A team of experts has invented a portable surveillance device powered by machine learning which can detect coughing and crowd size in real-time, and then analyze the data to directly monitor flu-like illnesses and influenza trends.
The creators of FluSense from the University of Massachusetts Amherst 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.
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.
"This may allow us to predict flu trends in a much more accurate manner," said co-author Tauhidur Rahman, assistant professor of computer and information sciences and lead author Forsad Al Hossain.
Results of their FluSense study were published in the Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies.
To give their invention a real-world tryout, the FluSense inventors partnered with Dr George Corey, executive director of University Health Services; biostatistician Nicholas Reich, director of the UMass-based CDC Influenza Forecasting Center of Excellence; and epidemiologist Andrew Lover, a vector-borne disease expert and assistant professor in the School of Public Health and Health Sciences.
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.
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In Rahman's Mosaic Lab, where computer scientists develop sensors to observe human health and behaviour, the researchers first developed a lab-based cough model. Then they trained the deep neural network classifier to draw 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," Rahman says.
They placed the FluSense devices, encased in a rectangular box about the size of a large dictionary, in four healthcare waiting rooms at UMass's University Health Services clinic.
From December 2018 to July 2019, the FluSense platform collected and analyzed more than 350,000 thermal images and 21 million coughing or sneezing 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".
Disclaimer: No Business Standard Journalist was involved in creation of this content
