Real Time Yoga Posture Recognition and Correction
Sakshi Labde · International Journal for Research in Applied Science and Engineering Technology · 2025
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.