Boston:
Scientists have developed a portable device powered by artificial intelligence (AI) which can detect coughing and crowd size in real time, then analyse the data to directly monitor trends in flu-like illnesses such as COVID-19.
The researchers from the University of Massachusetts Amherst In the US said the device called FluSense is envisioned for use in hospitals, healthcare waiting rooms and larger public spaces.
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The cutting edge-computing platform 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, they said.
The researchers noted that 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, they explained.
“This may allow us to predict flu trends in a much more accurate manner,” said Tauhidur Rahman, co-author of the study published in the journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
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, noted Ph.D. student and study lead author Forsad Al Hossain.
The researchers first developed a lab-based cough model. They then trained the algorithm to create 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,” Rahman said.
The researchers placed the FluSense devices, encased in a rectangular box about the size of a large dictionary, in four healthcare waiting rooms.
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.
Multiple and complementary sets of FluSense signals “strongly correlated” with laboratory-based testing for flu-like illnesses and influenza itself, according to the researchers.
“The early symptom-related information captured by FluSense could provide valuable additional and complementary information to current influenza prediction efforts,” the researchers noted.
“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,” Rahman added.
