Ethics code: IR.NKUMS.REC.1396.79


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1- Department of Sport Sciences, University of Bojnord, Bojnord, Iran
2- Department of Medical Physics and Radiology, School of Allied Medical Sciences, North Khorasan University of Medical Sciences, Bojnurd,Iran , a.younessi7@gmail.com
Abstract:   (30 Views)
Background: Muscle fatigue is a major factor contributing to impaired function and reduced performance in basketball players, particularly in maintaining balance during jump shooting. This study aimed to evaluate the effects of isotonic fatigue on the electromyographic (EMG) signals of the quadriceps and hamstring muscles in female basketball players during jump shooting.
Methods: Twelve professional female basketball players participated in this study. EMG signals were recorded from the quadriceps and hamstring muscles following a standardized warm-up and during jump shooting. Fatigue was induced using isotonic contractions. Following the fatigue protocol, EMG signals were recorded again, and the signal features obtained before and after fatigue were compared. Six time-domain features and three frequency-domain features were analyzed. Statistical analysis was performed using SPSS version 20, with the significance level set at 0.05.
Results: Analysis of the time-domain features revealed that the most pronounced changes occurred in the quadriceps muscles. The root mean square (RMS) and mean absolute value (MAV) increased significantly, whereas zero crossing (ZC) decreased significantly after fatigue compared with the pre-fatigue condition. Analysis of the frequency-domain features demonstrated a significant decrease in median frequency (MDF) across all muscles following fatigue.
Conclusion: The findings suggest that fatigue of the knee muscles may alter the activation patterns of the muscles surrounding the knee joint, thereby impairing balance during jump shooting. These findings may contribute to the development of rehabilitation and training programs aimed at minimizing fatigue-induced neuromuscular alterations and improving performance in basketball players.
     
Article Type: Research | Subject: Basic medical sciences

References
1. Paillard T. Effects of general and local fatigue on postural control: a review. Neurosci Biobehav Rev. 2012;36(1):162-76. [View at Publisher] [DOI] [PMID] [Google Scholar]
2. Zhang G, Chen TLW, Wang Y, Tan Q, Hong TTH, Peng Y, et al. Effects of prolonged brisk walking induced lower limb muscle fatigue on the changes of gait parameters in older adults. Gait Posture. 2023;101:145-53. [View at Publisher] [DOI] [PMID] [Google Scholar]
3. Aglioti SM, Cesari P, Romani M, Urgesi C. Action anticipation and motor resonance in elite basketball players. Nat Neurosci. 2008;11(9):1109-16. [View at Publisher] [DOI] [PMID] [Google Scholar]
4. Sofyan D, Budiman IA. Basketball jump shot technique design for high school athletes: Training method development. Journal Sport Area. 2022;7(1):47-58. [View at Publisher] [DOI] [Google Scholar]
5. Nazmi N, Abdul Rahman MA, Yamamoto S-I, Ahmad SA, Zamzuri H, Mazlan SA. A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors (Basel).2016;16(8):1304. [View at Publisher] [DOI] [PMID] [Google Scholar]
6. Samuel OW, Asogbon MG, Geng Y, Al-Timemy AH, Pirbhulal S, Ji N, et al. Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: advances, current challenges, and future prospects. IEEE Access. 2019;7:10150-65. [View at Publisher] [DOI] [Google Scholar]
7. Abbaspour S, Lindén M, Gholamhosseini H, Naber A, Ortiz-Catalan M. Evaluation of surface EMG-based recognition algorithms for decoding hand movements. Med Biol Eng Comput. 2020;58(1):83-100. [View at Publisher] [DOI] [PMID] [Google Scholar]
8. Pojskic H, Sisic N, Separovic V, Sekulic D. Association between conditioning capacities and shooting performance in professional basketball players: an analysis of stationary and dynamic shooting skills. J Strength Cond Res. 2018;32(7):1981-92. [View at Publisher] [DOI] [PMID] [Google Scholar]
9. Poyil AT, Steuber V, Amirabdollahian F. Influence of muscle fatigue on electromyogram-kinematic correlation during robot-assisted upper limb training. J Rehabil Assist Technol Eng. 2020;7:2055668320903014. [View at Publisher] [DOI] [PMID] [Google Scholar]
10. Zeygham Jahani M, Yaghoubi A, Younessi Heravi MA. Time and Frequency Analysis of EMG Signals for Simultaneous Evaluation of Knee Muscles in Concentric and Eccentric Strength Training in Healthy Men. J. Rehabil. Sci. 2022;9(4):185-90. [View at Publisher] [DOI] [Google Scholar]
11. Tkach D, Huang H, Kuiken TA. Study of stability of time-domain features for electromyographic pattern recognition. J Neuroeng Rehabil. 2010:7:21. [View at Publisher] [DOI] [PMID] [Google Scholar]
12. Bagheri T, Abedi B, Hedayatpour N. Effects of 12 Weeks Concentric and Eccentric Resistance Training on Neuromuscular Adaptation of Quadriceps Muscle. J. Rehabil. Sci. 2020;7(4):161-6. [View at Publisher] [DOI] [Google Scholar]
13. Sun J, Liu G, Sun Y, Lin K, Zhou Z, Cai J. Application of Surface Electromyography in Exercise Fatigue: A Review. Front Syst Neurosci. 2022;16:893275. [View at Publisher] [DOI] [PMID] [Google Scholar]
14. Naderi S, Naserpour H, Mohammadi-Pour F, Amir-Seyfaddini M. A. Comparative Study on the Effects of Functional and Non-Functional Fatigue Protocols on Dynamic Balance of Amateur Basketball Players. Sports Biomech. 2019;5(3):168-177. ‏ [View at Publisher] [DOI] [Google Scholar]
15. Shradhanjali A, Chowdhury S, Kumar N. Power spectral density estimation of EMG signals using parametric and non-parametric approach. Glob. J. Eng. Res. 2013;2(4):111-17. [View at Publisher] [Google Scholar]
16. Zecca M, Micera S, Carrozza MC, Dario P. Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit Rev Biomed. Eng.2002;30(4-6):459-85. [View at Publisher] [DOI] [PMID] [Google Scholar]
17. Rampichini S, Vieira TM, Castiglioni P, Merati G. Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: A review. Entropy (Basel). 2020;22(5):529.‏ [View at Publisher] [DOI] [PMID] [Google Scholar]
18. Pakosz P, Konieczny M, Domaszewski P, Dybek T, Gnoiński M, Skorupska E. Comparison of concentric and eccentric resistance training in terms of changes in the muscle contractile properties. J Electromyogr Kinesiol. 2023:73:102824. ‏ [View at Publisher] [DOI] [PMID] [Google Scholar]
19. Shanshan LV, Yanyu DONG. Analysis of different injuries of basketball players based on surface electromyography. Rev Bras Med Esporte. 2021;27:23-6. ‏ [View at Publisher] [DOI] [Google Scholar]
20. Gonzalez-Izal M, Malanda A, Navarro-Amézqueta I, Gorostiaga EM, Mallor F, Ibañez J, et al. EMG spectral indices and muscle power fatigue during dynamic contractions. J Electromyogr Kinesiol. 2010;20(2):233-40. ‏ [View at Publisher] [DOI] [PMID] [Google Scholar]

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