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akaturk Akademik ölçüm

Makale detayı · 2024 · paratext

Artificial Intelligence enhanced kinematic analysis of basketball free throws

Dergi Journal of Physical Education and Sport
ISSN2247 - 806X
YÖKSİS OpenAlex Açık erişim · diamond
Yıl2024
Atıf1OpenAlex
Scopus (SJR)Q3

Veri kaynağı ayrımı

  • YÖKSİSYÖKSİS makale kaydı
  • YÖKSİS dergi adıJournal of Physical Education and Sport
  • OpenAlexOpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

OpenAlex İngilizce

Problem Statement and Approach: In basketball, the success of free throws is important in terms of the ball's exit angle, its proper position in the air and its optimum speed kinematic features.This study investigates the kinematic characteristics of basketball players executing free throws both before and after experiencing fatigue, using artificial Intelligence (AI).Materials and Methods: We used various supervised machine learning algorithms including: k-Nearest Neighbours (k-NN), Naïve Bayes, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA), and Decision Tree.These algorithms were used for classifying features derived from motion data collected from players to uncover patterns and variations in their shooting mechanics under different levels of fatigue.The elbow, trunk, knee and ankle joint angles are measured at the ball release point when the players make successful and unsuccessful shots before and after fatigue.There are two used methods for classification of these features: the first one is used directly row data; the other is used reduced data by using principal component analysis (PCA).For both, data normalized between 0-1 before applying to the classifier.Results: We obtained the best classification accuracy as 98.44% by using Naïve Bayes classifier for row data.Moreover, the results of ANN with reduction data by using PCA have shown the best classification accuracy as 95.31%.The findings reveal distinct patterns and variations in shooting mechanics induced by fatigue and highlight the effectiveness of machine learning models in analysing biomechanical data.Discussion and conclusion: These results can aid in developing training programs to enhance performance and consistency in fatigued states.This research underscores the potential of AI and datadriven approaches in sports biomechanics to uncover valuable insights into athlete performance and fatigue management.

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

1atıfOpenAlex · cited_by_count (önbellek / veritabanı)

Yazarlar

2
  1. BEKİR KARLIK İSTANBUL ESENYURT ÜNİVERSİTESİ 1
  2. Musa Hawamdah 2