Blood pressure estimation by spatial pulse-wave dynamics in a facial video

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

Monte Carlo simulation of the effect of melanin concentration on light-tissue interactions in transmittance and reflectance finger photoplethysmography

Photoplethysmography (PPG) uses light to detect volumetric changes in blood, and is integrated into many healthcare devices to monitor various physiological measurements. However, an unresolved limitation of PPG is the effect of skin pigmentation on the signal and its impact on PPG based applications such as pulse oximetry. Hence, an in-silico model of the human finger was developed using the Monte Carlo (MC) technique to simulate light interactions with different melanin concentrations in a human finger, as it is the primary determinant of skin pigmentation. The AC/DC ratio in reflectance PPG mode was evaluated at source-detector separations of 1 mm and 3 mm as the convergence rate (Q), a parameter that quantifies the accuracy of the simulation, exceeded a threshold of 0.001. At a source-detector separation of 3 mm, the AC/DC ratio of light skin was 0.472 times more than moderate skin and 6.39 than dark skin at 660 nm, and 0.114 and 0.141 respectively at 940 nm. These findings are significant for the development of PPG-based sensors given the ongoing concerns regarding the impact of skin pigmentation on healthcare devices.

Fair non-contact blood pressure estimation using imaging photoplethysmography

Hypertension is typically manifested as a latent symptom that requires detection through specialized equipment. This poses an inconvenience for individuals who need to undergo long-term blood pressure monitoring in their daily lives. Therefore, there is a need for a portable, non-contact method for estimating blood pressure. However, current non-contact blood pressure estimation methods often rely on relatively narrow datasets, lacking a broad range of blood pressure distributions. Additionally, their applicability is confined to controlled experimental environments. This study proposes a non-contact blood pressure estimation method suitable for various life scenarios, encompassing multiple age groups, diverse ethnicities, and individuals with different skin tones. The aim is to enhance the practicality and accuracy of existing non-contact blood pressure estimation methods. The research extracts the imaging photoplethysmogram (IPPG) signal from facial videos and processes the signal through four layers of filtering operations to obtain an IPPG signal reflecting pulse wave variations. A CNN+BiLSTM+GRU network structure is constructed to improve the accuracy of current non-contact blood pressure estimation methods. In comparison to existing approaches, the mean absolute error (MAE) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) is reduced by 13.6% and 16.4%, respectively.

Robust blood pressure measurement from facial videos in diverse environments

Blood pressure (BP) management is important worldwide, and BP monitoring is a crucial aspect of maintaining good health. Traditional BP meter measures BP independently in various situations, such as at home or work, using a cuff to maintain a stable condition. However, these devices can causes a foreign body sensation and discomfort, and are not always practical for periodic monitoring. As a result, studies have been conducted on the use of photoplethysmography (PPG) for measuring BP. However, PPG also has limitations similar to those of traditional BP meters, as it requires the placement of sensors on two regions of the body (fingers or toes). To address this issue, researchers have conducted studies on non-contact methods for measuring BP using face and hand videos. These studies have utilized two cameras to measure PTT and have focused on internal environments, resulting in low accuracy of BP measurement in external environments. We proposes a method for robust BP measurement using pulse wave velocity (PWV) and PTT calculated from facial videos. PTT is estimated by measuring the phase difference between two different regions of interest (ROIs) and PWV is calculated using PTT and the actual distance between two ROIs. In addition, our proposed method extracts the pulse wave from the ROI to measure BP. The actual distance between the ROIs and PTT are estimated using the two extracted pulse waves, and BP is then measured using PWV and PTT. To evaluate the BP measurement performance, the BP calculated from both BP meters and facial videos (in indoor, outdoor, driving car, and flying drone environments) are compared. Our results reveal that the proposed method can robustly measure BP in diverse environments.