AI coach for badminton

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.

Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks

In the rapidly evolving landscape of transportation, the proliferation of automobiles has made road traffic more complex, necessitating advanced vision-assisted technologies for enhanced safety and navigation. These technologies are imperative for providing critical traffic sign information, influencing driver behavior, and supporting vehicle control, especially for drivers with disabilities and in the burgeoning field of autonomous vehicles. Traffic sign detection and recognition have emerged as key areas of research due to their essential roles in ensuring road safety and compliance with traffic regulations. Traditional computer vision methods have faced challenges in achieving optimal accuracy and speed due to real-world variabilities. However, the advent of deep learning and Convolutional Neural Networks (CNNs) has revolutionized this domain, offering solutions that significantly surpass previous capabilities in terms of speed and reliability. This paper presents an innovative approach leveraging CNNs that achieves an accuracy of nearly 96\%, highlighting the potential for even greater precision through advanced localization techniques. Our findings not only contribute to the ongoing advancement of traffic sign recognition technology but also underscore the critical impact of these developments on road safety and the future of autonomous driving.