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.
Most of the mentioned studies only focused on average HR measurement. The HR counts the total number of heartbeats in a given time period, which is coarse for describing the cardiac activity. On the other side, HRV features describe heart activity on a much finer scale, which are computed by analysing the IBI of pulse signals. Most common HRV features include low frequency (LF), high frequency (HF), and their ratio of LF HF and more, which are widely used in most medical applications. Besides, the respiratory frequency (RF) can also be estimated by analyzing the frequency power of IBIs, as in @cite_3 and @cite_4 . Apparently, compared with the task of estimating the average HR (only one number), measuring HRV features is more challenging, which requires accurate measure of the time location of each individual pulse peak. For the needs of most healthcare applications, average HR is far from enough. We need to step forward to develop methods that can measure heart activity on HRV level.
@cite_4 @cite_3
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Physiological signals, including heart rate (HR), heart rate variability (HRV), and respiratory frequency (RF) are important indicators of our health, which are usually measured in clinical examinations. Traditional physiological signal measurement often involves contact sensors, which may be inconvenient or cause discomfort in long-term monitoring sessions. Recently, there were studies exploring remote HR measurement from facial videos, and several methods have been proposed. However, previous methods cannot be fairly compared, since they mostly used private, self-collected small datasets as there has been no public benchmark database for the evaluation. Besides, we haven't found any study that validates such methods for clinical applications yet, e.g., diagnosing cardiac arrhythmias disease, which could be one major goal of this technology. In this paper, we introduce the Oulu Bio-Face (OBF) database as a benchmark set to fill in the blank. The OBF database includes large number of facial videos with simultaneously recorded reference physiological signals. The data were recorded both from healthy subjects and from patients with atrial fibrillation (AF), which is the most common sustained and widespread cardiac arrhythmia encountered in clinical practice. Accuracy of HR, HRV and RF measured from OBF videos are provided as the baseline results for future evaluation. We also demonstrated that the video-extracted HRV features can achieve promising performance for AF detection, which has never been studied before. From a wider outlook, the remote technology may lead to convenient self-examination in mobile condition for earlier diagnosis of the arrhythmia. We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.