Search Results (23)

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

Advancements in camera technology over the past two decades have made image-based monitoring increasingly accessible for healthcare applications. Imaging photoplethysmography (iPPG) and remote photoplethysmography (rPPG) are non-invasive methods for measuring vital signs, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, without physical contact. rPPG utilizes basic cameras to detect physiological changes, while rPPG enables remote monitoring by capturing subtle skin colour variations linked to blood flow. Various rPPG techniques, including colour-based, motion-based, multispectral, and depth-based approaches, enhance accuracy and resilience. These technologies are beneficial not only for healthcare but also for fitness tracking, stress management, and security systems, offering a promising future for contactless physiological monitoring. In this article, there is an overview of these methods and their uniqueness for use in remote photoplethysmography. Full article

Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset. Full article

One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson’s correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone. Full article

In today’s neonatal intensive care units, monitoring vital signs such as heart rate and respiration is fundamental for neonatal care. However, the attached sensors and electrodes restrict movement and can cause medical-adhesive-related skin injuries due to the immature skin of preterm infants, which may lead to serious complications. Thus, unobtrusive camera-based monitoring techniques in combination with image processing algorithms based on deep learning have the potential to allow cable-free vital signs measurements. Since the accuracy of deep-learning-based methods depends on the amount of training data, proper validation of the algorithms is difficult due to the limited image data of neonates. In order to enlarge such datasets, this study investigates the application of a conditional generative adversarial network for data augmentation by using edge detection frames from neonates to create RGB images. Different edge detection algorithms were used to validate the input images’ effect on the adversarial network’s generator. The state-of-the-art network architecture Pix2PixHD was adapted, and several hyperparameters were optimized. The quality of the generated RGB images was evaluated using a Mechanical Turk-like multistage survey conducted by 30 volunteers and the FID score. In a fake-only stage, 23% of the images were categorized as real. A direct comparison of generated and real (manually augmented) images revealed that 28% of the fake data were evaluated as more realistic. An FID score of 103.82 was achieved. Therefore, the conducted study shows promising results for the training and application of conditional generative adversarial networks to augment highly limited neonatal image datasets. Full article

The health monitoring system is one of the most innovative technologies that has gained traction in the Internet of Medical Things (IoMT). It allows the connection of multiple sensors and actuators that can capture and monitor the data through the web page or mobile application. IoMT technology not only provides communications but also will provide monitoring, recording, storage, and display. IoMT in healthcare is used for measuring the vital signs of the human body, which allows medical professionals to assess the well-being of a patient. The doctor may recommend lifestyle modifications, prescribe more tests, or diagnose a disorder according to the results. This paper illustrates the remote-control health monitoring system (HMS) with the integration of a UAV, which allows the doctor to access the data and analyze the patient data remotely. Thus, the proposed HMS-UAV system aims to measure the temperature, humidity, blood pressure, heart rate, and SpO₂ and stores the data on the UAV. Several sensors were thus used namely DHT11, MAX30102, Myoware and K24C16, and the Raspberry Pi camera. Reduced hospital stays and avoidance of readmissions are benefits of remote patient monitoring with IoMT-based UAVs. Contrary to its advantages, IoMT has flaws in information processing since a huge volume of data are needed to be handled in a single environment. One major novel inclusion in this work is to measure multiple parameters and provide a comparative analysis for all of them. Furthermore, the functionality of video recorded and stored is included where the doctor can surveil the patient. Full article

Blood pressure (BP) is one of the most common vital signs related to cardiovascular diseases. BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers; however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Therefore, it is clinically important to develop an innovative system that can accurately measure BP without the need for any direct physical contact with the people. This work aims to create a new computer vision system that remotely measures BP using a digital camera without a pressure cuff. The proposed BP system extracts the optical properties of photoplethysmographic signals in two regions in the forehead captured by a digital camera and calculates BP based on specific formulas. The experiments were performed on 25 human participants with different skin tones and repeated at different times under ambient light conditions. Compared to the systolic/diastolic BP readings obtained from a commercial digital sphygmomanometer, the proposed BP system achieves an accuracy of 94.6% with a root mean square error (RMSE) of 9.2 mmHg for systolic BP readings and an accuracy of 95.4% with an RMSE of 7.6 mmHg for diastolic BP readings. Thus, the proposed BP system has the potential of being a promising tool in the upcoming generation of BP monitoring systems. Full article

In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO₂). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO₂, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO₂ are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO₂ is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO₂ monitoring. Full article

We demonstrate a Contactless Vital Sign Monitoring (CVSM) system and road-test the system for in-cabin driver monitoring using a near-infrared indirect Time-of-Flight (ToF) camera. The CVSM measures both heart rate (HR) and respiration rate (RR) by leveraging the simultaneously measured grayscale and depth information from a ToF camera. For a camera-based driver monitoring system (DMS), key challenges from varying background illumination and motion-induced artifacts need to be addressed. In this study, active illumination and depth-based motion compensation are used to mitigate these two challenges. For HR measurements, active illumination allows the system to work under various lighting conditions, while our depth-based motion compensation has the advantage of directly measuring the motion of the driver without making prior assumptions about the motion artifacts. In addition, we can extract RR directly from the chest wall motion, circumventing the challenge of acquiring RR from the near-infrared photoplethysmography (PPG) signal of low signal quality. We investigate the system’s performance in various scenarios, including monitoring both drivers and passengers while driving on highways and local roads. Our results show that our CVSM system is ambient light agnostic, and the success rates of HR measurements on the highway are 82% and 71.9% for the passenger and driver, respectively. At the same time, we show that the system can measure RR on users driving on a highway with a mean deviation of −1.4 breaths per minute (BPM). With reliable HR and RR measurement in the vehicle, the CVSM system could one day be a key enabler to sudden sickness or drowsiness detection in DMS. Full article

