Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{181504,
        author = {Mr. G Sandeep Reddy and Smt D.Madhuri and Smt MD.Karishma},
        title = {AI FITNESS TRACKER},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4796-4802},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181504},
        abstract = {Maintaining fitness and adopting a healthy lifestyle are essential goals, yet individuals often face challenges due to the lack of personalized guidance and motivation. To address this, the proposed AI Fitness Tracker leverages advanced technologies like Media pipe for real-time and accurate pose estimation, OpenCV for video processing, and Tkinter for a user-friendly graphical interface. This system is designed to automatically recognize and count exercises, providing precise feedback to ensure proper form and reduce the risk of injuries. The integration of Mediapipe and OpenCV enables seamless tracking of body movements, making the system highly reliable for detecting various exercise postures. Tkinter serves as the interface, allowing users to interact effortlessly with the application, track their progress, and set fitness goals. This AI-powered solution demonstrates robust performance in exercise recognition and counting, offering a practical and accessible approach to fitness monitoring. By delivering real-time feedback and an intuitive experience, the AI Fitness Tracker empowers users to maintain an active and healthy lifestyle, promoting fitness accessibility for all.},
        keywords = {AIFitnessTracker, Solar MediaPipe, OpenCV, Video processing, Tkinter, User interface.},
        month = {June},
        }