LGI-rPPG-Net: A shallow encoder-decoder model for rPPG signal estimation from facial video streams

Abstract

A method to accurately estimate physiological signals from video streams at a minimal cost is invaluable. The importance of such a technique in pre-clinical health monitoring cannot be understated. Remote photoplethysmography (rPPG) can be used as a substitute for finger photoplethysmography (PPG) when such sensors are not recommended, such as for burn victims, premature babies, and patients with sensitive skin. Good quality rPPG signal that is highly correlated to finger PPG can be used to estimate many vital health signs. In this work, a shallow encoder-decoder architecture, LGI-rPPG-Net is proposed. The proposed model aims to produce highly correlated rPPG signals which can be substituted for finger PPG. In the reconstruction of rPPG, the model achieved a very good Pearson’s Correlation Coefficient (PCC), Root Mean Squared Error (RMSE), and dynamic time warping distance of 0.862, 0.148, and 0.699, respectively. This highly correlated rPPG was compared to finger PPG by calculating heart rate from rPPG and finger PPG. The model achieved a PCC of 0.984 and RMSE, and MAE of 2.91, 1.51 beats per minute (BPM), respectively. LGI-rPPG-Net model with video streaming to predict rPPG can thus be used as a replacement for finger PPG where in-contact collection is not feasible.