A cutting edge-computing platform, accurately predicts flu trends by autonomously monitoring public spaces.
University of Massachusetts Amherst researchers have recently invented and tested a surveillance device, powered by machine learning called FluSense. That can detect coughing and crowd size in real-time, then analyze its data and directly monitor flu-like illnesses and influenza trends. Models like these can be lifesavers by directly informing the public health response during a flu epidemic.
Tauhidur Rahman Co-author and, assistant professor of computer and information sciences, said “I’ve been interested in non-speech body sounds for a long time. I thought if we could capture coughing or sneezing sounds from public spaces, where a lot of people naturally congregate, we could utilize this information as a new source of data for predicting epidemiologic trends.”
FluSense is basically a thermal camera, a microphone, and a compact computing system loaded with a machine learning model programmed to detect people and the sounds of coughing. It is worth noting that it does not save personal identification features, such as speech data or distinguishing images.
From December 2018 – July 2019, FluSense analyzed more than 350,000 thermal images and 21 million non-speech audio samples from the public waiting areas. The results showed that FluSense was able to accurately predict illness rates at the university clinic. Multiple FluSense signals “strongly correlated” with the results from lab testing for flu-like illnesses and influenza.
The next step is to test FluSense in other public areas and geographic locations. “We have the initial validation that the coughing indeed has a correlation with influenza-related illness,” epidemiologist Andrew Lover says “Now we want to validate it beyond this specific hospital setting and show that we can generalize across locations.”