University Teams Develop a Wearable Blood Pressure Monitor

Blood pressure has long been a key metric of cardiovascular health, but standard methods for measuring it rely on occasional readings using inflatable cuffs, typically in a clinical setting. Current blood pressure monitors are bulky and uncomfortable and can only take readings when patients are sitting still.

However, an interdisciplinary team of mathematicians and engineers from the University of Utah and the University of Illinois, Chicago, is tackling this challenge by combining physics and artificial intelligence (AI) to overcome current devices’ limitations. Appearing soon in Nature Communications, their study describes a new wearable smartwatch that can measure both blood pressure and blood flow continuously without needing a cuff.

“Elevated blood pressure is considered the silent killer because it leads to heart attacks, aneurysms, and strokes. It represents a global healthcare burden, and it is considered a Holy Grail problem,” said Benjamin Sanchez Terrones, who hatched the project a few years ago as an assistant professor of electrical and computer engineering at the University of Illinois, Chicago. The device works by measuring the electrical properties of blood as it travels through the wrist artery, which fluctuate with blood pressure changes.

The University of Illinois, Chicago, holds the technology’s intellectual property—based on physics-informed machine learning—and the University of Utah’s Technology Licensing Office is currently exploring licensing opportunities to bring the invention to market.

The scientific basis of commercial wearable devices that use light to estimate blood pressure isn’t fully understood and often relies on machine learning as a “black box” to determine blood pressure, making their outputs difficult to interpret and clinically trust—the latter a major barrier to clinical adoption. Unlike these devices that measure light to gauge blood pressure, Sanchez Terrones’ uses a painless and imperceptible electrical current.

The technology records tiny electrical changes in your wrist using bioimpedance, which measures how easily electricity flows through blood and tissue. Since blood flow changes with each heartbeat, these electrical signals carry information about the underlying pressure.

“This work shows how combining machine learning with physics can fundamentally change what’s possible,” noted co-author Christel Hohenegger, a University of Utah associate mathematics professor. “By building physical principles directly into the model, we can move beyond black-box prediction toward systems that are more accurate, more interpretable, and more broadly applicable in real-world healthcare.”

The system harnesses fluid dynamics (the way blood flows) and electromagnetism, giving it a clear scientific foundation and improving reliability. The model encodes the physics of pulsating blood and the electromagnetics of the bioimpedance measurement, so the network won’t predict something that is physically impossible.

The result is a wearable device that can track cardiovascular health continuously, during rest and activity, without needing calibration to each individual user.

Utah graduate students Henry Crandall, Tyler Schuessler and Filip Bělík played a key role in testing the device on 150 people, including patients in intensive care and outpatient settings. “We went the extra mile and measured patients in the intensive care unit as well as the Madsen Health Center [a clinic just off campus in Salt Lake City] because we wanted to test the technology on the target population,” said Sanchez Terrones, who last year relocated his lab to University of Illinois, Chicago.

The Similarity Between Blood and Movies

“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,” Sanchez Terrones explained. “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.”

Sanchez Terrones’ technology can capture the rest of the movie by recording the velocity and pulse of blood as a continuous waveform, not just the familiar systolic and diastolic values provided in standard cuff readings like 120/80. (Systolic is the top number, measuring the pressure against the artery walls when the heart contracts, while diastolic is the pressure when the heart rests between beats.)

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

This research appears in Nature Communications under the title, “Cuffless hemodynamic monitoring with physics-informed machine learning models.” Co-lead authors are Crandall, Schuessler, and Bělík; other authors include scientists with the University of Utah’s School of Medicine, Molecular Medicine Program, Scientific Computing and Imaging Institute, College of Engineering, as well as from Harvard Medical School, University of Pittsburgh. Funding started with a University of Utah seed grant, with additional funding from B-Secur Ltd., National Science Foundation, and the National Institutes of Health.