Goodbye, Cuff: Scientists Develop a Compact Wearable for True Continuous Blood Pressure Monitoring

Researchers from the University of Utah, University of Illinois Chicago, Harvard Medical School, and the University of Pittsburgh have come up with a wearable designed to make continuous blood-pressure monitoring as easy as telling the time — ditching the awkward inflatable cuff for a smartwatch-inspired form factor.

"This is a behemoth of work; a tour de force from my lab," claims Sanchez Terrones, an associate professor in the University of Illinois Chicago's College of Engineering who is cited as the originator of the project. "Cardiovascular disease is the leading cause of death worldwide, but high blood pressure often goes undetected. Engineers refer to measuring blood pressure as a 'Holy-Grail problem.'"

The team's design does away with the point-in-time measurements provided by an inflatable cuff. "Our blood pressure throughout the day is like a movie, but when you put on the cuff, all you get is one snapshot of the picture," Terrones explains. "The cuff device is very useful, but at the same time, limited: it only gives you the least amount of useful information because of the way the technology works: systolic readout over diastolic readout, which translates to the maximum and minimum pressure value during the recording. At the end, we are missing 99% of the movie that explains how blood pressure might change in a patient throughout the day while they are walking, running or climbing up stairs."

The solution: continuous blood pressure monitoring. Traditionally, this is achieved by wearing an automated cuff that inflates on a schedule to take multiple point-in-time measurements throughout the day — an uncomfortable and invasive experience for the wearer. The team's approach ditches this in favor of a smartwatch-inspired wearable that passes an imperceptible electrical current through the wearer's wrist to measure bioimpedance changes with each heartbeat. The data thus gathered is then fed through a machine learning model to translate it back into blood pressure — providing not only point-in-time measurements but vectors representing moment-to-moment changes.

"Blood pressure isn't two numbers; it's a function of time," explains co-author Braxton Ostin, a professor of mathematics at the University of Utah. "The mathematical challenge was recovering that whole waveform from indirect electrical measurements at the wrist — a classic inverse problem. Embedding the physics of blood flow directly into the model makes the prediction more trustworthy."

The team's work is due for publication in the journal Nature Communications, and is currently available as an unedited early release under open-access terms; the technology is being patented under US Patent Application No. 18/586,19.