Emanuela Marasco, Assistant Professor, Center for Secure Information Systems (CSIS), is working to determine whether COVID-19 could be diagnosed via sweat metabolites.
If so, this method could enable diagnosis of the virus via non-invasive real-time means.
The concentrations of the biochemical content in human sweat have been measured using reagent kits and instruments, such as spectrophotometers. With this research, relevant spatial information will be integrated with a corresponding spectral signature to enable the diagnosis through advanced image processing and pattern recognition techniques. The proposed COVID-19 detector will be assessed using standard performance metrics of machine learning algorithms and compared to tampon-based testing methods.
Marasco received $100,000 from the National Science Foundation for this project. Funding began in July 2020 and will end in late June 2022.
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