March 23, 2020
Biostatistician Nicholas Reich and epidemiologist Andrew Lover are collaborating with a pair of UMass Amherst computer scientists who have invented a portable surveillance device powered by machine learning called FluSense. Created by Tauhidur Rahman, assistant professor of computer and information sciences, and his Ph.D. student Forsad Al Hossaina, the device can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends.
Results of their FluSense study were recently published in the Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies.
The FluSense creators say the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS.
According to the researchers, “the early symptom-related information captured by FluSense could provide valuable additional and complementary information to current influenza prediction efforts,” such as the FluSight Network, which is a multidisciplinary consortium of flu forecasting teams, including the Reich Lab at UMass Amherst.