Researchers are combining physics and AI to create a smartwatch that can measure blood pressure and blood flow continuously without needing a cuff.

An interdisciplinary team of mathematicians and engineers from the University of Utah and the University of Illinois, Chicago, are combining physics and AI to overcome some of the limitations of existing devices.

Blood pressure is a key metric of cardiovascular health, but standard methods for measuring it rely on occasional readings using inflatable cuffs, usually in a clinical setting. Today’s blood pressure monitors are bulky, uncomfortable and only give readings while the wearer is sitting still.

“Elevated blood pressure is considered the silent killer because it leads to heart attacks, aneurysms and strokes,” said Benjamin Sanchez Terrones, assistant professor at the University of Utah. “It represents a global healthcare burden and it is considered a Holy Grail problem. It works by measuring the electrical properties of blood as it travels through the artery at the wrist, which fluctuate with changes in blood pressure.”

Utah University holds the intellectual property associated with this technology, based on physics-informed machine learning, and the university’s technology licensing office is exploring opportunities to bring this to market.

The scientific basis of commercial wearable devices that use light to estimate blood pressure isn’t fully understood, and they often rely on machine learning to determine blood pressure, making their outputs difficult to interpret and clinically trust, the latter a major barrier for clinical adoption. Unlike these devices that measure light to gauge blood pressure, the researchers used a painless and imperceptible electrical current.

The technology records tiny electrical changes in the wrist using bioimpedance, a measure of how easily electricity flows through blood and tissue. Because 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,” said co-author Christel Hohenegger, a Utah University associate professor of mathematics. “By building physical principles directly into the model, we can move beyond black-box prediction towards systems that are more accurate, more interpretable and more broadly applicable in real-world healthcare.”

The system harnesses fluid dynamics 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 helped test 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 because we wanted to test the technology on the target population,” said Terrones. “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. 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 technology records velocity and pulse of blood as a continuous waveform, not just the familiar systolic and diastolic values provided in standard cuff readings, such as 120/80.

“Blood pressure isn’t two numbers, it’s a function of time,” said co-author Braxton Osting, a University of Utah (www.utah.edu) professor of mathematics. “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.”

To read the study in Nature Communications, visit www.nature.com/articles/s41467-026-72693-1.