Early Detection of COVID-19 Using Unobtrusive, Wearable Sensor Data

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Due to the prevalence and transmissibility of COVID-19, healthcare workers face significant risk. Given their importance to public health, it is imperative to protect their physical and psychological wellbeing. Yet, current options are limited: There is a shortage of personal protective equipment, test results take days to confirm, and COVID-19 symptoms are often non-specific and delayed. Many individuals remain asymptomatic though infectious.

The high risk of exposure is exacerbated by demanding schedules. Sleep and affect are major contributors to psychological resilience, which in turn impacts physical wellbeing and job performance.

Led by Deniz S. Ones, Hellervik Professor of Industrial Psychology and Distinguished McKnight University Professor, Department of Psychology; and Michael Cullen, Director of Evaluation, Graduate Medical Education, this study will test the hypothesis that data from an unobtrusive, wearable ring sensor that measures body temperature, heart rate, respiration, sleep quality, and psychological readiness can be useful for early detection of COVID-19, as well as for understanding the consequences of infection indicators for psychological resilience and job performance.

The study’s researchers have experience studying wellbeing with wearable technologies.  

“Data from this ring wearable can predict illness reliably, often before users feel unwell,” said Ones. “Better understanding COVID-19 contraction, development, and resolution not only helps address the immediate health crisis but helps us better understand impacts on psychological resilience and work performance, preparing the healthcare workforce for potential future similar adverse conditions.”


This project is supported by the UMN Campus Public Health Officer's Rapid Response Research Grants program, which support University of Minnesota faculty to catalyze and energize small-scale research projects designed to address and mitigate the COVID-19 virus and its associated risks.