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Refining and Expanding Duke's Wearable Infection Detection Platform (2023-2024)

Building on the work of previous teams, this team used a multi-method and interdisciplinary approach to design and build a novel wearable device for the purpose of detecting and predicting COVID-like infections.

Team members began by developing the user requirements for a device that can detect COVID-like infections using physiological signals from wearables. This involved completing a thorough literature review of existing research in the field of wearable infection detection.

The team then considered the design and use of current wearable devices and iterated on this design to choose the best sensors and technologies for the stated objectives.

The hardware subteam developed the prototype for a novel wearable device consisting of a variety of sensors to collect and stream physiological data. The team developed a wearable casing and manufacturing plan to print additional sensors.

The mobile app subteam developed an iOS app capable of connecting to the wearable device via Bluetooth and streaming sensor data to a cloud platform. The iOS app was designed to be robust, user-friendly and capable of creating and editing user profiles with secure login functionality.

The data science and visualization subteam developed initial data cleaning and digital biomarkers pipelines to analyze sensor data streamed from the app. They created automatic cloud functions to process data as it is streamed from the iOS app.

Team members plan to present their work at the 2024 International Conference on Body Sensor Networks: NextGen Health: Sensor Innovation, AI and Social Responsibility conference in Chicago.

Timing

Summer 2023 – Spring 2024

Team Outputs

Infection detection algorithm

Wearable device prototype and iOS application

Conference presentations

This Team in the News

Meet the Members of the 2023-2024 Student Advisory Council

See related Data+ project, Refining and Expanding Duke's Wearable Infection Detection Platform (2023), and related teams, Further Developing Duke's Wearable Infection Detection Platform (2024-2025) and Improving Infection Detection With Wearable Device Data (2022-2023).

 

Image: CovIdentify app, courtesy of Pratt School of Engineering

Team Leaders

  • Jessilyn Dunn, Pratt School of Engineering: Biomedical Engineering
  • Ali Roghanizad, Pratt School of Engineering: Biomedical Engineering

Graduate Team Members

  • Lauren Baur, Master of Egr Biomedical Egr
  • Dina Habboosh, Electrical/Computer Engg-MS
  • Seijung Kim, Biomedical Engineering-MS
  • Lauren Lederer, Biomedical Engineering-PHD

Undergraduate Team Members

  • Misha Aganin, Mechanical Engineering (BSE)
  • Aseda Asomani, Computer Science (BS)
  • Krish Bansal, Computer Science (BS); Statistical Science (BS2)
  • Ritvik Janamsetty, Electrical & Computer Egr(BSE); Computer Science (BS2)
  • Lola Maglione Silva, Computer Science (BS)
  • Luke Redmore, Electrical & Computer Egr(BSE)
  • Anna Zhang, Computer Science (BS)