People blink their eyes 20 times per minute. However, when we start using the PC, it decreases down to 2~5 times per minute. This causes the dry-eye symptom which damages our eyes. In addition, we start to sit on the chair with an unhealthy posture and that causes our face to get too close to the PC screen. When our face and PC screen is too close, there is a possibility of damaging your eyes due to the blue light.

What it does

This application detects two things. 1) Number of eye blinks. 2) Distance between your face and PC screen. It will visualize the collected data with a (x,y) plane and show your health condition.

How we built it

[Sam] Ux/UI design, Frontend Backend, Deploy [Che] Frontend Backend architecture, Deploy We used HTML/CSS, Javascript (used OpenCV API for face recognition), ejs, Node.js, and Heroku.

Challenges we ran into

After building the application, we realized that the architecture we setup will not let us run and collect real-time video data in background.

Accomplishments that we're proud of

We were able to find out how to use real-time face recognition API (OpenCV lib) and detect eye blinks and distance between the face and PC display.

What we learned

It was hard to tell if the collected realtime video data from our web camera is accurate enough to say the user has dry-eye symptom or blue-light damage. However, we were happy with being able to design an architecture that can collect peoples behaviors and visualize it and relate it to some possible symptoms.

What's next for Desk life

We will like to re-design the architecture so it can run more efficiently/accurately on the background. Design it more user friendly.

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