-
-
Project cover thumbnail
-
Paper Prototype / UI/UX Sketch
-
Prototype with ML running on laptop
-
Beta interface with prompts
-
Day 1 progress (ft. Aarush)
Inspiration: Our team thought that the impacts rise of screen-time due to remote work and learning needed to be addressed, so we focused on easing the burden of/creating tools to aide recovery from the common problem of digital eye strain.
What it does:
AEYE Health uses computer vision to track blinking frequency, offering real-time feedback and reminders to help users maintain healthy blinking habits.
How we built it:
We used JavaScript, HTML, CSS, Chart.js, D3.js, and machine learning through the computer vision library opencv for blink detection to create our app, as well as Figma to prototype.
Challenges:
Integrating AI algorithms with the frontend and ensuring efficient functioning and user-friendliness was challenging.
Proud Accomplishments:
We're proud of creating an app that reduces digital eye strain and of our team's synergy throughout the project.
Learnings:
We learned about integrating AI with web applications, data visualization techniques, and team collaboration.
What's next:
We aim to expand AEYE Health's features, including comprehensive eye health reports, refining our AI, and potentially developing a mobile version.
Log in or sign up for Devpost to join the conversation.