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@article{174371,
        author = {Keesara Sireesha and Rallabandi Pavan Kumar and Somu Nandini and Shaik Nayeem and Shaik Baji Baba},
        title = {Yoga Pose Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3613-3620},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174371},
        abstract = {The rapid advancement of deep learning has enabled the development of intelligent applications for various domains, including health and fitness. This project presents a web-based Yoga Pose Detection System that utilizes deep learning techniques to accurately identify and analyze yoga poses. The system integrates state-of-the-art convolutional neural network architectures, including MobileNet, VGG16, and DenseNet, to classify yoga postures based on image inputs.
Users can interact with the system through two primary modes: image-based pose prediction and real-time pose detection using a webcam. The integration of a user-friendly interface ensures seamless interaction, while an admin panel provides analytics, user management, and content control. Additionally, sentiment analysis is applied to user feedback to enhance system engagement and improve recommendations. A third-party API- based recommendation system suggests yoga poses tailored to individual health goals.
This platform aims to provide an interactive and efficient approach to learning yoga by offering personalized feedback, pose recommendations, and a dynamic knowledge base. The integration of deep learning and real-time processing makes this system a valuable tool for yoga practitioners, fitness enthusiasts, and instructors.},
        keywords = {Yoga Pose Detection, Deep Learning, Mo- bileNet, VGG16, DenseNet, Real-Time Analysis, Sentiment Analysis, Health and Fitness.},
        month = {March},
        }