Abstract
Hypertension is a leading contributor to cardiovascular disease, stroke, and chronic kidney disease worldwide. However, current blood pressure (BP) monitoring techniques, whether auscultatory, oscillometric, or invasive, are constrained by episodic measurement, user variability, and limited feasibility for real-world, continuous monitoring. These limitations hinder early detection of abnormal BP patterns and long-term cardiovascular risk assessment. This paper presents a noninvasive device based on photoplethysmography (PPG) signals, with calculated Pulse Wave Velocity (PWV), that combines off-the-shelf sensors with an infrared-based dual PPG sensor. The device combines PPG signals and physiological measurements to predict blood pressure comprehensively using machine learning, with initial trials on 25 participants demonstrating promising accuracy with mean differences of 1.1 mmHg for Systolic Blood Pressure (SBP), -0.9 mmHg for Diastolic Blood Pressure (DBP), and -0.2 mmHg for Mean Arterial Pressure (MAP) compared to standard measurements with the Vicorder device. While primarily tested on a young population, the device shows potential for continuous, real-world BP and PWV monitoring, offering greater usability and accessibility than traditional tonometry- and oscillometry-based devices.