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حاج لطفعلیان, مصطفی, حیدری, فائزه, دهقان نیری, الهام. (1405). استعدادیابی حیطه‌های مختلف ورزش با استفاده از روش‌های مبتنی بر هوش مصنوعی. , 18(1), -. doi: 10.22059/jsmdl.2025.387595.1817
مصطفی حاج لطفعلیان; فائزه حیدری; الهام دهقان نیری. "استعدادیابی حیطه‌های مختلف ورزش با استفاده از روش‌های مبتنی بر هوش مصنوعی". , 18, 1, 1405, -. doi: 10.22059/jsmdl.2025.387595.1817
حاج لطفعلیان, مصطفی, حیدری, فائزه, دهقان نیری, الهام. (1405). 'استعدادیابی حیطه‌های مختلف ورزش با استفاده از روش‌های مبتنی بر هوش مصنوعی', , 18(1), pp. -. doi: 10.22059/jsmdl.2025.387595.1817
حاج لطفعلیان, مصطفی, حیدری, فائزه, دهقان نیری, الهام. استعدادیابی حیطه‌های مختلف ورزش با استفاده از روش‌های مبتنی بر هوش مصنوعی. , 1405; 18(1): -. doi: 10.22059/jsmdl.2025.387595.1817

استعدادیابی حیطه‌های مختلف ورزش با استفاده از روش‌های مبتنی بر هوش مصنوعی

رشد و یادگیری حرکتی ورزشی
دوره 18، شماره 1، فروردین 1405
نوع مقاله: مقاله پژوهشی Released under CC BY-NC 4.0 license I Open Access I
شناسه دیجیتال (DOI): 10.22059/jsmdl.2025.387595.1817
نویسندگان
مصطفی حاج لطفعلیان* 1؛ فائزه حیدری2؛ الهام دهقان نیری3
1گروه تربیت بدنی و علوم ورزشی، دانشکده روانشناسی و علوم تربیتی، دانشگاه یزد، یزد، ایران.
2گروه تربیت بدنی و علوم ورزشی، دانشگاه ملی مهارت، تهران، ایران.
3گروه علوم رفتاری و شناختی ورزشی، دانشکدۀ علوم ورزشی و تندرستی، دانشگاه تهران، تهران، ایران.
چکیده
مقدمه: استعدادیابی ورزشی برای بهبود نتایج و اعتبار نیازمند جنبه‌های مختلف علوم است. امروزه هوش مصنوعی در مسائل مربوط به پیش‌بینی و طبقه‌بندی به‌خوبی عمل می‌کند و از آنجا که استعدادیابی چیزی جز پیش‌بینی صحیح و طبقه‌بندی افراد نیست، استفاده از آن می‌تواند راه‌گشا باشد. بر این اساس هدف تحقیق حاضر استفاده از هوش مصنوعی به‌منظور استعدادیابی در حیطه‌های مختلف ورزش بود.
روش پژوهش: با وجود پراکندگی رشته‌های ورزشی، چهار دسته رشتۀ توپی، راکتی، رزمی و آبی انتخاب و از روش نظر متخصص برای اولویت‌بندی شاخص‌های منتخب پیکری، جسمانی، ادراکی – حرکتی، آمادگی بدنی و روانشناختی استفاده شد. 310 آزمودنی (پسر و دختر 6 تا 16 سال) ارزیابی و بر اساس امتیازها برچسب‌زنی شدند. در ادامه برای بررسی میزان ارتباط بین هرکدام از مؤلفه‌ها با برچسب‌ها، از آزمون تی مستقل استفاده و شش مؤلفۀ اصلی برای هر یک از دسته رشته‌ها انتخاب شد. در نهایت از شبکۀ عصبی پروسپترون 6-1-1 برای بررسی دقت و اعتبار نتایج استفاده شد.
یافته‌ها: نتایج حاصل از شبکه‌های عصبی نشان داد که دقت برای دسته‌بندی افراد در رشته‌های توپی، راکتی، رزمی، آبی و سایر رشته‌ها به‌ترتیب 97.9، 97.9، 87.2، 91.5 و 80.8 درصد است که دقت مطلوب و بالایی است.
نتیجه‌گیری: در نهایت می‌توان گفت که تعیین مؤلفه‌های اصلی هر رشته به تفکیک و طراحی و برنامه‌ریزی شبکۀ عصبی مصنوعی کمک می‌کند تا محققان و مربیان شاخص‌های مهم هر رشته را بشناسند و برای استعدادیابی ورزشی در رشته‌های موردنظر از آن استفاده کنند.
کلیدواژه‌ها
استعدادیابی ورزشی؛ رشته‌های آبی؛ رشته‌های توپی؛ رشته‌های راکتی؛ رشته‌های رزمی؛ شبکه عصبی مصنوعی
عنوان مقاله [English]
Design and Development of the Intelligent Sports Talent Identification Model Based on Artificial Neural Network Algorithm
نویسندگان [English]
Mostafa Hajlofalian1؛ Faeze Heydari2؛ Elham Dehghan Niri3
1Department of Physical Education and Sports Sciences, Faculty of Psychology and Educational Sciences, Yazd University, Yazd, Iran.
2Department of Physical Education, Technical and Vocational University (TVU), Tehran, Iran.
3Department of Behavioral and Cognitive Sciences in Sports, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran.
چکیده [English]
Introduction: Sports talent identification (STI) is a complex process that benefits from insights across various scientific fields to enhance its credibility. Nowadays, artificial intelligence (AI) has proven effective in prediction and classification tasks. Given that talent identification revolves around accurately predicting and classifying individuals, leveraging AI can be truly transformative. This study aimed to explore how artificial intelligence can be used for STI.
Methods: Despite the diversity of sports disciplines, four categories were selected: ball sports, racket sports, martial arts, and aquatic sports. The expert opinion method was used to prioritize the selected indicators, which included anthropometric characteristics, physical abilities, perceptual‑motor skills, fitness‑related abilities, and psychological factors. A total of 310 participants (boys and girls aged 6 to 16 years) were evaluated and labeled based on their scores. To assess the relationship between each component and the labels, independent t‑tests were conducted, resulting in the selection of six key components for each category. Finally, a perceptron 6‑1‑1 neural network was used to evaluate the accuracy and validity of the STI results.
Results: The neural network results showed that the classification accuracy for ball sports, racket sports, martial arts, aquatic sports, and other sports was 97.9%, 97.9%, 87.2%, 91.5%, and 80.8%, respectively, which represent high and desirable accuracy levels.
Conclusion: Finally, it can be concluded that identifying the principal components specific to each sport category and designing an artificial neural network can help researchers and coaches recognize the important indicators for each sport and use them for sports talent identification in their respective fields.
کلیدواژه‌ها [English]
Artificial Neural Network, Ball Sports, Martial arts, Racket Sports, Sports Talent Identification, Water Sports
مراجع
Anderson, D. N. J., Gottwald, V. M., & Lawrence, G. P. (2022). Capturing the holistic profile of high-performance Olympic weightlifting development. Frontiers in Sports and Active Living, 4. https://doi.org/10.3389/fspor.2022.986134

