We got inspired to build this project by the simple fact that too many lives are at risk due to a lack of monitoring of physician fatigue.

What it does

Our project uses computer vision to scan each physician's face and identify different facial indicators for fatigue.

How we built it

We built the back-end of our project using Python and various libraries such as OpenCV and Dlib. The front-end was mainly done in Swift to develop an iOS app.

Challenges we ran into

We ran into challenges linking our back-end software with the front-end. As well, iOS 13 updates that came out in the past month were a bottleneck across linking services.

Accomplishments that we're proud of

We are proud of a polished product that accurately identifies facial indicators of fatigue and one that has a positive social impact.

What we learned

We learned how to work with real-time databases, computer vision used for facial recognition, and the sheer impact of physician fatigue.

What's next for PitStop

Linking the front-end with the back-end and adding a cognitive test to keep the physician occupied while facial scans are occurring. As well, the scores from the cognitive test could be used as an additional determinant for physician fatigue.

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