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@article{179103,
        author = {Manoj P and Ankit Mathpati and Chandra Mohan K R and Uppara Siddarth},
        title = {Yoga Pose Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6914-6918},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179103},
        abstract = {This paper presents a real-time yoga pose detection and correction system based on machine learning and computer vision techniques. Using MediaPipe for keypoint extraction and Support Vector Machines (SVM) for classification, the system identifies, classifies, and evaluates yoga poses. The system also provides real-time feedback using cosine similarity and angle analysis for pose correction. The aim is to enhance self-practice accuracy, support remote instruction, and reduce the risk of injury during yoga sessions. The model demonstrates a high recognition accuracy and facilitates an interactive, accessible, and effective yoga experience.},
        keywords = {Yoga Pose Detection, Machine Learning, Computer Vision, SVM, MediaPipe, Pose Correction, Cosine Similarity, Real-Time Feedback.},
        month = {May},
        }