A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning

Detection of hypertension using a target spectral camera: a prospective clinical study

Hypertension is a significant contributor to premature mortality, and the regular monitoring of blood pressure (BP) enables the early detection of hypertension and cardiovascular disease. There is an urgent need for the development of highly accurate cuffless BP devices. We examined BP measurements based on a target spectral camera's recordings and evaluated their accuracy. Images of 215 adults' palms and faces were recorded, and BP was measured. The camera captured RGB wavelength data at 640 × 480 pixels and 150 frames per second (fps). These recordings were analyzed to extract pulse transit time (PTT) values between the face and palm, a key parameter for estimating BP. Continuous BP measurements were taken using a CNAPmonitor500 for validation. Three frequency wavelengths were measured from video images. A machine learning model was constructed to determine hypertension, defined as a systolic BP of 130 mmHg or higher or a diastolic BP of 80 mmHg or higher, using the visualized data. The discrimination between hypertension and normal BP was 95.0% accurate within 30 s and 90.3% within 5 s, based on the captured images. The results of heartbeat-by-heartbeat analyses can be used to determine hypertension based on only one second of camera footage or one heartbeat. The data extracted from a video recorded by a target spectral camera enabled accurate hypertension diagnoses, suggesting the potential for simplified BP monitoring.

Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment

Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy. Here, we review seven widely used measures to assess PPG-based algorithm performance and recommend implementing standardized error evaluation steps in their development. This standardization can reduce bias and improve the reliability and accuracy of PPG-based BP estimation, leading to better health outcomes for patients managing hypertension.

Application of Independent Component Analysis and Nelder-Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation

In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder-Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.

Preserving shape details of pulse signals for video-based blood pressure estimation

In recent years, imaging photoplethysmograph (iPPG) pulse signals have been widely used in the research of non-contact blood pressure (BP) estimation, in which BP estimation based on pulse features is the main research direction. Pulse features are directly related to the shape of pulse signals while iPPG pulse signals are easily disturbed during the extraction process. To mitigate the impact of pulse feature distortion on BP estimation, it is necessary to eliminate interference while retaining valuable shape details in the iPPG pulse signal. Contact photoplethysmograph (cPPG) pulse signals measured at rest can be considered as the undisturbed reference signal. Transforming the iPPG pulse signal to the corresponding cPPG pulse signal is a method to ensure the effectiveness of shape details. However, achieving the required shape accuracy through direct transformation from iPPG to the corresponding cPPG pulse signals is challenging. We propose a method to mitigate this challenge by replacing the reference signal with an average cardiac cycle (ACC) signal, which can approximately represent the shape information of all cardiac cycles in a short time. A neural network using multi-scale convolution and self-attention mechanisms is developed for this transformation. Our method demonstrates a significant improvement in the maximal information coefficient (MIC) between pulse features and BP values, indicating a stronger correlation. Moreover, pulse signals transformed by our method exhibit enhanced performance in BP estimation using different model types. Experiments are conducted on a real-world database with 491 subjects in the hospital, averaging 60 years of age.

Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter

This study presents a non-contact approach to measuring heart rate and blood pressure using an image photoplethysmography (iPPG) signal, and compares the results to those from an oscillometric blood pressure meter. Facial videos of 100 subjects were recorded via a webcam under ambient lighting conditions to extract iPPG signals. The results revealed a strong correlation between the heart rate derived from iPPG and that obtained from an oscillometric blood pressure meter. In addition, a continuous wavelet transform images with a 6-s duration were used as input for a custom convolutional neural network model, providing the most accurate blood pressure estimation. The proposed method received a grade A for diastolic and grade B for systolic blood pressure based on the British Hypertension Society's criteria. It also met the standards set by the Association for the Advancement of Medical Instrumentation. This non-contact framework shows promising potential for efficient screening purposes.

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.

Contactless Blood Pressure Measurement Via Remote Photoplethysmography With Synthetic Data Generation Using Generative Adversarial Networks

