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@article{176147,
        author = {Dommeti Charmi Sri Tulasi and Polumuri Spandana Valli and Datti Bhavishya Sree and Sappa Anudeep and Gattu Nikhilesh and Valeri Adinarayana},
        title = {Fat Stat: Body Fat Percentage Estimation Using Machine Learning and Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5466-5471},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176147},
        abstract = {Accurate body fat assessment is vital for individualized health and exercise planning. Traditional methods relying solely on static data such as height, weight, and circumferences of- ten overlook individual variations in body shape and fat distribution, leading to imprecise estimates. To address these limitations, we propose a novel approach that combines machine learning with real-time body image analysis. Our system captures user inputs—including height, weight, age, gender, and specific body part measurements—and employs Random Forest Regression to predict body fat percentage from dimensions extracted via a calibrated camera. This integration of dynamic image processing and model-based predictions yields a robust performance with an accuracy of 93%, significantly outperforming conventional techniques. Moreover, the system provides personalized dietary and exercise recommendations, ensuring high adaptability to diverse body types. Overall, this cost- effective and scalable hybrid approach offers a user-friendly alternative for more precise body fat estimation.},
        keywords = {Body Fat Estimation, Machine Learning, Computer Vision, OpenCV, Random Forest},
        month = {April},
        }