Soliss Device
Soliss Team!

Inspiration

Too much sun exposure is the leading preventable cause of skin cancer, but most people don’t realize when they’re at risk. We wanted to create a simple, wearable way to monitor UV exposure in real time and make sun safety more accessible, especially across different skin tones.

What it does

Sollis is a wearable t-shitrt and iOS app that tracks UV exposure. The UV sensor measures sunlight intensity, our backend calculates a risk score, and Cerebras AI generates personalized recommendations, like when to reapply sunscreen or step into the shade.

How we built it

We used an Arduino board and UV sensor programmed in C++ to capture exposure data. A Python backend processes this data and communicates with the Cerebras API for AI-powered insights. Finally, a Swift-based iOS app visualizes the results and provides users with actionable guidance.

Challenges we ran into

We faced compiling issues when integrating Arduino code into the system. Another challenge was designing the UV-to-risk formula so that it produced accurate and meaningful results. Getting each layer of the stack to communicate reliably under hackathon time constraints was also a hurdle.

Accomplishments that we're proud of

We built a full-stack prototype in under 36 hours that integrated hardware, backend logic, AI inference, and a mobile UI. We’re proud of making a functional system that connects raw sensor data with a clear, user-friendly experience.

What we learned

We learned how to connect hardware with backend and frontend software into a cohesive system. We also gained experience debugging integration issues and refining formulas for real-world accuracy. Most importantly, we saw how AI can make technical health data feel personal and actionable.

What's next for Solis's

We plan to make the design sleeker and more wearable, improve accuracy with better calibration, and expand inclusivity by tailoring recommendations for a wider range of skin tones.

Built With

Submitted to

PennApps XXVI

Created by

I worked on the back-end, researching on how to design our algorithm based on past scientific research. I also collaborated on the UI/UX design

Emma Wong
I worked on the iOS mobile application using Swift to create the UI/UX including the login, home, settings, and combining the statistics/AI components into the app. I also worked on the backend, using Firebase Auth to allow users to login and Firestore Database to store user / sensor information.

Adishree Das
I fixed code, organized the database, connected the Gemini API and frontend AI components

Skyler Hall
I constructed the hardware component of the project: a wearable that detects UV rays and reminds the user to reapply sunscreen at a certain time. I specifically also included a textile sensor (made of muslin and gum wrapper paper) that acted ad a pressure sensor to see if the user had reapplied sunscreen.

Vijeta Garg