رشد و یادگیری حرکتی ورزشی
مجله علمی

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

نوع مقاله : مقاله پژوهشی Released under CC BY-NC 4.0 license I Open Access I

نویسندگان

مصطفی حاج لطفعلیان 1
فائزه حیدری 2
الهام دهقان نیری 3

1 گروه تربیت بدنی و علوم ورزشی، دانشکده روانشناسی و علوم تربیتی، دانشگاه یزد، یزد، ایران.

2 گروه تربیت بدنی و علوم ورزشی، دانشگاه ملی مهارت، تهران، ایران.

3 گروه علوم رفتاری و شناختی ورزشی، دانشکدۀ علوم ورزشی و تندرستی، دانشگاه تهران، تهران، ایران.

https://doi.org/10.22059/jsmdl.2025.387595.1817
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 Hajlofalian 1
Faeze Heydari 2
Elham Dehghan Niri 3
1 Department of Physical Education and Sports Sciences, Faculty of Psychology and Educational Sciences, Yazd University, Yazd, Iran.
2 Department of Physical Education, Technical and Vocational University (TVU), Tehran, Iran.
3 Department 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
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رشد و یادگیری حرکتی ورزشی
دوره 18، شماره 1
بهار 1405
صفحه 45-63

  • تاریخ دریافت 05 دی 1403
  • تاریخ بازنگری 12 اسفند 1403
  • تاریخ پذیرش 14 اسفند 1403

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