Estimation of Respiratory Signals from Remote Photoplethysmography of RGB Facial Videos

Recently, technologies monitoring users’ physiological signals in consumer electronics such as smartphones or kiosks with cameras and displays are gaining attention for their potential role in diverse services. While many of these technologies focus on photoplethysmography for the measurement of blood flow changes, respiratory measurement is also essential for assessing an individual’s health status. Previous studies have proposed thermal camera-based and body movement-based respiratory measurement methods. In this paper, we adopt an approach to extract respiratory signals from RGB face videos using photoplethysmography. Prior research shows that photoplethysmography can measure respiratory signals, due to its correlation with cardiac activity, by setting arterial vessel regions as areas of interest for respiratory measurement. However, this correlation does not directly reflect real-time respiratory components in photoplethysmography. Our new approach measures the respiratory rate by capturing changes in skin brightness from motion artifacts. We utilize these brightness factors, including facial movement, for respiratory signal measurement. We applied the wavelet transform and smoothing filters to remove other unrelated motion artifacts. In order to validate our method, we built a dataset of respiratory rate measurements from 20 individuals using an RGB camera in a facial movement-aware environment. Our approach demonstrated a similar performance level to the reference signal obtained with a contact-based respiratory belt, with a correlation above 0.9 and an MAE within 1 bpm. Moreover, our approach offers advantages for real-time measurements, excluding complex computational processes for measuring optical flow caused by the movement of the chest due to respiration.