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摘要
Physiological signals, such as heart rate (HR), respiration frequency (RF), and heart rate variability (HRV), are important clues to analyze a person's health and affective status. Traditional measurements of physiological signals are based on the electrocardiography (ECG) or contact photoplethysmography (cPPG) technology. However, both technologies require professional equipment, which may cause inconvenience and discomfort for subjects. Remote photoplethysmography (rPPG) technology for remote measurement of physiological signals has progressed considerably and recently attracted considerable research attention. The rPPG technology, which is based on skin color variations due to the periodical optical absorption of skin tissue caused by cardiac activity, demonstrates high potential in many applications, such as healthcare, sleep monitoring, and defection detection. The process for rPPG-based physiological measurement can be divided into three steps. First, regions of interest (ROIs) are extracted from the face video. Second, blood volume pulse(BVP) signal is reconstructed from signals generated from the ROIs. Finally, the reconstructed BVP signal is used for physiological measurements. The reconstruction of the BVP signal is the key step for rPPG-based remote physiological measurements. A detailed review of methods for rPPG-based remote physiological measurement is presented in this study from the aspect of assumptions they use, which can be categorized into three kinds, i.e., methods based on the skin reflection model, methods based on the BVP signal's physical characteristics, and data-driven methods. Studies on the skin reflection model-based methods can be further categorized into spatial skin and skin reflection models of different color channels. Studies on methods that using the BVP signal's physical characteristics can be further categorized into blind signal separation, manifold projection, low rank factorization, and frequency domain constraint. Studies on data-driven methods can be further categorized into methods based on hand-crafted features and deep learning. A detailed review of evaluations of different rPPG-based physiological measurement methods is also presented from the aspects of tasks, databases, metrics, and protocols. Evaluation tasks used for remote physiological measurement include average heart rate measurement, respiration frequency measurement, and heart rate variability analysis. Databases of rPPG-based physiological measurements are summarized according to database scale and variations. Evaluation metrics for remote physiological measurement can be categorized into statistics of error, correlation, and signal quality. Evaluation protocols for data-driven methods are summarized into fixed partition, subject-independent division, subject-exclusive division, and cross-database protocols. Finally, we discuss the challenges of the rPPG-based remote physiological measurement and put forward the potential research directions for future investigations. Challenges include video quality (i.e., video compression and pre-processing of frames), influence of subject's head movements, variations of lighting conditions, and lacking data. Future research trends include designing hand-crafted methods for different challenge scenarios and exploring technologies, such as self-supervised, semi-supervised, and weakly-supervised learning, for data-driven methods.
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单位中国科学院; 中国科学院大学
