Real Time Yoga Posture Recognition and Correction

Abstract

The interest in human pose detection and correction technologies has surged, especially following the COVID-19 pandemic, which underscored the importance of maintaining personal health and fitness. While traditional manual techniques have contributed significantly to this field, their limitations in speed and precision have prompted the search for more advanced solutions. Simultaneously, the rising adoption of digital health tools has intensified the demand for applications that assist individuals in performing exercises correctly from home. Yoga, in particular, requires exact posture alignment to maximize its health benefits and minimize injury risks. However, conventional manual assessment methods often entail lengthy evaluation periods and are prone to errors. In response to these challenges, we’ve developed a real-time yoga posture recognition and correction system powered by a deep learning model. Using the MoveNet Thunder architecture, our solution provides users with instant feedback to help fine-tune their posture and alignment. Alongside visual indicators, the app offers real-time voice guidance, making practice sessions safer, smoother, and more effective. This initiative highlights how AI can revolutionize at-home fitness and promote overall well-being.

Introduction

The integration of AI and computer vision has revolutionized fitness, especially for real-time yoga posture detection and correction. The COVID-19 pandemic increased demand for remote, accurate fitness guidance as traditional in-person feedback became less accessible. This research proposes a scalable, AI-driven yoga posture recognition system using the MoveNet Thunder model for keypoint detection combined with a Random Forest classifier to identify and evaluate complex yoga poses like Malasana and Baddha Konasana. The system offers real-time visual and audio feedback via a ReactJS frontend and Flask backend, storing user data in a MongoDB cloud database for performance tracking.

Compared to prior works, this system improves accuracy, responsiveness, and user interactivity by combining deep learning with machine learning classifiers and voice guidance. The modular architecture ensures efficient processing (with pose detection accuracy of 94.7% and correction accuracy of 92.3%) and high user satisfaction (91%). Overall, this AI-powered solution enhances safe, effective home yoga practice by delivering instant, personalized posture corrections, advancing the future of intelligent fitness tools.

Conclusion

This project presents the successful development of a real-time system for yoga pose detection and correction using the MoveNet Thunder model. The model accurately identifies key body landmarks and evaluates user posture, enabling timely feedback to ensure correct alignment. A Flask-based backend handles the pose analysis and correction logic, while a React-based frontend offers a user-friendly interface for real-time visual guidance. The integrated system provides accurate and responsive performance, making it an effective tool for supporting at-home yoga practice and virtual instruction. Overall, the application demonstrates the potential of combining deep learning with modern web technologies to promote wellness and improve exercise form. Future work may explore the inclusion of advanced pose sets, personalized progress tracking, and integration with additional health-monitoring tools.

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Copyright

Copyright © 2025 Sakshi Labde, Chetan Marathe, Sahil Vichare, S. K. Wagh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.