Paper Title
Exercise Posture Detection

Abstract
This paper presents a novel approach to real-time exercise posture detection using a convolutional neural network (CNN) to improve fitness training and prevent injuries caused by incorrect postures. With an increasing number of people relying on virtual and self-guided fitness routines, there is a demand for automated systems that can provide immediate feedback on form and technique. Our CNN model was trained on an extensive dataset comprising various exercise postures, focusing on exercises like deadlifts, bench presses, bicep curls, shoulder presses, and front raises. By leveraging computer vision techniques, our model accurately identifies incorrect postures, enabling users to make corrective adjustments in real time. The results highlight the potential of AI-driven posture detection as a valuable tool in personal fitness, enhancing workout quality and safety. This research could pave the way for advanced applications in the fitness industry, potentially integrating with wearable devices or virtual trainers. Keywords - Exercise Posture Detection, Convolutional Neural Network ,Computer Vision, Posture Recognition