Contactless blood pressure sensing using facial visible and thermal images

Hypertension is one of the leading risk factors for several diseases. Measurement and monitoring of blood pressure anytime and anywhere are important to lower blood pressure and prevent pathogenesis of diseases. Non-contact blood pressure measurement is desired to monitor blood pressure anytime and anywhere. The aim of this study was to develop a non-contact blood pressure sensing system. A previous study reported that amplitude and time differences of facial photoplethysmogram (PPG) components extracted using brightness variation of facial skin color in facial visible images could be useful indices for estimating blood pressure. The maximum error between measured and estimated blood pressure using facial PPG components was 12 mmHg. An additional signal processing algorithm is desired to increase the accuracy for estimating blood pressure using facial PPG components. By contrast, facial skin temperature also reflects changes in the facial blood circulation. High-accuracy estimation of blood pressure could be expected using both facial PPG components and facial skin temperature. In this study, improvement of accuracy for estimating blood pressure using facial PPG components by attempting to apply additional signal processing to facial skin color variation. Furthermore, a correlation analysis between facial skin temperature and measured blood pressure was performed, and individual models for blood pressure estimation were created.