Improving infection detection efficiency with wearables

Improving infection detection efficiency with wearables


A team of students led by researchers in the BIG IDEAs Lab will work to create a cloud-based infection detection platform that populates and translates wearable data from a variety of sources. The project will involve working with existing wearable data pipelines (e.g., APIs) to collect, process, and visualize wearable device data in real-time as well as implement machine learning techniques to produce insights of user health. The project will also involve app development and UI/UX considerations so that the platform is easily accessible and user friendly. The ultimate goal of this work is to inform wearable device users of changes in their health condition before more serious symptoms occur.

Project Lead: Ali Roghanizad

View the team’s final poster

Watch the team’s final presentation below:




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