A team of students led by researchers in the BIG IDEAs Lab optimized and further developed an existing cloud-based infection detection platform that populates and translated wearable data from a variety of sources. The project involved 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. Software development with UI/UX considerations will also be performed so that the data platform and visualizations are easily accessible and user friendly. Other aspects of the project included training and implementation of machine learning models for infection detection by means of wearable data. 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, Ph.D.
Project Manager:Lauren Lederer
View the team’s final poster here: team24
View the team’s video presentation here:
