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@article{164497,
        author = {Krishna Nandkishor Bhutada and Prof. Ateeq Ahmed and Rohit Kattimani},
        title = {Yoga Pose Classification Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {849-853},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164497},
        abstract = {Human pose estimation is a deep-rooted problem in computer vision that has exposed many challenges in the past. Analyzing human activities is beneficial in many fields like video surveillance, biometrics, assisted living, at-home health monitoring etc. With our fast-paced lives these days, people usually prefer exercising at home but feel the need of an instructor to evaluate their exercise form. As these resources are not always available, human pose recognition can be used to build a self-instruction exercise system that allows people to learn and practice exercises correctly by themselves. This project lays the foundation for building such a system by discussing various machine learning and deep learning approaches to accurately classify yoga poses on prerecorded videos and also in real-time. The project also discusses various pose estimation and keypoint detection methods in detail and explains different deep learning models used for pose classification.},
        keywords = {Human pose estimation, yoga, openpose, machine learning, deep learning},
        month = {},
        }