A team of US researchers has invented a portable surveillance device powered by machine learning called ‘FluSense’ that can detect coughing and crowd size in real time, analyse the data to directly monitor flu-like illnesses and influenza trends and predict the next pandemic in the making. The ‘FluSense’ creators from University of Massachusetts Amherst said that 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.
“This may allow us to predict flu trends in a much more accurate manner,” said study co-author Tauhidur Rahman, assistant professor of computer and information sciences.
Models like these can be lifesavers by directly informing the public health response during a flu epidemic.
These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more.
The ‘FluSense’ platform processes a low-cost microphone array and thermal imaging data with a Raspberry Pi and neural computing engine.
It stores no personally identifiable information, such as speech data or distinguishing images.
In Rahman’s Mosaic Lab, the researchers first developed a lab-based cough model.
They then trained the deep neural network classifier to draw bounding boxes on thermal images representing people, and then to count them.
“Our main goal was to build predictive models at the population level, not the individual level,” said Rahman.
From December 2018 to July 2019, the FluSense platform collected and analysed more than 350,000 thermal images and 21 million non-speech audio samples from the public waiting areas.
The researchers found that FluSense was able to accurately predict daily illness rates at the university clinic.
According to the study, “the early symptom-related information captured by FluSense could provide valuable additional and complementary information to current influenza prediction efforts”.
Study lead author Forsad Al Hossain said FluSense is an example of the power of combining Artificial Intelligence with edge computing.
“We are trying to bring machine-learning systems to the edge,” Al Hossain says, pointing to the compact components inside the FluSense device. “All of the processing happens right here. These systems are becoming cheaper and more powerful.”
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. Now we want to validate it beyond this specific hospital setting and show that we can generalise across locations,” said epidemiologist Andrew Lover.
Rahman added: “I thought if we could capture coughing or sneezing sounds from public spaces where a lot of people naturally congregate, we could utilise this information as a new source of data for predicting epidemiologic trends”.