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 you’re sitting still.
Now, 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 to overcome some of the limitations of existing devices. 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 a U assistant professor of electrical and computer engineering. 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.
The U holds the intellectual property associated with this technology, based on physics-informed machine learning, and the university’s Technology Licensing Office is currently exploring licensing opportunities to bring this invention to market.
Light vs. electricity
The scientific basis of commercial wearable devices that use light to estimate blood pressure isn’t fully understood, and often rely 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 for 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, 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 U associate professor of mathematics. “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.”