Auletta, F., Kallen, R. W., di Bernardo, M., & Richardson, M. J. (2023). Predicting and understanding human action decisions during skillful joint-action using supervised machine learning and explainable-AI. Scientific Reports, 13(1), 1–14. https://doi.org/10.1038/s41598-023-31807-1

Aurand, A. M., Dufour, J. S., & Marras, W. S. (2017). Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume. Journal of Biomechanics, 58, 237–240. https://doi.org/10.1016/j.jbiomech.2017.05.006

Baker, J., Johnston, K., & Till, K. (2024). Is it time to retire ‘talent’ from discussions of athlete development? High Ability Studies, 35(1), 93–105. https://doi.org/10.1080/13598139.2023.2295320

Barth, M., Emrich, E., & Güllich, A. (2019). A Machine Learning Approach to “Revisit” Specialization and Sampling in Institutionalized Practice. Sage Open, 9(2). https://doi.org/10.1177/2158244019840554

Boerhout, C., van Busschbach, J. T., Vermerris, S. M., Troquete, N. A. C., Hof, A. L., & Hoek, H. W. (2018). Force production parameters as behavioural measures for anger expression and control: The Method of Stamp Strike Shout. PLOS ONE, 13(11), e0206494. https://doi.org/10.1371/journal.pone.0206494

Brown, J. (2001). Sports talent. Human kinetics

Collins, R., Collins, D., MacNamara, Á., & Jones, M. I. (2014). Change of plans: an evaluation of the effectiveness and underlying mechanisms of successful talent transfer. Journal of Sports Sciences, 32(17), 1621–1630. https://doi.org/10.1080/02640414.2014.908324

Cho, E. H., Yun, H. J., & So, W. Y. (2020). THE validity of alternative hand wall toss tests in Korean children. Journal of Men’s Health, 16(1), e10–e18. https://doi.org/10.15586/jomh.v16i1.166

Cook, G., Burton, L., Hoogenboom, B. J., & Voight, M. (2014). Functional movement screening: the use of fundamental movements as an assessment of function-part 2. International Journal of Sports Physical Therapy, 9(4).

