Accuracy Detection in Some Sports Training Using Computer Vision and Deep Learning Techniques

Abstract

In this study, the performance of the MediaPipe Pose Estimation model in estimating body position in different sports activities was investigated in the light of biomechanical parameters. Additionally, the performance of the model was evaluated by comparing the real-time data obtained from the camera with different machine learning algorithms (regression, classification, etc.). The results showed that the MediaPipe Pose Estimation model is a suitable and effective tool for sports biomechanics. The model was able to estimate body position with high accuracy in different sports activities. Additionally, the performance of the model was improved by using different machine learning algorithms. This study is a pioneer research on the applicability of computer vision-supported deep learning techniques in sports training and pose estimation. The model has been developed into an application that can be used to improve the performance of athletes.