Enhancing Health and Wellness: Deep Learning Powered Exercise Recognition and Tracking

Abstract

Abstract: This project aims to develop a React-based web application that enables users to track, analyze, manage, and schedule their workout routines. Additionally, the application incorporates a machine learning model that utilizes the user’s web camera to track their movements and count reps, sets, and calories burned. This innovative feature provides users with a more accurate representation of their workout progress and empowers them to make data-driven decisions regarding their fitness goals. To achieve this, we utilized various technologies such as React for the frontend, Node.js for the backend, and MongoDB for the database. We also integrated TensorFlow.js, an open-source machine learning library, to build the ML model that powers the application’s tracking and analysis capabilities. Furthermore, we designed the user interface using Bootstrap with Vanilla CSS to ensure a responsive and user-friendly experience. Through this project, our primary objective was to address the limitations of existing workout tracking applications that heavily rely on subjective and often inaccurate user input. Our solution offers a more objective and efficient approach to tracking fitness progress. We firmly believe that this technology has the potential to revolutionize the fitness industry. Overall, our project showcases the power of machine learning and its ability to enhance people’s lives in meaningful ways.