Noninvasive vital sign monitoring, especially blood pressure (BP), is crucial for evaluating overall health and identifying early indicators of medical issues. Photoplethysmography (PPG) is a noninvasive technique increasingly adopted in vital sign monitoring, as it allows for the continuous and precise measurement of cardiac-related signals. This technology is sensitive to fluctuations in blood volume within blood vessels, enabling real-time tracking of each heartbeat and identification of any irregularities. This study investigates the effectiveness of machine learning (ML) models for predicting arterial pressure based solely on PPG signals, utilizing a novel dataset and multiwavelength sensor technology…
