🏋️ IntelliFit

A Personalized AI-Powered Fitness Assistant

🚀 Inspiration

With so many unhealthy choices in daily life, we wanted to create a dedicated fitness platform that provides personalized workouts, making it easier for people to stay healthy and active.

🎯 What It Does

IntelliFit helps users track their fitness progress and improve workout form through AI-driven pose estimation. Features include:
✅ User Accounts – Track fitness progress over time.
✅ Workout Selection – Choose from predefined workouts or create custom workouts to fit personal goals.
✅ AI-Powered Pose Estimation – Uses computer vision to overlay real-time posture corrections on the user's figure.
✅ Repetition Counter & Form Accuracy Score – Automatically detects and counts reps while assessing exercise form.

🛠️ Technologies Used

Frontend (React.js & Firebase)

• React.js – A JavaScript framework for building dynamic web applications.
• Firebase – Used for user authentication and storing workout data.
• CSS (Styled Components / Tailwind CSS) – Enhances the UI with a modern, responsive design.

Backend (Python & Mediapipe)

• Python – Powers the backend logic for AI-driven pose estimation.
• MediaPipe – A Google library for real-time pose estimation, enabling workout tracking and posture correction.
• Flask – Handles API communication between the frontend and backend.

Computer Vision & AI

• OpenCV – Used for processing real-time images and enhancing pose tracking.
• Custom AI Model Training – Each exercise is individually calibrated for accurate posture correction.

⚡ Challenges We Faced

• Integrating Frontend & Backend – Handling image transmission between React and Python while ensuring real-time feedback.
• Calibrating the AI Model – Each exercise required separate fine-tuning for accurate pose estimation and rep counting.

🎯 Accomplishments We're Proud Of

• Successfully implemented AI-powered pose estimation to assist with real-time form correction.
• Built a fully functional fitness tracking platform with personalized workout creation.
• Overcame technical difficulties in frontend-backend communication to enable smooth user interactions.

📚 What We Learned

• The complexity of real-time image processing and computer vision-based pose estimation.
• How challenging frontend-backend integration can be, especially when dealing with real-time data transmission.
• Fine-tuning AI models for different exercise types is more complex than expected but essential for accuracy.

🔮 Future Improvements

• Enhancing Pose Estimation – Improve AI accuracy for better form correction and more reliable rep counting.
• Adding More Exercises – Expand the exercise library to cover more complex workout routines.
• Optimizing API Communication – Improve efficiency in sending workout images between the frontend and backend.

🏁 Getting Started

Prerequisites

• Node.js and npm installed for frontend development.
• Python 3.x installed for backend development.
• Firebase Account for managing user authentication and workout data.

Installation

1. Clone the repository:

   git clone https://github.com/sanj6y/IntelliFit.git
   cd IntelliFit