Falk, B., Lidor, R., Lander, Y., & Lang, B. (2004). Talent identification and early development of elite water-polo players: a 2-year follow-up study. Journal of Sports Sciences, 22(4), 347–355. https://doi.org/10.1080/02640410310001641566

Han, M., Gómez-Ruano, M.-A., Calvo, A. L., & Calvo, J. L. (2023). Basketball talent identification: a systematic review and meta-analysis of the anthropometric, physiological and physical performance factors. Frontiers in Sports and Active Living, 5. https://doi.org/10.3389/fspor.2023.1264872

Johnston, K., Wattie, N., Schorer, J., & Baker, J. (2018). Talent Identification in Sport: A Systematic Review. Sports Medicine, 48(1), 97–109. https://doi.org/10.1007/s40279-017-0803-2

Kaynar, Ö., & Bilici, F. (2019). Examination of Talent Selection Methods in Different Sports Branches. Journal of Education and Training Studies, 6(12a), 44. https://doi.org/10.11114/jets.v6i12a.3925

Kim, B.-M., Lee, B.-E., Park, H.-S., Kim, Y.-J., Suh, Y.-J., Kim, J., Shin, J.-Y., & Ha, E.-H. (2016). Long working hours and overweight and obesity in working adults. Annals of Occupational and Environmental Medicine, 28(1), 36. https://doi.org/10.1186/s40557-016-0110-7

Kondric, M. (1995). The expert system for orientation of children into table tennis in the Republic of Slovenia. January 1996, 10.

Lake, J., Mundy, P., Comfort, P., McMahon, J. J., Suchomel, T. J., & Carden, P. (2018). Concurrent Validity of a Portable Force Plate Using Vertical Jump Force–Time Characteristics. Journal of Applied Biomechanics, 34(5), 410–413. https://doi.org/10.1123/jab.2017-0371

Lemmink, K. A. P. M., Kemper, H. C. G., Greef, M. H. G., Rispens, P., & Stevens, M. (2003). The Validity of the Sit-and-Reach Test and the Modified Sit-and-Reach Test in Middle-Aged to Older Men and Women. Research Quarterly for Exercise and Sport, 74(3), 331–336. https://doi.org/10.1080/02701367.2003.10609099

Marovic, I., Janicijevic, D., Knežević, O. M., Garcia-Ramos, A., Prebeg, G., & Mirkov, D. M. (2022). Potential use of the medicine ball throw test to reveal the upper-body maximal capacities to produce force, velocity, and power. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. https://doi.org/10.1177/17543371221113127

Mendes, D., Travassos, B., Carmo, J. M., Cardoso, F., Costa, I., & Sarmento, H. (2022). Talent Identification and Development in Male Futsal: A Systematic Review. International Journal of Environmental Research and Public Health, 19(17). https://doi.org/10.3390/ijerph191710648

Menezes, K., Scianni, A., Faria-Fortini, I., Avelino, P., Faria, C., & Teixeira-Salmela, L. (2015). Measurement properties of the lower extremity motor coordination test in individuals with stroke. Journal of Rehabilitation Medicine, 47(6), 502–507. https://doi.org/10.2340/16501977-1963

Mohammadhossein Noori, & Sadeghi, H. (2018). Designing smart model in volleyball talent identification via fuzzy logic based on main and weighted criteria resulted from the analytic hierarchy process. Journal of Advanced Sport Technology, 1(1), 16–24. Retrieved from https://www.magiran.com/paper/2116640 LK - https://www.magiran.com/paper/2116640. (In Persian).

Morris, T. (1995). Sport psychology in Australia: A profession established. Australian Psychologist, 30(2), 128–134. https://doi.org/10.1080/00050069508258917

Noori, M., & Sadeghi, H. (2018). A Review of Sport Talent Identification Studies. Sport Management Journal, 10(2), 387–400. https://doi.org/10.22059/jsm.2017.219155.1716. (In Persian).

