Unsupervised Remote Photoplethysmograph and Heart Rate Estimation by Dynamic Region of Interest Tracking

Remote photoplethysmography (PPG) estimates vital signs by measuring changes in the reflected light from the human skin. Compared with traditional PPG techniques, remote PPG enables contactless measurement and reduced cost. In this paper, we propose a novel unsupervised method to extract remote PPG signals and heart rate from videos. We propose an algorithm to dynamically track regions of interest (ROIs) and combine the signals from all ROIs based on signal qualities. To maintain a stable frame rate and accuracy, we propose a dynamic down-sampling approach, which makes our system robust to the different video resolutions and user-camera distances. We also propose the strategy of waiting time adaptation for HR measurements, which can achieve comparable accuracy in HR estimation while reduce the average waiting time. To test the accuracy of the proposed system, we have collected data from 30 subjects with facial masks. Experimental results show that the proposed system can achieve 3.0bpm mean absolute error in HR estimation.