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@article{169461,
        author = {Rushiraj Sachin Rajeshirke and Omkar Satish Bhavthankar and Sahil Pravinkumar Pingale and Saurabh Santosh Patil and Swati Pandurang Jadhav},
        title = {Review on Yoga Pose Detection and Feedback System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1060-1064},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169461},
        abstract = {In an era where health and fitness are becoming increasingly important, personalized tools for self-improvement are essential. This research presents the design and implementation of a real-time Yoga Pose Detection and Feedback System aimed at assisting users in accurately performing yoga postures. The system integrates several key features: real-time yoga pose detection using image processing techniques, feedback generation based on body posture deviations, and a user- friendly interface for pose selection. The pose detection system leverages machine learning algorithms and OpenPose for key-point extraction, while feedback is provided through voice commands and visual cues. The app ensures proper form and posture, improving user experience by making at-home yoga practice more effective. This paper delves into the system’s architecture, challenges encountered, and the overall impact on modern fitness routines.},
        keywords = {Yoga Pose Estimation, Computer Vision, OpenPose, Machine Learning},
        month = {November},
        }