Premature infants are among the most vulnerable patients in a hospital. Due to numerous complications associated with immaturity, a continuous monitoring of vital signs with a high sensitivity and accuracy is required. Today, wired sensors are attached to the patient’s skin. However, adhesive electrodes can be potentially harmful as they can damage the very thin immature skin. Although unobtrusive monitoring systems using cameras show the potential to replace cable-based techniques, advanced image processing algorithms are data-driven and, therefore, need much data to be trained. Due to the low availability of public neonatal image data, a patient phantom could help to implement algorithms for the robust extraction of vital signs from video recordings. In this work, a camera-based system is presented and validated using a neonatal phantom, which enabled a simulation of common neonatal pathologies such as hypo-/hyperthermia and brady-/tachycardia. The implemented algorithm was able to continuously measure and analyze the heart rate via photoplethysmography imaging with a mean absolute error of 0.91 bpm, as well as the distribution of a neonate’s skin temperature with a mean absolute error of less than 0.55 °C. For accurate measurements, a temperature gain offset correction on the registered image from two infrared thermography cameras was performed. A deep learning-based keypoint detector was applied for temperature mapping and guidance for the feature extraction. The presented setup successfully detected several levels of hypo- and hyperthermia, an increased central-peripheral temperature difference, tachycardia and bradycardia. Full article

In this study, a contactless vital signs monitoring system was proposed, which can measure body temperature (BT), heart rate (HR) and respiration rate (RR) for people with and without face masks using a thermal and an RGB camera. The convolution neural network (CNN) based face detector was applied and three regions of interest (ROIs) were located based on facial landmarks for vital sign estimation. Ten healthy subjects from a variety of ethnic backgrounds with skin colors from pale white to darker brown participated in several different experiments. The absolute error (AE) between the estimated HR using the proposed method and the reference HR from all experiments is $2.70\pm 2.28$ beats/min (mean ± std), and the AE between the estimated RR and the reference RR from all experiments is $1.47\pm 1.33$ breaths/min (mean ± std) at a distance of 0.6–1.2 m. Full article

This study describes a contactless vital sign monitoring (CVSM) system capable of measuring heart rate (HR) and respiration rate (RR) using a low-power, indirect time-of-flight (ToF) camera. The system takes advantage of both the active infrared illumination as well as the additional depth information from the ToF camera to compensate for the motion-induced artifacts during the HR measurements. The depth information captures how the user is moving with respect to the camera and, therefore, can be used to differentiate where the intensity change in the raw signal is from the underlying heartbeat or motion. Moreover, from the depth information, the system can acquire respiration rate by directly measuring the motion of the chest wall during breathing. We also conducted a pilot human study using this system with 29 participants of different demographics such as age, gender, and skin color. Our study shows that with depth-based motion compensation, the success rate (system measurement within 10% of reference) of HR measurements increases to 75%, as compared to 35% when motion compensation is not used. The mean HR deviation from the reference also drops from 21 BPM to −6.25 BPM when we apply the depth-based motion compensation. In terms of the RR measurement, our system shows a mean deviation of 1.7 BPM from the reference measurement. The pilot human study shows the system performance is independent of skin color but weakly dependent on gender and age. Full article

Remote sensing of vital signs has been developed to improve the measurement environment by using a camera without a skin-contact sensor. The camera-based method is based on two concepts, namely color and motion. The color-based method, remote photoplethysmography (RPPG), measures the color variation of the face generated by reflectance of blood, whereas the motion-based method, remote ballistocardiography (RBCG), measures the subtle motion of the head generated by heartbeat. The main challenge of remote sensing is overcoming the noise of illumination variance and motion artifacts. The studies on remote sensing have focused on the blind source separation (BSS) method for RGB colors or motions of multiple facial points to overcome the noise. However, they have still been limited in their real-world applications. This study hypothesized that BSS-based combining of colors and the motions can improve the accuracy and feasibility of remote sensing in daily life. Thus, this study proposed a fusion method to estimate heart rate based on RPPG and RBCG by the BSS methods such as ensemble averaging (EA), principal component analysis (PCA), and independent component analysis (ICA). The proposed method was verified by comparing it with previous RPPG and RBCG from three datasets according to illumination variance and motion artifacts. The three main contributions of this study are as follows: (1) the proposed method based on RPPG and RBCG improved the remote sensing with the benefits of each measurement; (2) the proposed method was demonstrated by comparing it with previous methods; and (3) the proposed method was tested in various measurement conditions for more practical applications. Full article

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations. Full article

Non-contact visual monitoring of vital signs in neonatology has been demonstrated by several recent studies in ideal scenarios where the baby is calm and there is no medical or parental intervention. Similar to contact monitoring methods (e.g., ECG, pulse oximeter) the camera-based solutions suffer from motion artifacts. Therefore, during care and the infants’ active periods, calculated values typically differ largely from the real ones. In this way, our main contribution to existing remote camera-based techniques is to detect and classify such situations with a high level of confidence. Our algorithms can not only evaluate quiet periods, but can also provide continuous monitoring. Altogether, our proposed algorithms can measure pulse rate, breathing rate, and to recognize situations such as medical intervention or very active subjects using only a single camera, while the system does not exceed the computational capabilities of average CPU-GPU-based hardware. The performance of the algorithms was evaluated on our database collected at the I^st Dept. of Neonatology of Pediatrics, Dept of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary. Full article