AI tracks coughing to predict flu-like outbreaks

Known as FluSense, the platform was developed by researchers at the University of Massachusetts Amherst. It consists of low-cost microphone and thermal imaging arrays combined with a Raspberry Pi processor and neural computing engine, all in a package about the size of a large book. When placed in an environment like a hospital waiting room, the system uses the thermal array to count the number of people in an area, while the microphone and processor analyses levels of coughing.

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Between December 2018 and July 2019, the FluSense platform collected and analysed more than 350,000 thermal images and 21 million non-speech audio samples from four waiting rooms at the university’s health clinics. The team found that the system was able to accurately predict daily illness rates at the clinics. According to the researchers, multiple and complementary sets of FluSense signals "strongly correlated" with laboratory-based testing for flu-like illnesses and influenza itself. The work is published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Tech.