eyeT | Devpost

Inspiration

The inspiration for eye-T stemmed from the growing concern over eye health in our increasingly digital world. With more people spending extended hours in front of screens, the risk of eye strain and other related issues has escalated. Recognizing this problem, we aimed to create a solution that not only monitors screen time but also promotes healthier habits and detects potential eye conditions early.

What it does

eye-T is an Eye Health Monitoring System designed to:

Track screen time and enforce healthy breaks.
Utilize AI for automated eye condition detection.
Provide early detection of common eye problems, such as red-eye.
Help users build better screen time habits through reminders and analysis

How we built it

The project was built using a modern tech stack:

Frontend: We utilized Vue.js 3 for a responsive user interface, along with Vue Router for navigation and Axios for API requests. The application also leverages HTML5 File and Camera APIs for image uploads and captures.

Backend: The backend is powered by the Django REST Framework, with a MySQL database for data storage.

AI/ML: We integrated the Roboflow API for image classification and computer vision capabilities to analyze eye conditions.

Challenges we ran into

Throughout the development process, we faced several challenges:

AI Integration: Ensuring accurate detection of eye conditions using AI required extensive testing and fine-tuning of the model.
User Experience: Balancing functionality with a user-friendly interface was crucial, as we wanted to make the app accessible to users of all ages.
Data Privacy: Implementing secure user data management while complying with privacy regulations was a significant concern.

Accomplishments that we're proud of

We are particularly proud of:

Successfully implementing real-time screen time tracking and automated break reminders.
Achieving a high accuracy rate in eye condition detection through our AI model.
Creating a seamless user experience that encourages healthy screen habits.

What we learned

This project taught us valuable lessons in:

The importance of user feedback in refining features and improving usability.
The complexities of integrating AI into practical applications and the need for continuous learning in this field.
Effective collaboration and communication within our team, which was essential for overcoming challenges. ## What's next for eyeT Looking ahead, we plan to:
Expand the AI capabilities to include more eye conditions and improve detection accuracy.
Introduce additional features such as personalized health recommendations based on user data.
Explore partnerships with eye care professionals to enhance the credibility and effectiveness of our solution.
We plan to develop a dedicated mobile application for both Android and iOS platforms. With these developments, we aim to make eye-T an indispensable tool for maintaining eye health in the digital age.