Enhancing Yoga Practice: Real-time Pose Analysis and Personalized Feedback

Yoga, a holistic practice with origins in ancient India, has gained popularity worldwide due to its numerous physical, mental, and spiritual benefits. However, achieving correct yoga postures can be challenging, and improper poses may lead to counterproductive results. While the importance of a trained instructor is undeniable, not all practitioners have access to one. In this paper, we propose a computer vision-based approach that combines machine learning techniques, pose estimation, and deep learning to detect and correct yoga poses in real-time. Our contribution lies in the development of a system that empowers yoga practitioners to receive real-time feedback and correction of their poses, enhancing their yoga practice even without a dedicated instructor. We employ the PoseNet model to estimate key points on the human body, providing feedback to users for posture correction. We discuss the methodology, datasets, and technology involved in our approach. Our AI gym trainer system enhances user workouts through personalized routines and real-time feedback. It significantly improves fitness outcomes by tailoring exercise plans to individual needs and performance tracking using OpenCV and MediaPipe. Furthermore, we highlight the potential future scope for improving yoga practice through technology.