Remote photoplethysmography (rPPG) has been used to measure vital signs such as heart rate, heart rate variability, blood pressure (BP), and blood oxygen. Recent studies adopt features developed with photoplethysmography (PPG) to achieve contactless BP measurement via rPPG. These features can be classified into two groups: time or phase differences from multiple signals, or waveform feature analysis from a single signal. Here we devise a solution to extract the time difference information from the rPPG signal captured at 30 FPS. We also propose a deep learning model architecture to estimate BP from the extracted features. To prevent overfitting and compensate for the lack of data, we leverage a multi-model design and generate synthetic data. We also use subject information related to BP to assist in model learning. For real-world usage, the subject information is replaced with values estimated from face images, with performance that is still better than the state-of-the-art. To our best knowledge, the improvements can be achieved because of: 1) the model selection with estimated subject information, 2) replacing the estimated subject information with the real one, 3) the InfoGAN assistance training (synthetic data generation), and 4) the time difference features as model input. To evaluate the performance of the proposed method, we conduct a series of experiments, including dynamic BP measurement for many single subjects and nighttime BP measurement with infrared lighting. Our approach reduces the MAE from 15.49 to 8.78 mmHg for systolic blood pressure (SBP) and 10.56 to 6.16 mmHg for diastolic blood pressure(DBP) on a self-constructed rPPG dataset. On the Taipei Veterans General Hospital(TVGH) dataset for nighttime applications, the MAE is reduced from 21.58 to 11.12 mmHg for SBP and 9.74 to 7.59 mmHg for DBP, with improvement ratios of 48.47% and 22.07% respectively.

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring

Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.

Anti-motion imaging photoplethysmography via self-adaptive multi-ROI tracking and selection

Objective.The imaging photoplethysmography (IPPG) technique allows people to measure heart rate (HR) from face videos. However, motion artifacts caused by rigid head movements and nonrigid facial muscular movements are one of the key challenges.Approach.This paper proposes a self-adaptive region of interest (ROI) pre-tracking and signal selection method to resist motion artifacts. Based on robust facial landmark detection, we split the whole facial skin (including the forehead, cheeks, and chin) symmetrically into small circular regions. And two symmetric sub-regions constitute a complete ROI. These ROIs are tracked and the motion state is simultaneously assessed to automatically determine the visibility of these ROIs. The obscured or invisible sub-regions will be discarded while the corresponding symmetric sub-regions will be retained as available ROIs to ensure the continuity of the IPPG signal. In addition, based on the frequency spectrum features of IPPG signals extracted from different ROIs, a self-adaptive selection module is constructed to select the optimum IPPG signal for HR calculation. All these operations are updated per frame dynamically for the real-time monitor.Results.Experimental results on the four public databases show that the IPPG signal derived by our proposed method exhibits higher quality for more accurate HR estimation. Compared with the previous method, metrics of the evaluated HR value on our approach demonstrates superior or comparable performance on PURE, VIPL-HR, UBFC-RPPG and MAHNOB-HCI datasets. For instance, the RMSEs on PURE, VIPL-HR, and UBFC-RPPG datasets decrease from 4.29, 7.62, and 3.80 to 4.15, 3.87, and 3.35, respectively.Significance.Our proposed method can help enhance the robustness of IPPG in real applications, especially given motion disturbances.

A new method of continuous blood pressure monitoring using multichannel sensing signals on the wrist

Hypertension is a worldwide health problem and a primary risk factor for cardiovascular disease. Continuous monitoring of blood pressure has important clinical value for the early diagnosis and prevention of cardiovascular disease. However, existing technologies for wearable continuous blood pressure monitoring are usually inaccurate, rely on subject-specific calibration and have poor generalization across individuals, which limit their practical applications. Here, we report a new blood pressure measurement method and develop an associated wearable device to implement continuous blood pressure monitoring for new subjects. The wearable device detects cardiac output and pulse waveform features through dual photoplethysmography (PPG) sensors worn on the palmar and dorsal sides of the wrist, incorporating custom-made interface sensors to detect the wearing contact pressure and skin temperature. The detected multichannel signals are fused using a machine-learning algorithm to estimate continuous blood pressure in real time. This dual PPG sensing method effectively eliminates the personal differences in PPG signals caused by different people and different wearing conditions. The proposed wearable device enables continuous blood pressure monitoring with good generalizability across individuals and demonstrates promising potential in personal health care applications.

VidBP: Detecting Blood Pressure from Facial Videos with Personalized Calibration

Recent studies have found that blood volume pulse (BVP) in facial videos contains features highly correlated to blood pressure (BP). However, the mapping from BVP features to BP varies from person to person. To address this issue, VidBP has been proposed as a BP detector that can be calibrated based on an individual's data. VidBP is pre-trained on a large dataset to extract BP-related features from BVP. Then, BVP samples and BP labels of an individual are fed into the pre-trained VidBP to create a personal dictionary of BP-related features. When estimating the individual's BP, the current BP-related feature is compared to the features saved in the dictionary, and the BP labels of the similar features are considered as the BP estimate. The performance of VidBP was evaluated on 640 samples of 16 subjects, and it demonstrated significantly lower errors in BP estimation compared to state-of-the-art methods. The personalized calibration of VidBP is a significant advantage, enabling it to better capture the unique mapping from BVP features to BP for each individual.Clinical relevance This study reports a feasible method to estimate BP from facial videos, providing a convenient and cost-effective way for home BP monitoring.

