COVID-19 has pushed medicine forward in ways many people thought weren't possible prior to the pandemic.
Graduate students at the University of Michigan recently developed an algorithm that could allow early COVID detection through a smartwatch or fitness wristband by monitoring differences in a person’s heart rate.
The early detection could give researchers further insight into COVID infection and quarantine times, as well as allow for detection before symptoms arise.
“Post-doctoral students and graduate students have spent over a year working on this algorithm; developing it and coding it,” said Caleb Mayer, a mathematics graduate student who spearheaded the research. “So I think, eventually, the goal would be to develop some sort of individualized, personalized, real-time detection system of disease.”
By monitoring step count and heart rate, the algorithm can determine when someone is resting, sleeping, or exercising. It will then use that baseline and compare it to a heart rate that increases due to viral infection, theoretically improving the time of diagnosis before symptoms arise and allowing someone to quarantine earlier while refining how long they should isolate.
“These similar algorithms could be applied to other infections or other diseases, and it really is all about the mathematics given the state of how much information and useable information you can get from it,” said Danny Forger, professor of mathematics at the University of Michigan. “Especially in lower resource settings and different communities, this can give additional insight for individuals, and also it can help understand recovery.”
The technology has not yet been utilized in smartwatches or fitness wristbands. After it is refined, the algorithm’s creators hope is it will send alerts to users when they may have been infected.