AI Push-Up Trainer

Your personal AI-powered fitness coach that counts your reps and corrects your form in real-time, right from your webcam.

This project uses Python with OpenCV and MediaPipe to create a smart push-up counter. It goes beyond simple counting by implementing an intelligent state machine that waits for the user to be in the correct plank position before starting, and provides real-time feedback on elbow flare to ensure proper form.

(A live demonstration of the application counting reps and providing real-time form feedback.)

✨ Key Features

• 🧠 Intelligent State Machine: The trainer waits for you to be in a proper, stable plank position before it starts counting. No more accidental counts from random movements!

• 🏋️‍♂️ Real-Time Repetition Counting: Accurately counts push-ups by tracking the angle of both elbows.

• 💪 Form Correction: Actively monitors for flared elbows (a common mistake) and provides instant visual feedback to help you maintain good form.

• 📊 Intuitive UI: A clean, on-screen display shows your rep count, current stage (GET_READY, READY, DOWN), and color-coded form feedback.

• 📹 Session Recording: Automatically records your workout session and saves it as an .mp4 file for you to review later.

• 🐛 Live Debug View: An optional display shows the exact back and elbow angles the AI is seeing, helping you understand its logic.

🧠 How It Works

The application processes your webcam feed frame-by-frame to analyze your posture and count repetitions.

1. Video Capture: OpenCV is used to capture the live video feed from your webcam.

2. Pose Estimation: Google's MediaPipe library is used to detect and track 33 key body landmarks (joints) in real-time.

3. Angle Calculation: The program calculates the angles at the elbows (shoulder-elbow-wrist) and hips (shoulder-hip-ankle) to understand your body's position.

4. State Machine Logic: The core of the trainer is a state machine that progresses through three states:

   • GET_READY: The initial state. The program waits for you to hold a stable plank position (straight back, extended arms) before proceeding. This prevents false starts.

   • READY: You are in the correct starting position. The trainer is now actively waiting for you to lower your body.

   • DOWN: You have successfully completed the downward motion. The trainer now waits for you to push back up. A rep is counted upon returning to the READY state.

5. Form Analysis: In every frame, the program also calculates the "flare angle" (hip-shoulder-elbow). If this angle is too wide, it triggers the "TUCK ELBOWS" feedback, helping to prevent shoulder injuries.

🚀 Getting Started

Follow these instructions to get the project running on your local machine.

Prerequisites

• Python 3.8+

• A webcam connected to your computer.

🛠️ Installation & Setup

1. Clone the repository:

git clone \[https://github.com/your-username/AI-Pushup-Trainer.git\](https://github.com/your-username/AI-Pushup-Trainer.git)cd AI-Pushup-Trainer

2. Create and activate a virtual environment:

   *   python -m venv venv
       venv\\Scripts\\activate

3. Install the required libraries:

 pip install opencv-python mediapipe numpy

▶️ How to Run

With your virtual environment activated and dependencies installed, run the following command in your terminal:

  python pushup_trainer_smart.py   

• A window will pop up showing your webcam feed.

• Position yourself so your full body is visible.

• Follow the on-screen prompts to get into position.

• Press the 'q' key to quit the application.

• A video file named pushup_session_smart.mp4 will be saved in the project folder.

🔧 Technologies Used

• Python: The core programming language.

• OpenCV: For video capture, image processing, and rendering the UI.

• MediaPipe: For high-fidelity body pose tracking.

• NumPy: For numerical operations and angle calculations.

💡 Future Improvements

• Audio Feedback: Add voice cues for rep counts and form corrections.

• Back Straightness Check: Implement feedback for a sagging or arched back.

• Workout Log: Save workout stats (reps, date, time) to a file.

• Support for More Exercises: Extend the logic to count other exercises like squats or bicep curls.