From video to vital signs: using personal device cameras to measure pulse rate and predict blood pressure using explainable AI

Abstract In this study, we address the need for low-cost, rapid, and reliable methods for blood pressure measurement to facilitate timely diagnosis and treatment of hypertension. We aim to enhance understanding and trust in the novel remote photoplethysmography technology, specifically as deployed by Lifelight ^ ®, for vital sign monitoring. Lifelight ^ ® utilizes remote photoplethysmography to extract physiologically meaningful features from video recordings on personal devices. This study describes the accurate calculation of pulse rates and the extraction of morphological and time series features from the remote photoplethysmography signal for blood pressure prediction. Unlike common approaches, distinct model types are employed for predicting systolic and diastolic blood pressure. Evaluation on an independent clinical laboratory’s dataset demonstrates the efficacy of the proposed method. The pulse rate accuracy, measured as root mean square error, is 1. 9 beats per minute. The model for systolic blood pressure prediction shows a mean error of 1. 2 mmHg and a standard deviation of 15. 9 mmHg. The model for diastolic prediction shows a mean error of - - 3. 2 mmHg and a standard deviation of 13. 1 mmHg. This research establishes the validity of remote photoplethysmography technology by showcasing accurate pulse rate calculations and effective feature extraction for blood pressure prediction. The results indicate the potential for widespread adoption of remote photoplethysmography in measuring blood pressure and other vital signs.