پژوهش در طب ورزشی و فناوری

طراحی الگوی استعدادیابی ورزشکاران کاراته‌کار مبتنی بر الگوریتم‌های هوش مصنوعی

نویسندگان

سید احسان نقیبی 1
مهرداد عنبریان 1
الهام شیرزاد 2

1 دانشگاه بو علی سینا

2 دانشگاه تهران

https://doi.org/10.29252/jsmt.19.1.37
چکیده
باوجود اهمیت استعدادیابی برای رشته­‌های ورزشی، مستندات مرتبط با استعدادیابی در کاراته بسیار اندک است. هدف از انجام این مطالعه طراحی الگوی استعدادیابی ورزشکاران کاراته‌­کار مبتنی بر الگوریتم‌­های هوش مصنوعی است. آزمودنی‌­ها به‌صورت نمونه‌­گیری در دسترس به دو گروه کاراته­‌کاران نوجوان نخبه (19 نفر) و غیر کاراته­‌کاران (20 نفر) تقسیم شدند. برخی متغیرهای آنتروپومتریکی و بیومکانیکی مبتنی بر پیشینه تحقیق انتخاب و اندازه­‌گیری شد. از آزمون شاپیرو-ویلک برای تعیین نرمال بودن توزیع داده‌­ها استفاده شد. برای کاهش حجم داده­ها و تعیین مهم­ترین متغیرهای آنتروپومتریکی و بیومکانیکی، روش آماری آنالیز مؤلفه­‌های اصلی (PCA) به‌کاربرده شد. سپس در مدل­سازی از الگوریتم شبکه­‌های عصبی با سه لایه ورودی (10 نرون)، میانی (7 نرون) و خروجی (2 نرون) استفاده شد. نتایج نشان داد که مهم­ترین متغیرهای آنتروپومتریکی و بیومکانیکی کاراته­کاران نوجوان نخبه به ترتیب چربی زیرپوستی سینه­ای، قد، پرش، تعادل ایستا، قدرت نسبی پنجه دست، محیط سینه، محیط مچ پا، چربی زیرپوستی شکم و طول ظاهری پا هستند. همچنین درصد طبقه­بندی صحیح و حساسیت داده­ها بالا و به ترتیب 87٪ و 85٪ بود. با توجه به یافته­ها می­توان از این الگوی پیشنهادی هوشمند برای استعدادیابی کاراته­‌کاران در کنار سایر روش­ها استفاده کرد.

کلیدواژه‌ها

کاراته
استعدادیابی
شبکه عصبی
آنتروپومتری
بیومکانیک

عنوان مقاله English

A new model for talent identification in karate based on artificial intelligence algorithms

نویسنده English

Elham Shirzad 2
چکیده English

Despite the importance of talent for sports, but it has yet received little attention. The purpose of this study was to present a pattern design for talent identification in karate based on artificial intelligence algorithms. Subjects divided to adolescent elite karate athletes (n = 19) and non-karate athletes adolescent (n=20) by convenience sampling. Besed on previous literature, we selected and measured biomechanical and anthropometric variables. The normal distribution of all data was analyzed using Shapiro-Wilk test. Principal component Analysis (PCA) was performed to reduce the number of variables and identify the most important anthropometric and biomechanical variables. Then, for modeling, the neural network algorithm was used with three input layer (10 neurons), middle (7 neurons) and output (2 neurons). The results showed the most important anthropometric variables of adolescent elite karate athletes were thoracic subcutaneous fat, height, jump, static balance, grip strength, chest circumference, ankle circumference, abdominal subcutaneous fat and apparent length leg respectively. Also, percentage of correct classification and sensitive of data was high and 87% and 85% respectively. According to the results of this study, this method can be used for talent karate athletes along with other methods.

کلیدواژه‌ها English

Karate
Talent
Neural Network
Anthropometry
Biomechanics
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