Spatial averaging method based on adaptive weight for imaging photoplethysmography.

Significance: Imaging photoplethysmography (iPPG) is a non-contact measuring technology for several physiological parameters reflecting personal health status without a special sensor. However, the pulse signal obtained using the iPPG usually is contaminated by various noises, and the intensity of the interesting pulse signal is relatively weak compared to the noises, emphasizing the necessity of obtaining high-quality pulse signals to measure physiological parameters correctly. Aim: Various regions of the face harbor distinct pulse information. We propose a spatial averaging method based on adaptive weights, which can obtain high-quality pulse signals by applying different weights to facial sub-regions of interest (sub-ROIs; sROIs). Approach: First, the facial ROI is divided into seven sROIs and the coarse heart rate (HR) is calculated from them. Next, the signal-to-noise ratio (SNR) of the raw signal obtained from each sROI is calculated using the coarse HR, and then a high-quality pulse signal is obtained by assigning positive or negative weights to each sROI based on the SNRs. Results: We compare our method with others through the quality analysis of the obtained pulse signals using the self-collected database and the public database PURE. The comparison results show that the proposed method can provide a better pulse signal compared to other methods under various resolutions and states. Conclusions: This proposed method can obtain the pulse signal with better quality, which is helpful to accurately measure physiological parameters, such as HR and HR variability.

A real-time heart rate estimation framework based on a facial video while wearing a mask.

BACKGROUND: The imaging photoplethysmography (iPPG) method is a non-invasive, non-contact measurement method that uses a camera to detect physiological indicators. On the other hand, wearing a mask has become essential today when COVID-19 is rampant, which has become a new challenge for heart rate (HR) estimation from facial videos recorded by a camera. OBJECTIVE: The aim is to propose an iPPG-based method that can accurately estimate HR with or without a mask. METHODS: First, the facial regions of interest (ROI) were divided into two sub-ROIs, and the original signal was obtained through spatial averaging with different weights according to the result of judging whether wearing a mask or not, and the CDF, which emphasizes the main component signal, was combined with the improved POS suitable for real-time HR estimation to obtain the noise-removed BVP signal. RESULTS: For self-collected data while wearing a mask, MAE, RMSE, and ACC were 1.09 bpm, 1.44 bpm, and 99.08%, respectively. CONCLUSION: Experimental results show that the proposed framework can estimate HR stably in real-time in both cases of wearing a mask or not. This study expands the application range of HR estimation based on facial videos and has very practical value in real-time HR estimation in daily life.

A New Framework for Robust Heart Rate Measurement Based on the Head Motion State Estimation.

It is of great significance in managing human health, preventing and curing diseases such as heart disease to measure and monitor the physiological parameters accurately and robustly. However, imaging photoplethysmography (iPPG) can be easily affected by the ambient illumination variations or the subject's motions. In this paper, therefore, a novel framework of heart rate (HR) measurement robust to both illumination and motion artefacts is proposed, which combines the projection-plane-switching-based iPPG method (2PS) with the singular spectrum analysis (SSA). Based on the estimation of the head motion state, one reasonable projection plane is firstly determined, the temporally normalized red-green-blue signals are projected onto the plane and a pulse signal is obtained by alpha-tuning. After that, singular spectrum analysis (SSA) is applied to the obtained pulse signal and the normalized B-channel signal of the facial region of interest (ROI) to remove the artefacts remained in the pulse signal. For the self-collected database and the public PURE database, Bland-Altman plots show that the proposed 2PS-SSA has better agreement than the five compared methods, where the mean biases are 0.59 beat per minute (bpm) and 0.034 bpm, with 95% limits from -2.59 bpm to 3.78 bpm and from -1.97 bpm to 2.04 bpm, respectively.

Research on the combination of color channels in heart rate measurement based on photoplethysmography imaging.

SIGNIFICANCE: The measurement of human vital signs based on photoplethysmography imaging (PPGI) can be severely affected by the interference of various factors in the measurement process; therefore, a lot of complex signal processing techniques are used to remove the influence of the interference. AIM: We comprehensively analyze several methods for color channel combination in the color spaces currently used in PPGI and determine the combination method that can improve the quality of the pulse signal, which results in a modified plane-orthogonal-to-skin based method (POS). APPROACH: Based on the analysis of the previous studies, 13 methods for color channel combination in the different color spaces, which can be seen as having potential abilities in measuring vital signs, were compared by employing the average value of signal-to-noise ratio (SNR) and the box-plot in the public databases UBFC-RPPG and PURE. In addition, the pulse signal was extracted through the dual-color space transformation (sRGB → intensity normalized RGB → YCbCr) and fine-tuning on the CbCr plane. RESULTS: Among the 13 methods for color channel combination, the signal extracted by the Cb+Cr combination in the YCbCr color space includes the most pulse information. Furthermore, the average SNR of the modified POS for all the used databases is improved by 69.3% compared to POS. CONCLUSIONS: The methods using prior knowledge are not only simple to calculate but can significantly increase the SNR, which will provide a great help in the practical use of vital sign measurements based on PPGI.

A measurement of illumination variation-resistant noncontact heart rate based on the combination of singular spectrum analysis and sub-band method.

BACKGROUND AND OBJECTIVE: The imaging photoplethysmography method is a non-contact and non-invasive measurement method that usually uses surrounding illumination as an illuminant, which can be easily influenced by the surrounding illumination variations. Thus, it has a practical value to develop an efficient method of heart rate measurement that can remove the interference of illumination variations robustly. METHOD: We propose a novel framework of heart rate measurement that is robust to illumination variations by combining singular spectrum analysis and sub-band method. At first, we extract the blood volume pulse signal by applying the modified sub-band method to the raw facial RGB trace signals. Then the spectra for the interference of illumination variations are extracted from the raw signal obtained from facial regions of interest by using singular spectrum analysis. Finally, we estimate the more robust heart rate through comparison analysis between the spectra of the extracted blood volume pulse signal and the illumination variations. RESULTS: We compared our method with several state-of-the-art methods through the analysis using the self-collected data and the UBFC-RPPG database. Bland-Altman plots and Pearson correlation coefficients pointed out that the proposed method could measure the heart rate more accurately than the state-of-the-art methods. For the self-collected data and the UBFC-RPPG database, Bland-Altman plots showed that proposed method caused better agreement with 95% limits from -4 bpm to 10 bpm and from -2 bpm to 4 bpm respectively than the other state-of-the-art methods. It also revealed that the highly linear relation was held between the estimated heart rate and ground truth with the correlation coefficients of 0.89 and 0.99, respectively. CONCLUSION: By extracting illumination variation directly from the facial region of interest rather than from the background region of interest, the proposed method demonstrates that it can overcome the drawbacks of the previous methods due to the illumination variation difference between the background and facial regions of interest. It can be found that the proposed method has a relatively good robustness regardless of whether illumination variation exists or not.