Technological Insights into Yoga Posture Recognition: A State-of-the-Art Review
Rohan Tyagi, Ravi Yadav, Devendra Mishra · 2024
In the contemporary world, the ancient practice of yoga has gained immense popularity for its holistic approach to well-being. However, with the rise of self-guided yoga practice facilitated by digital platforms, practitioners face challenges in achieving precise alignment and correctness in postures. This paper delves into the intersection of traditional yoga and modern technology, presenting a comprehensive analysis of existing literature and innovative methodologies to address these challenges. Through the integration of deep learning techniques such as PoseNet, OpenPose, and LSTM, our research aims to provide real-time feedback and guidance for self-practitioners. We review studies exploring the significance of yoga in modern life, the surge in self-practice, challenges encountered, and the role of technology in enhancing the yoga experience. Additionally, we present a detailed methodology encompassing data collection, preprocessing, augmentation techniques, and the development of a Yoga Pose Recognition and Correction System. Our approach integrates advanced machine learning models with traditional algorithms, offering a multi-faceted framework for accurate pose identification. Technological innovations such as real-time feedback mechanisms and user-centric interfaces further enhance the system's usability and effectiveness. We discuss diverse applications ranging from yoga education to healthcare and propose future research directions to address challenges such as dataset variability and real-time processing. In conclusion, this research contributes to the evolving landscape of technology-enabled wellness applications, paving the way for accessible and personalized yoga guidance in the digital age.