University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning, called FluSense, which can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends.
According to the researchers, the edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, could provide another health surveillance tool 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, in an article on the University of Massachusetts website. The study was authored by Ph.D. student and Forsad Al Hossain, and was 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. In Rahman's Mosaic Lab, where computer scientists develop sensors to observe human health and behavior, the researchers first developed a lab-based cough model. Then they 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," Rahman says.
The FluSense devices, encased in a rectangular box about the size of a large dictionary, were then placed 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 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 study, "the early symptom-related information captured by FluSense could provide valuable additional and complementary information to current influenza prediction efforts," such as the FluSight Network, which is a multidisciplinary consortium of flu forecasting teams, including the Reich Lab at UMass Amherst.
"I've been interested in non-speech body sounds for a long time," said Rahman. "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."