Contact Free human heart rate prediction using LWHRPnet deep learning in real time face and wrist videos

Abstract

The SARS-CoV-2 pandemic has highlighted the critical need for remote health monitoring, a method that is expected to remain a key approach for delivering medical care in the future. On the other hand, contactless monitoring of vital signs, such as heart rate (HR), is highly challenging. This is because the amplitude of the physiological signal is quite uncertain and can be readily distorted due to noise. Noise can originate from a variety of sources, including head motions, variations in light, and acquiring appliances. The detection of heart rate without physical touch will become an important necessity in the future to prevent the further spread of the disease. This paper proposes a novel light-weight deep learning architecture of LWHRPnet for the prediction of human heart rate using real-time captured face and wrist videos. For face video-based HR detection, a face detection algorithm is used to segment the forehead region and an overlayed mean frame of the forehead is used in the learning of LWHRPnet regression CNN. For the wrist, we performed the segmentation of the blood vessel region from the whole hand and predicted the HR from the mean frame of the same via the same LWHRPnet. Two parallel processes of HR detection implemented by face and wrist simultaneously and comparing the results of HR prediction in terms of RMSE. This simulation performance is compared with earlier works. We achieved a very low RMSE of 0.4137 and a prediction accuracy of 99.86%.