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@article{177547,
        author = {Nambiaaruran S and Dr. J. Angel Ida Chellam and Raghunath C G and Sriraam K N},
        title = {Personalized Fitness Recommendation and Tracking System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1652-1655},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177547},
        abstract = {This paper presents a comprehensive smart fitness system that merges embedded hardware and mobile software to deliver personalized workout planning and real-time strength training tracking. Utilizing the NodeMCU ESP8266 microcontroller and MPU6050 inertial measurement unit (IMU), the system captures and transmits motion data during barbell exercises. The Flutter-based mobile application employs machine learning models to classify exercises and accurately count repetitions and sets. Personalized workout and diet recommendations are generated based on user profiles including age, gender, medical conditions, and fitness goals. The system eliminates reliance on external servers by embedding models directly in the app, ensuring efficient and low-latency performance. Experimental evaluation indicates high accuracy in exercise classification and user satisfaction in recommendation quality.},
        keywords = {Fitness Tracking, Personalized Recommendation, NodeMCU, MPU6050, Machine Learning, Flutter, Barbell Exercises},
        month = {May},
        }