework for athlete performance assessment in smart sport industry. IEEE Internet of Things Journal. 2020; 8(12): 9523–9530. doi: 10.1109/JIOT.2020.3012440
10. Zeng Y. Evaluation of physical education teaching quality in colleges based on the hybrid technology of data mining and hidden markov model. International Journal of Emerging Technologies in Learning (iJET). 2020; 15(1): 4–15. doi: 10.3991/ijet.v15i01.12533
11. Nasuka N, Setiowati A, Indrawati F. Power, strength and endurance of volleyball athlete among different competition levels. Utopia y Praxis Latinoamericana. 2020; 25(10): 15–23. doi: 10.5281/zenodo.4155054
12. Morya E, Monte-Silva K, Bikson M, et al. Beyond the target area: an integrative view of tDCS-induced motor cortex modulation in patients and athletes. Journal of neuroengineering and rehabilitation. 2019; 16(1): 1–29. doi: 10.1186/s12984-019-0581-1
13. Solopova IA, Selionov VA, Blinov EO, et al. Higher responsiveness of pattern generation circuitry to sensory stimulation in healthy humans is associated with a larger hoffmann reflex. Biology. 2022; 11(5): 707–724. doi: 10.3390/biology11050707
14. Pecuch A, Gieysztor E, Telenga M, et al. Primitive reflex activity in relation to the sensory profile in healthy preschool children. International journal of environmental research and public health. 2020; 17(21): 8210–8225. doi: 10.3390/ijerph17218210
15. Dhawale AK, Wolff SBE, Ko R, et al. The basal ganglia control the detailed kinematics of learned motor skills. Nature neuroscience. 2021; 24(9): 1256–1269. doi: 10.1038/s41593-021-00889-3
16. Chen S, Liu Y, Wang ZA, et al. Brain-wide neural activity underlying memory-guided movement. Cell. 2024; 187(3): 676–691. doi: 10.1016/j.cell.2023.12.035
17. Qian L, Liu J. Application of data mining technology and wireless network sensing technology in sports training index analysis. EURASIP Journal on Wireless Communications and Networking. 2020; 121(2020): 1–17. doi: 10.1186/s13638-020-01735-z
18. Sohail M, Talha M, Ikram P, et al. Application of Data Mining Technology in exploring the relationship between cultural sports psychology and intersecting identities. Revista de Psicologia del Deporte (Journal of Sport Psychology). 2021; 30(4): 11–19. Retrieved from https://mail.rpd-online.com/index.php/rpd/article/view/586
19. Khromov N, Korotin A, Lange A, et al. Esports athletes and players: A comparative study. IEEE Pervasive Computing. 2019; 18(3): 31–39. doi: 10.1109/MPRV.2019.2926247
20. Houtmeyers KC, Jaspers A, Figueiredo P. Managing the training process in elite sports: from descriptive to prescriptive data analytics. International Journal of Sports Physiology and Performance. 2021; 16(11): 1719–1723. doi: 10.1123/ijspp.2020-0958
21. Aljawarneh S, Anguera A, Atwood JW, et al. Particularities of data mining in medicine: lessons learned from patient medical time series data analysis. EURASIP Journal on Wireless Communications and Networking. 2019; 260(2019): 1–29. doi: 10.1186/s13638-019-1582-2
22. YILDIZ BF. Applying decision tree techniques to classify European Football Teams. Journal of Soft Computing and Artificial Intelligence. 2021; 1(2): 86–91.
23. Husnain A, Hussain HK, Shahroz HM, et al. Advancements in Health through Artificial Intelligence and Machine Learning: A Focus on Brain Health. Revista Espanola de Documentacion Cientifica. 2024; 18(01): 100–123.
24. Rani P, Kumar R, Jain A, et al. Taxonomy of machine learning algorithms and its applications. Journal of Computational and Theoretical Nanoscience. 2020; 17(6): 2508–2513. doi: 10.1166/jctn.2020.8922
25. Patel HH, Prajapati P. Study and analysis of decision tree-based classification algorithms. International Journal of Computer Sciences and Engineering. 2018; 6(10): 74–78. doi: 10.26438/ijcse/v6i10.7478
26. Jayanthi E, Ramesh T, Kharat RS. Cybersecurity enhancement to detect credit card frauds in health care using new machine learning strategies. Soft Computing. 2023; 27(11): 7555–7565. doi: 10.1007/s00500-023-07954-y
27. Hong S, Walton B, Kim HW, et al. Predicting the behavioral health needs of Asian Americans in public mental health treatment: a classification tree approach. Administration and Policy in Mental Health and Mental Health Services Research. 2023; 50(4): 630–643. doi: 10.1007/s10488-023-01266-x
28. Ju L, Huang L, Tsai SB. Online data migration model and ID3 algorithm in sports competition action data mining application. Wireless Communications and Mobile Computing. 2021; 1(2021): 1–11. doi: 10.1155/2021/7443676
Copyright (c) 2024 Lai Liu, Yan Dong
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
