ADHD & Autism Detection System

Approach

1. Get real-time info of user's eye movement using OpenCV.
2. Gather the co-ordinates of the pupil according to the viewport.
3. Plot a line graph for the co-ordinates.
4. Pass the resulting graph through CNN for classifying.
5. Displaying the test result.

Project Structure

• main.py :
  • embedding the machine learning model to flask
  • routing functions
• templates/ :
  • frontend files
• static/ :
  • media files
• model/ :
  • pre-trained pickle file

Dependencies

• Flask
• OpenCV
• Tensorflow
• Matplotlib
• dlib
• NumPy
• PyTorch
• Torchvision

Detailed Installation & Execution Guide

Prerequisites

1. Python 3.10 (recommended for compatibility with all packages)
2. Visual Studio Build Tools (for installing dlib)
3. A webcam
4. Git (for cloning the repository)

Step 1: Environment Setup

1. Install Python 3.10

   • Download Python 3.10 from Python's official website
   • During installation, make sure to check "Add Python to PATH"

2. Install Visual Studio Build Tools

   • Download Visual Studio Build Tools from Microsoft's website
   • During installation, select "Desktop development with C++"

Step 2: Project Setup

1. Clone the Repository

   git clone https://github.com/Kiettranquoc/ADHD-identification.git
   cd ADHD-identification

2. Create and Activate Virtual Environment

   # Create virtual environment
   python -m venv .venv
   
   # Activate virtual environment
   # On Windows:
   .venv\Scripts\activate
   # On Linux/Mac:
   source .venv/bin/activate

3. Install Required Packages

   pip install -r req.txt

4. Install dlib

   # If you have Visual Studio Build Tools installed:
   pip install dlib
   
   # If the above fails, try the pre-built wheel:
   pip install https://github.com/jloh02/dlib/releases/download/v19.22/dlib-19.22.99-cp310-cp310-win_amd64.whl

Step 3: Running the Application

1. Start the Flask Server

   python main.py

   You should see output similar to:

   * Serving Flask app "main" (lazy loading)
   * Environment: production
   * Debug mode: on
   * Running on http://127.0.0.1:5000/
   

2. Access the Application

   • Open your web browser
   • Go to http://localhost:5000
   • You should see the main page of the application

Step 4: Using the Application

1. Select Test Type

   • Choose between ADHD or Autism test
   • Each test has three difficulty levels: EASY, MEDIUM, and HARD

2. Starting a Test

   • Click the "Start Test" button
   • Allow camera access when prompted
   • Position yourself in front of the camera
   • Read the text displayed on screen
   • The application will track your eye movements

3. Test Results

   • After completing the test, results will be displayed
   • The system analyzes eye movement patterns to make predictions

Troubleshooting Common Issues

1. Camera Access Issues

   • Make sure your webcam is properly connected
   • Check browser permissions for camera access
   • Try refreshing the page if camera doesn't initialize

2. Package Installation Issues

   • If dlib installation fails, ensure Visual Studio Build Tools are properly installed
   • Try using the pre-built wheel for dlib
   • Make sure you're using Python 3.10

3. Application Not Starting

   • Check if port 5000 is already in use
   • Ensure all dependencies are properly installed
   • Check the console for error messages

4. Eye Tracking Issues

   • Ensure good lighting conditions
   • Position yourself directly in front of the camera
   • Remove glasses if they cause reflections
   • Keep your head relatively still during the test

Important Notes

1. The application requires a stable internet connection for initial setup
2. Good lighting conditions are essential for accurate eye tracking
3. The test results are for screening purposes only and should not be considered as medical diagnosis
4. Keep the browser window active during the test
5. Do not move too much during the test as it may affect eye tracking accuracy

Contributing

Feel free to submit issues and enhancement requests!

License

This project is licensed under the MIT License - see the LICENSE file for details.