Pose Perfect: Elevating Yoga Experience with Real- Time Pose Detection

This project introduces a real-time system for enhancing yoga practice through pose correction utilizing machine learning techniques and the Media Pipe library. Yoga, a practice rooted in physical and mental well-being, often requires precise alignment to maximize its benefits and prevent injuries. However, achieving correct poses can be challenging, especially for beginners. To address this challenge, we propose a system that leverages computer vision algorithms to track key body joints and provide real-time feedback on pose accuracy. The system employs machine learning models trained on a diverse dataset of yoga poses to accurately estimate and correct deviations from ideal postures. Through integration with Media Pipe, a robust and efficient framework for real-time pose estimation, our system offers seamless user experience and immediate feedback during yoga sessions. This project contributes to the advancement of yoga practice by harnessing technology to promote correct alignment and safety, thereby enhancing the overall effectiveness and accessibility of yoga for practitioners of all levels.