  In the competitive realm of sports, optimal performance necessitates rigorous
management of nutrition and physical conditioning. Specifically, in badminton,
the agility and precision required make it an ideal candidate for motion
analysis through video analytics. This study leverages advanced neural network
methodologies to dissect video footage of badminton matches, aiming to extract
detailed insights into player kinetics and biomechanics. Through the analysis
of stroke mechanics, including hand-hip coordination, leg positioning, and the
execution angles of strokes, the research aims to derive predictive models that
can suggest improvements in stance, technique, and muscle orientation. These
recommendations are designed to mitigate erroneous techniques, reduce the risk
of joint fatigue, and enhance overall performance. Utilizing a vast array of
data available online, this research correlates players' physical attributes
with their in-game movements to identify muscle activation patterns during
play. The goal is to offer personalized training and nutrition strategies that
align with the specific biomechanical demands of badminton, thereby
facilitating targeted performance enhancements.
