Feature Extraction of Blood Pressure from Facial Skin Temperature Distribution Using Deep Learning

Abstract

Vital sign monitoring in daily life is very important for the early detection of hypertension, which causes cerebrovascular and cardiovascular diseases. A non-contact vital sign sensing is essential for vital sign monitoring in daily life. Our previous studies have constructed linear regression models for estimating blood pressure, using nasal skin temperature and photoplethysmogram components in the nasal region, which were obtained using a non-contact method. Feature extraction from the whole facial area is expected improve the accuracy in estimating blood pressure. In this study, feature extraction related to blood pressure levels from facial skin temperature distribution using a deep learning algorithm was performed. As the result, features at nasal and lip regions were extracted as common features related to blood pressure levels. Furthermore, a possibility for proposal of a general model for estimating blood pressure levels using the common features was shown.