Noori, M., & Sadeghi, H. (2019). Validation and reliability of football talent identification software based on fuzzy logic  TT. Research-in-Sport-Medicine-and-Technology, 17(17), 1–10. https://doi.org/10.29252/jsmt.17.17.1.(In Persian).

Nurjaya, D. R., Abdullah, A. G., Ma’Mun, A., & Rusdiana, A. (2020). Rowing talent identification based on main and weighted criteria from the Analytic Hierarchy Process (AHP). Journal of Engineering Science and Technology, 15(6), 3723–3740.

Otieno, M. D. (2019). Influence of socio-cultural environment on talent identification for rugby players in rugby clubs in Kenya.

Raymakers, J. A., Samson, M. M., & Verhaar, H. J. J. (2005). The assessment of body sway and the choice of the stability parameter(s). Gait & Posture, 21(1), 48–58. https://doi.org/10.1016/j.gaitpost.2003.11.006

Reilly, T., Williams, A. M., Nevill, A., & Franks, A. (2000). A multidisciplinary approach to talent identification in soccer. Journal of Sports Sciences, 18(9), 695–702. https://doi.org/10.1080/02640410050120078

Reyaz, N., Ahamad, G., Khan, N. J., & Naseem, M. (2022). Machine Learning in Sports Talent Identification: A Systematic Review. 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), 1–6. https://doi.org/10.1109/ICEFEET51821.2022.9848247

Rozi, F., Setijono, H., & Kusnanik, N. W. (2019). THE IDENTIFICATION MODEL ON SWIMMING ATHLETES SKILL. Теорія і Методика Фізичного Виховання і Спорту, 4, 30–35. https://doi.org/10.32652/tmfvs.2019.4.30-35

Sarmento, H., Anguera, M. T., Pereira, A., & Araújo, D. (2018). Talent Identification and Development in Male Football: A Systematic Review. Sports Medicine, 48(4), 907–931. https://doi.org/10.1007/s40279-017-0851-7

Schorer, J., Wattie, N., Cobley, S., & Baker, J. (2017). Concluding, but definitely not conclusive, remarks on talent identification and development. In Routledge handbook of talent identification and development in sport (pp. 466-476). Routledge.

Shirzad, E. (2021). A new model for talent identification in karate based on artificial intelligence algorithms. Research in Sport Medicine and Technology, 19(21). https://doi.org/10.29252/jsmt.19.1.37

Shah, D. P. (2017). New performance measure in Cricket. IOSR Journal of Sports and Physical Education, 04(03), 28–30. https://doi.org/10.9790/6737-04032830

Simmons, K. (2001). Body measurement techniques: a comparison of three-dimensional body scanning and physical anthropometric methods.

Taha, Z., Musa, R. M., P.P. Abdul Majeed, A., Alim, M. M., & Abdullah, M. R. (2018). The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach. Human Movement Science, 57, 184–193. https://doi.org/10.1016/j.humov.2017.12.008

Truswell, D. (2021). Tokyo 2020 team shows importance of investment in talent.

Van Harten, K., Bool, K., Van Vlijmen, J., & Elferink-Gemser, M. (2021). Talent transfer: A systematic review. Current Issues in Sport Science (CISS), 6, 006. https://doi.org/10.36950/2021ciss006

Velenczei, A., & Gál, A. (2011). New Challenges, Old Answers in Hungarian Sport:The Case of Talent Management. European Journal for Sport and Society, 8(4), 281–297. https://doi.org/10.1080/16138171.2011.11687883

Williams, A. M., & Reilly, T. (2000). Talent identification and development in soccer. Journal of Sports Sciences, 18(9), 657–667. https://doi.org/10.1080/02640410050120041

Williams, A. Mark, Ford, P. R., & Drust, B. (2020). Talent identification and development in soccer since the millennium. Journal of Sports Sciences, 38(11–12), 1199–1210. https://doi.org/10.1080/02640414.2020.1766647

Yasin, S. N., Ma’mun, A., Rusdiana, A., Abdullah, A. G., & Nur, L. (2020). The Talent Identification of Kayak Athletes: A Research-based on Analytic Hierarchy Process. International Journal of Human Movement and Sports Sciences, 8(6), 395–402. https://doi.org/10.13189/saj.2020.080611

Zhao, J., Xiang, C., Kamalden, T. F. T., Dong, W., Luo, H., & Ismail, N. (2024). Differences and relationships between talent detection, identification, development and selection in sport: A systematic review. Heliyon, 10(6), e27543. https://doi.org/10.1016/j.heliyon.2024.e27543

 

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