Review on badminton players hand-grip analysis and tactical dynamics prediction using egocentric vision

Abstract

Many works have already been carried out on computer vision-based sports analytics, activities in egocentric vision to improve player performance. But less is explored in terms of vision based kinematic analysis in badminton where shuttle trajectory is explored, angles are derived from features and analyzing biomechanics of the player first person vision cameras. This paper reviews existing research on badminton hand-grip analysis and tactical prediction using egocentric vision. It highlights key methods, datasets, and challenges in applying computer vision techniques to sports analytics. The review focuses on computer vision approaches for analyzing hand-grip patterns and predicting tactical dynamics in badminton using egocentric video data. The study summarizes current advancements, identifies research gaps, and outlines future directions for improving egocentric-vision-based sports analytics.