Physics-informed models enable cuffless continuous blood pressure

Researchers from the University of Utah and the University of Illinois, Chicago published a study in Nature Communications and an arXiv preprint describing a smartwatch that measures **electrical bioimpedance (BioZ)** and uses a `physics-informed neural network` to estimate continuous blood pressure and blood velocity, per the Nature Communications article and arXiv:2601.00081. The authors report the approach is calibration-free and was tested on healthy volunteers at rest and after exercise, and on patients with hypertension in outpatient and intensive care settings, according to the preprint and journal manuscript. The University of Utah holds the associated intellectual property and its Technology Licensing Office is exploring licensing opportunities, News-Medical reports. Editorial analysis: This work combines physics-based modeling with data-driven learning, which can improve interpretability and robustness versus purely data-driven cuffless methods. Researchers from the University of Utah and the University of Illinois, Chicago published a study in Nature Communications and an arXiv preprint describing a smartwatch that measures electrical bioimpedance (BioZ) and uses a physics-informed neural n... [1828 chars]