Camera Wavelength Selection for Multi-wavelength Pulse Transit Time based Blood Pressure Monitoring

Multi-wavelength pulse transmit time (MV-PTT) is a potential tool for remote blood pressure (BP) monitoring. It uses two wavelengths, typically green (G) and near-infrared (NIR), that have different skin penetration depths to measure the PTT between artery and arterioles of a single site of the skin for BP estimation. However, the impact of wavelength selection for MV-PTT based BP calibration is unknown. In this paper, we explore the combination of different wavelengths of camera photoplethysmography for BP measurement using a modified narrow-band camera centered at G-550/R-660/NIR-850 nm, especially focused on the comparison between G-R (full visible) and G-NIR (hybrid). The experiment was conducted on 17 adult participants in a dark chamber with their BP significantly changed by the protocol of ice water stimulation. The experimental results show that the MV-PTT obtained by G-NIR has a higher correlation with BP, and the fitted model has lower MAE in both the systolic pressure (5.78 mmHg) and diastolic pressure (6.67 mmHg) than others. It is confirmed that a hybrid wavelength of visible (G) and NIR is still essential for accurate BP calibration due to their difference in skin penetration depth that allows proper sensing of different skin layers for this measurement.

Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research

In this paper, a multi-stage deep learning blood pressure prediction model based on imaging photoplethysmography (IPPG) signals is proposed to achieve accurate and convenient monitoring of human blood pressure. A camera-based non-contact human IPPG signal acquisition system is designed. The system can perform experimental acquisition under ambient light, effectively reducing the cost of non-contact pulse wave signal acquisition while simplifying the operation process. The first open-source dataset IPPG-BP for IPPG signal and blood pressure data is constructed by this system, and a multi-stage blood pressure estimation model combining a convolutional neural network and bidirectional gated recurrent neural network is designed. The results of the model conform to both BHS and AAMI international standards. Compared with other blood pressure estimation methods, the multi-stage model automatically extracts features through a deep learning network and combines different morphological features of diastolic and systolic waveforms, which reduces the workload while improving accuracy.

Photoplethysmography upon cold stress-impact of measurement site and acquisition mode

Photoplethysmography (PPG) allows various statements about the physiological state. It supports multiple recording setups, i.e., application to various body sites and different acquisition modes, rendering the technique a versatile tool for various situations. Owing to anatomical, physiological and metrological factors, PPG signals differ with the actual setup. Research on such differences can deepen the understanding of prevailing physiological mechanisms and path the way towards improved or novel methods for PPG analysis. The presented work systematically investigates the impact of the cold pressor test (CPT), i.e., a painful stimulus, on the morphology of PPG signals considering different recording setups. Our investigation compares contact PPG recorded at the finger, contact PPG recorded at the earlobe and imaging PPG (iPPG), i.e., non-contact PPG, recorded at the face. The study bases on own experimental data from 39 healthy volunteers. We derived for each recording setup four common morphological PPG features from three intervals around CPT. For the same intervals, we derived blood pressure and heart rate as reference. To assess differences between the intervals, we used repeated measures ANOVA together with paired t-tests for each feature and we calculated Hedges' g to quantify effect sizes. Our analyses show a distinct impact of CPT. As expected, blood pressure shows a highly significant and persistent increase. Independently of the recording setup, all PPG features show significant changes upon CPT as well. However, there are marked differences between recording setups. Effect sizes generally differ with the finger PPG showing the strongest response. Moreover, one feature (pulse width at half amplitude) shows an inverse behavior in finger PPG and head PPG (earlobe PPG and iPPG). In addition, iPPG features behave partially different from contact PPG features as they tend to return to baseline values while contact PPG features remain altered. Our findings underline the importance of recording setup and physiological as well as metrological differences that relate to the setups. The actual setup must be considered in order to properly interpret features and use PPG. The existence of differences between recording setups and a deepened knowledge on such differences might open up novel diagnostic methods in the future.

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets

It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.

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

We propose a remote method to estimate continuous blood pressure (BP) based on spatial information of a pulse-wave as a function of time. By setting regions of interest to cover a face in a mutually exclusive and collectively exhaustive manner, RGB facial video is converted into a spatial pulse-wave signal. The spatial pulse-wave signal is converted into spatial signals of contours of each segmented pulse beat and relationships of each segmented pulse beat. The spatial signal is represented as a time-continuous value based on a representation of a pulse contour in a time axis and a phase axis and an interpolation along with the time axis. A relationship between the spatial signals and BP is modeled by a convolutional neural network. A dataset was built to demonstrate the effectiveness of the proposed method. The dataset consists of continuous BP and facial RGB videos of ten healthy volunteers. The results show an adequate estimation of the performance of the proposed method when compared to the ground truth in mean BP, in both the correlation coefficient (0.85) and mean absolute error (5.4 mmHg). For comparison, the dataset was processed using conventional pulse features, and the estimation error produced by our method was significantly lower. To visualize the root source of the BP signals used by our method, we have visualized spatial-wise and channel-wise contributions to the estimation by the deep learning model. The result suggests the spatial-wise contribution pattern depends on the blood pressure, while the pattern of pulse contour-wise contribution pattern reflects the relationship between percussion wave and dicrotic wave.

