EyeTune

Inspiration

With students and professionals spending hours in front of screens, Computer Vision Syndrome (CVS) has become a growing concern. Symptoms like eye strain, dryness, blurred vision, and headaches are becoming increasingly common. We wanted to build a solution that actively protects your eyes in real time, not just by reminding you to take breaks, but by intelligently controlling your display settings using your webcam and environment.

What it does

EyeTune is an intelligent eye–wellness assistant that monitors your eyes and surroundings using your webcam. It detects signs of eye fatigue such as squinting, abnormal blink rates, and poor lighting conditions, and automatically adjusts your screen settings to reduce strain.

Key features include:

• Automated Screen Scaling
  Automatically adjusts the size of content on your screen based on your eye squint and distance.

• Dynamic Color & Brightness Adjustment
  Automatically changes screen tint based on ambient light and time of day.

• Blink & Eye Tracking
  Monitors your blink rate and eye movement to ensure you are resting your eyes properly.

• Distance Monitoring
  Alerts you if you are sitting too close or too far from the screen.

• Look-Away Reminders
  Encourages you to look away from the screen periodically.

• Screen Break Alerts
  Provides periodic notifications to take short screen breaks.

How we built it

We used MediaPipe for real-time facial landmark and eye tracking, along with OpenCV and other Python libraries for image analysis. For display adjustments, we integrated system-level accessibility controls and custom scripts to modify brightness, scaling, and notification triggers.

Challenges we ran into

• Ensuring precise eye-tracking in different lighting and camera conditions
  
• Handling cross-platform system integration, since display settings work differently on Windows, macOS, and Linux
  
• Balancing real-time performance with accuracy so the app runs smoothly without draining system resources
  
• Concurrent Processing – Implementing real-time monitoring of multiple vision metrics (blinks, distance, ambient light, gaze direction) required efficient concurrency design. We leveraged concurrent programming techniques to run multiple I/O-bound tasks in parallel without slowing down the user’s system.

Accomplishments that we’re proud of

• Successfully detecting blink frequency, gaze direction, and squinting patterns in real time
  
• Implementing automatic screen scaling and brightness adjustments based on both eye behavior and room lighting
  
• Building an initial wellness notification system that encourages healthier screen use
  

What’s next for EyeTune

• Adding personalized thresholds so users can customize when adjustments should happen
  
• Expanding the dashboard with detailed health analytics (daily usage reports, fatigue patterns, and suggestions)
  
• Improving cross-platform support for smoother integration with Windows, macOS, and Linux accessibility APIs
  
• Exploring gamification and rewards to make healthy screen habits more engaging
  

Built With

• opencv
• python