Yoga has gained international recognition for enhancing mental health, physical strength, and flexibility. However, the ability of the practitioner to maintain correct alignment in a variety of postures is major factor in how successful is yoga. In addition to reducing the practice's advantages, improper postures raise the possibility of injury. In-person sessions are typically when yoga instructors provide direction and corrections. As at-home practice and virtual classes have grown in popularity, practitioners frequently lack real-time feedback, which makes it challenging to correct posture. In order to bridge the gap between instructor-led sessions and remote practice, this research discusses the development of an AI-powered yoga position monitoring system that makes use of computer vision and artificial intelligence. The suggested system tracks and analyzes users' yoga positions in real time using a webcam or smartphone camera. The technology uses machine learning models like Pose Net to track important body landmarks, evaluate alignment, and give immediate feedback on pose accuracy. The feedback offers precise modifications and suggestions to assist users in properly aligning their bodies, which lowers the risk of injury and increases the practice's overall advantages. Additionally, the system provides users with ongoing progress tracking, which lets them see how much they've improved over time. In addition to improving accessibility by offering online or remote supervision, the AI-based platform customizes the yoga practice by offering specific feedback and pose suggestions based on each practitioner's performance. The goal of this study is to show how AI may improve the experience of practicing yoga by providing real-time, individualized feedback to all users, wherever they may be. The project provides a scalable solution that transfers the knowledge of a yoga instructor into the digital sphere by incorporating open-source technologies like TensorFlow and OpenCV. In order to create a solution that is safe, effective, and easily accessible for yoga practitioners, key performance criteria include pose detection accuracy, user engagement, and overall happiness. Future potential system expansions to accommodate voice feedback, numerous language options, and improved mobile integration are also covered in this study.
Python, Flask/Django, PoseNet, OpenCV, TensorFlow/Keras, SQLite/MySQL, React/HTML/CSS
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