PhysioSens1D-NET: A 1D Convolution Network for Extracting Heart Rate from Facial Videos.

Non-contact heart rate (HR) monitoring from video streams is the most established approach to unobtrusive vitals monitoring. A multitude of classical signal processing algorithms and cutting-edge deep learning models have been developed for non-contact HR extraction. Classical signal processing algorithms excel in real-time application, even on low-end CPUs, while deep learning models offer higher accuracy at the cost of computational complexity. In this study, we introduce PhysioSens1DNET- a novel 1D convolutional neural network, that deliver both computational efficiency and accurate HR measures. In contrast to classical rPPG algorithms like ICA, POS, CHROM, PBV, LGI, and GREEN, the PhysioSens1D-NET demonstrates significant improvements, achieving reductions in Mean Absolute Error (MAE) by 91.4%, 72.5%, 70.7%, 93.1%, 76.7%, and 95.1%, respectively. When compared to state-of-the-art deep learning models, including DeepPhys, EfficientNet, PhysNet, and TS-CAN, our 1D-NET exhibits comparable performance. A performance analysis on low specification CPU's, indicated that PhysioSens1DNET outperforms deep learning models, showcasing a considerable speed advantage-being 180 times faster than the bestperforming DL model. Furthermore, our 1D-NET aligns closely with classical algorithms with a computational time of only 2.3 ms.