Year: 2024
Type: article
Abstract: : This study presents an innovative system using computer vision and machine learning for real-time yoga pose detection and feedback. Leveraging advanced algorithms like MediaPipe, OpenPose, and CNNs, it analyzes users' postures through a camera, comparing them with standard poses to provide personalized alignment corrections. This enhances yoga practice by minimizing injuries, improving posture accuracy, and supporting fitness goals. Designed for all skill levels, the system integrates with yoga apps, smart mirrors, and wearables, offering features like progress tracking and adaptive feedback. It demonstrates potential in making yoga safer and more engaging. Future developments could include wearable integration, injury prevention, and customized yoga plans, advancing AI-driven fitness solutions.
Authors: Prof. M. A. Pradhan, Virendra Tendolkar, Yash Pazade, Vedant Yeole, Sandesh Rengade
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404760527