Recently average heart rate (HR) can be measured relatively accurately from human face videos based on non-contact remote photoplethysmography (rPPG). However in many healthcare applications, knowing only the average HR is not enough, and measured blood volume pulse signal and its heart rate variability (HRV) features are also important. We propose the first end-to-end rPPG signal recovering system (PhysNet) using deep spatio-temporal convolutional networks to measure both HR and HRV features. PhysNet extracts the spatial and temporal hidden features simultaneously from raw face sequences while outputs the corresponding rPPG signal directly. The temporal context information helps the network learn more robust features with less fluctuation. Our approach was tested on two datasets, and achieved superior performance of HR and HRV features comparing to the state-of-the-art methods.
In the past few years, several studies explored measuring HR remotely from face videos by analysing facial color changes. @cite_9 introduced independent component analysis to decompose the original RGB channel signals into independent non-Gaussian signals to measure HR.
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Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.