Robust blood pressure estimation using an RGB camera

Blood pressure (BP) is one of important vital signs in diagnosing certain cardiovascular diseases such as hypertension. A few studies have shown that BP can be estimated by pulse transit time (PTT) derived by calculating the time difference between two photoplethysmography (PPG) measurements, which requires a set of body-worn sensors attached to the skin. Recently, remote photoplethysmography (rPPG) has been proposed as an alternative to contactless monitoring. In this paper, we propose a novel contactless framework to estimate BP based on PTT. We develop an algorithm to adaptively select reliable local rPPG pairs, which can remove the rPPG pairs having poor quality. To further improve the PTT estimation, an adaptive Gaussian model is developed to refine the shape of rPPG by analyzing the essential characteristics of rPPG. The adjusted PTT is computed from the refined rPPG signal to estimate BP. The proposed framework is validated using the video sequences captured by an RGB camera, with the ground truth BP measured using a BP monitor. Experiments on the videos collected in laboratory have shown that the proposed framework is capable of estimating BP, with a statistically compliance compared with BP monitor.