USF researchers explore wearables for COVID-19 monitoring

15-Jun-2020 - Last updated on 15-Jun-2020 at 14:16 GMT

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University of South Florida scientists and Shimmer Research are working on sensors that could provide an early warning system for at-risk patients.

Scientists from the University of South Florida (USF) have partnered with wearable technology specialists Shimmer Research to come up with a wearable device able to monitor and provide alerts for patients at highest risk of severe infection.

2 million US cases

Johns Hopkins University reports the number of COVID-19 cases in the US recently passed the 2 million mark. Thankfully, while the high number of total infections is high, the percentage of patients experiencing severe symptoms due to the virus is still relatively low.

A list of factors can contribute to the likelihood of developing severe COVID-19 symptoms, such as advanced age and history of complicating health issues. The USF researchers reportedly home to utilize wearable sensors to better understand differences in physiological changes between patients with severe effects and those without.

Principal USF investigator Matt Mullarkey said pinpointing which infected patients will develop more serious symptoms, and which will experience only mild or no symptoms, remains a challenge.

“When you look at the viral progression across a population of people, it is very hard to anticipate which people will be severely affected by this virus,” he said. “There are many different cases of individuals who are otherwise healthy, yet still have a violent reaction.”

“We are confident that by examining certain markers, we can find physiological patterns that can help identify patients who are headed toward serious complications,” Mullarkey added.

Study parameters

The research aims to use existing noninvasive medical monitoring technology to monitor more than 100 study participants that have tested positive for COVID-19. The wearable device will track a variety of markers, including skin temperature, thoracic bioimpedance, oxygen saturation and others.

When the data is gathered, researchers will turn to machine learning and artificial intelligence to synthesize the information and find patterns within the physiological fluctuations. The patterns will then be used to develop various profiles for potential patient outcomes.

Co-principal investigator Asa Oxner said discovering an effective method to detect the development of severe systems early on could prove beneficial for COVID-19 medical professionals and patients alike.

“We want to give medical professionals and patients as early an indicator as possible, an early warning system if you will, that a particular person, who is normally healthy but who has been exposed to the virus, fits a physiological profile for negative outcomes,” Oxner said. “If we can alert medical professionals early about the viral progression, the hope is they can take the appropriate medical interventions to save lives.”

The study, funded through a USF COVID-19 Rapid Response Grant, brings together a diverse team of researchers from across the university. Mullarkey is director of the USF Muma College of Business’s Doctor of Business Administration Program; Oxner is director of the TGH-USF Health COVID Clinic, colleagues from the USF College of Nursing and in the School of Information, along with the private company supplying the devices.