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@article{175928,
        author = {Brice Pimenta and Gaurav Kashelkar and Vedant Gharat and Martina Dsouza},
        title = {FitFusion: Real-Time Exercise Form Correction Using OpenCV},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4510-4514},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175928},
        abstract = {Maintaining the correct form during exercise is crucial to preventing injuries and improving performance. While personal trainers can help, factors like cost, availability, and personal preference often make them inaccessible. To bridge this gap, we developed an AI-powered Exercise Form Correction System that uses OpenCV-based pose estimation to analyze workout posture in real-time and provide instant feedback. Our web-based application, built with HTML, CSS, JavaScript, React and OpenCV, tracks key body points to evaluate posture accuracy. If an incorrect form is detected, users receive immediate corrections through pop-up notifications. Additionally, the system includes a BMI calculator and generates customized meal plans based on BMI categories, offering a more holistic fitness solution. Testing and evaluation show that our approach effectively identifies improper exercise forms, making it a cost-effective and accessible alternative to traditional personal training. Future advancements could include deep learning integration, larger datasets, and wearable sensor fusion to enhance accuracy and adaptability, making AI-driven fitness assistants even more effective.},
        keywords = {Exercise Form Corrector, Convolutional Neural Network (CNN), Machine Learning, OpenCV, Exercise Applications},
        month = {April},
        }