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Youngsters often face stress, pressure, and various challenges in today's fast-paced world. The need for yoga arises as a powerful tool to alleviate stress, promote mental well-being, and enhance resilience. The purpose of this research is to present an innovative approach to personalized yoga practice by leveraging deep learning and computer vision techniques for real-time monitoring and correction of yoga poses. The system consists of mood-based yoga recommendation, yoga pose detection, yoga pose estimation, and pose correction. Through the seamless integration of Singular Value Decomposition (SVD), YOLOv3, PoseNet, and an angle heuristic algorithm, this work ensures accurate analysis and correction of the user's yoga poses, facilitating an effective and personalized yoga experience. By addressing the lack of personalization and emotional oversight prevalent in traditional yoga practices, the work aims to promote emotional balance, stress relief, and physical wellness. Through this innovative approach, the work endeavors to transform the traditional paradigm of yoga practice, offering a more personalized, effective, and holistic wellness experience for practitioners.
Duppala et al. (Wed,) studied this question.