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

Effectiveness of Remote PPG Construction Methods: A Preliminary Analysis

The contactless recording of a photoplethysmography (PPG) signal with a Red-Green-Blue (RGB) camera is known as remote photoplethysmography (rPPG). Studies have reported on the positive impact of using this technique, particularly in heart rate estimation, which has led to increased research on this topic among scientists. Therefore, converting from RGB signals to constructing an rPPG signal is an important step. Eight rPPG methods (plant-orthogonal-to-skin (POS), local group invariance (LGI), the chrominance-based method (CHROM), orthogonal matrix image transformation (OMIT), GREEN, independent component analysis (ICA), principal component analysis (PCA), and blood volume pulse (PBV) methods) were assessed using dynamic time warping, power spectrum analysis, and Pearson's correlation coefficient, with different activities (at rest, during exercising in the gym, during talking, and while head rotating) and four regions of interest (ROI): the forehead, the left cheek, the right cheek, and a combination of all three ROIs. The best performing rPPG methods in all categories were the POS, LGI, and OMI methods; each performed well in all activities. Recommendations for future work are provided.

Blood pressure monitoring techniques in the natural state of multi-scenes: A review

Blood pressure is one of the basic physiological parameters of human physiology. Frequent and repeated measurement of blood pressure along with recording of environmental or other physiological parameters when measuring blood pressure may reveal important cardiovascular risk factors that can predict occurrence of cardiovascular events. Currently, wearable non-invasive blood pressure measurement technology has attracted much research attention. Several different technical routes have been proposed to solve the challenge between portability or continuity of measurement methods and medical level accuracy of measurement results. The accuracy of blood pressure measurement technology based on auscultation and oscillography has been clinically verified, while majority of other technical routes are being explored at laboratory or multi-center clinical demonstration stage. Normally, Blood pressure measurement based on oscillographic method outside the hospital can only be measured at intervals. There is a need to develop techniques for frequent and high-precision blood pressure measurement under natural conditions outside the hospital. In this paper, we discussed the current status of blood pressure measurement technology and development trends of blood pressure measurement technology in different scenarios. We focuses on the key technical challenges and the latest advances in the study of miniaturization devices based on oscillographic method at wrist and PTT related method at finger positions as well as technology processes. This study is of great significance to the application of high frequency blood pressure measurement technology.

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

The measurement of physiological parameters is fundamental to assess the health status of an individual. The contactless monitoring of vital signs may provide benefits in various fields of application, from healthcare and clinical setting to occupational and sports scenarios. Recent research has been focused on the potentiality of camera-based systems working in the visible range (380-750 nm) for estimating vital signs by capturing subtle color changes or motions caused by physiological activities but invisible to human eyes. These quantities are typically extracted from videos framing some exposed body areas (e.g., face, torso, and hands) with adequate post-processing algorithms. In this review, we provided an overview of the physiological and technical aspects behind the estimation of vital signs like respiratory rate, heart rate, blood oxygen saturation, and blood pressure from digital images as well as the potential fields of application of these technologies. Per each vital sign, we provided the rationale for the measurement, a classification of the different techniques implemented for post-processing the original videos, and the main results obtained during various applications or in validation studies. The available evidence supports the premise of digital cameras as an unobtrusive and easy-to-use technology for physiological signs monitoring. Further research is needed to promote the advancements of the technology, allowing its application in a wide range of population and everyday life, fostering a biometrical holistic of the human body (BHOHB) approach.

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

Regular blood pressure (BP) monitoring enables earlier detection of hypertension and reduces cardiovascular disease. Cuff-based BP measurements require equipment that is inconvenient for some individuals and deters regular home-based monitoring. Since smartphones contain sensors such as video cameras that detect arterial pulsations, they could also be used to assess cardiovascular health. Researchers have developed a variety of image processing and machine learning techniques for predicting BP via smartphone or video camera. This review highlights research behind smartphone and video camera methods for measuring BP. These methods may in future be used at home or in clinics, but must be tested over a larger range of BP and lighting conditions. The review concludes with a discussion of the advantages of the various techniques, their potential clinical applications, and future directions and challenges. Video cameras may potentially measure multiple cardiovascular metrics including and beyond BP, reducing the risk of cardiovascular disease.