At your fingertips: Investigating a new way to detect COVID-19
August 25, 2020 / by Nanci Hellmich
“It would allow people to enter a facility, a classroom, or the Metro by scanning their fingertips to make sure they are negative to COVID-19. We would be able to live a life in which human touch would be less dangerous, and we could return to enjoying in-person meetings.”
— Emanuela Marasco, assistant professor in the Department of Information Sciences and Technology
Detectives use fingerprints to solve crimes, and a Mason Engineering researcher wants to use fingertips to detect COVID-19.
Emanuela Marasco, an assistant professor in the Department of Information Sciences and Technology (IST), is working on a contactless fingertip imaging system that would quickly identify people with COVID-19. The project involves the selection of a panel of substances formed by the body during metabolism that are specific to the infectious disease.
“Our study will evaluate the sensitivity of sweat metabolite biometrics (body measurements) for detecting the COVID-19 infection in people with and without symptoms,” says Marasco, who is collaborating with researchers at the National Institutes of Health. “Monitoring biomarkers in sweat is non-invasive, and it could be much more accurate than a temperature check, which is what is currently used.”
Marasco received a $100,000 EAGER (Early-concept Grants for Exploratory Research) Award from the National Science Foundation for her project, “COVID-19 Real-time Detection via Hyperspectral Analysis of Sweat Metabolite Biometrics."
The EAGER Award is meant to support bold, potentially transformative research ideas in their early stages of development.
Real-time testing for COVID-19 can help us to isolate the infected population including the healthy carriers, says Marasco, a researcher with Mason’s Center for Secure Information Systems (CSIS). “We are creating a new methodology that can acquire sweat metabolites specifically altered by COVID-19 to be processed through machine learning strategies for automatic diagnosis.
“The proposed methodology is based on a radically different approach using the imaging of data to capture a rich signal that can be processed through pattern recognition techniques,” she says.
Max Albanese, an associate professor in IST and associate director of CSIS, says that “should this research effort produce encouraging results in a relatively short period of time, the technology could be deployed at Mason and become an integral part of the Safe Return to Campus Plan. The ability to identify and isolate infected individuals is critical to safety on a university campus.”
Andre Manitius, former chair of IST, says Marasco’s current project builds on her previous success. “Emanuela distinguished herself by using digital image processing to analyze capillary lines of fingerprints to detect fingerprints made by fake fingers. The image of the natural skin perspiration helps to authenticate true fingers and detect the fake ones.”
If Marasco’s new project goes as planned, the new system could be available at universities, airports, supermarkets, and social events to test people before they entered a location. It could drastically reduce the spread of the coronavirus and let people return to a lifestyle close to the one they had before the pandemic.
“It would allow people to enter a facility, a classroom, or the Metro by scanning their fingertips to make sure they are negative to COVID-19,” she says. “We would be able to live a life in which human touch would be less dangerous, and we could return to enjoying in-person meetings.”