Non-contact Heart Rate Measurement Research Articles

First noncontact millimeter-wave radar measurement of heart rate in great apes: Validation in chimpanzees (Pan troglodytes).

Heart rate is a crucial vital sign and a valuable indicator for assessing the physical and psychological condition of a target animal. Heart rate contributes to (1) fundamental information for cognitive research, (2) an indicator of psychological and physical stress, and (3) improving the animal welfare of captive animals, especially in nonhuman primate studies. Heart rate has been measured using a contact-type device; however, the device burdens the target animals and that there are risks associated with anesthesia during installation. This study explores the application of heartbeat measurement techniques using millimeter-wave radar, primarily developed for humans, as a remote and noninvasive method for measuring the heart rate of nonhuman primates. Through a measurement test conducted on two chimpanzees, we observed a remarkable correspondence between the peak frequency spectrum of heart rate estimated using millimeter-wave radar and the mean value obtained from electrocardiograph data, thereby validating the accuracy of the method. To the best of our knowledge, this is the first demonstration of the precise measurement of great apes' heart rate using millimeter-wave radar technology. Compared to heart rate measurement using video analysis, the method using millimeter-wave radar has the advantage that it is less susceptible to weather and lighting conditions and that measurement techniques for multiple individuals have been developed for human subjects, while its disadvantage is that validation of measurement from long distances has not been completed. Another disadvantage common to both methods is that measurement becomes difficult when the movement of the target individual is large. The possibility of noncontact measurement of heart rate in wild and captive primates will undoubtedly open up a new research area while taking animal welfare into consideration.

Non-contact heart rate measurement using low-cost RGB camera under complex light conditions

Non-contact heart rate measurement using low-cost RGB camera under complex light conditions

Contactless assessment of heart rate in neonates within a clinical environment using imaging photoplethysmography.

In neonatology, the accurate determination of vital parameters plays a pivotal role in monitoring critically ill newborns and premature infants, as well as aiding in disease diagnosis. In response to the limitations associated with contact-based measurement methods, substantial efforts have been directed toward developing contactless measurement techniques, particularly over the past decade. Building upon the insights gained from our pilot study, we realized a new investigation to assess the precision of our imaging photoplethysmography-based system within a clinical environment of the neonatal intermediate care unit. We conducted measurements in 20 preterm infants or newborns requiring therapeutic interventions. As a point of reference, we employed a conventional pulse oximeter. To analytically predict measurement artifacts, we analyzed the potential influence of confounding factors, such as motion artifacts, illumination fluctuations (under- and overexposure), and loss of region of interest prior to heart rate evaluation. This reduced the amount of data we evaluated for heart rate to 56.1% of its original volume. In artifact-free time segments, the mean difference between the pulse oximetry and the imaging photoplethysmography-based system for 1 s sampling intervals resulted in -0.2 bpm (95% CI -0.8 to 0.4, LOA ± 12.2). For the clinical standard of 8 s averaging time, the mean difference resulted in -0.09 bpm (95% CI -0.7 to 0.6, LOA ± 10.1). These results match the medical standards. While further research is needed to increase the range of measurable vital parameters and more diverse patient collectives need to be considered in the future, we could demonstrate very high accuracy for non-contact heart rate measurement in newborn infants in the clinical setting, provided artifacts are excluded. In particular, performing a priori signal assessment helps make clinical measurements safer by identifying unreliable readings.

Var-HR: Noncontact Heart Rate Measurement Using an RGB Camera Based on Adaptive Region Selection With Singular Value Decomposition

Var-HR: Noncontact Heart Rate Measurement Using an RGB Camera Based on Adaptive Region Selection With Singular Value Decomposition

Non-contact Heart Rate Measurement from Face Video Sequences using SIFT and FastICA.

Non-contact Heart Rate Measurement from Face Video Sequences using SIFT and FastICA.

Non-contact heart rate measurement from face video sequences using SIFT and FastICA

Non-contact heart rate measurement from face video sequences using SIFT and FastICA

Non-contact heart rate measurement from face video based on group sparse representation

Non-contact heart rate measurement based on face video is rapidly developed due to its comfort and wide application. However, it is difficult to extract the pulse signals for non-contact heart rate measurement due to the various interference factors, such as illumination variation, head motion and face expression. In this paper, we propose group sparse representation to reconstruct the pulse signals, then estimate the heart rate based on the fact that the real heart rate is consistent at the same time from different sub-regions. Specifically, we formulate the reconstruction of pulse signals as group sparse representation problem and require the raw signals of all the sub-regions to be similar sparse representation. Firstly, we use the face detection algorithm to obtain the region of interest (ROI) and divide it into sub-regions, followed by the distortion compensation of color signals from different sub-regions. Then we extract the sub-region chrominance signals and select the high-quality chrominance signals to construct the raw pulse signals matrix. After that, we construct a mixed pulse dictionary containing discrete cosine bases and wavelet bases considering the periodicity and pulsatility of the pulse signals. Finally, we conduct group sparse representation to reconstruct the pulse signals and estimate the heart rate via spectral analysis based on the reconstructed pulse signals. Experimental results on three public datasets show that this method outperforms most existing heart rate measurement methods.

Estimation of Heart Rate and Heart Rate Variability with Real-Time Images Based on Independent Component Analysis and Particle Swarm Optimization

With the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovascular disease year on year. Therefore, a non-contact measurement of heart rate and heart rate variability (HRV) is proposed to assist physicians in diagnosing symptoms related to mental illness and cardiovascular disease. In this paper, continuous images are obtained by general network cameras with non-contact, facial feature points and regions of interest (ROI) employed to track faces and regional images; HRV parameters were analyzed with the green wavelength of RGB color space. The artifact signal is eliminated by a hybrid algorithm of independent component analysis (ICA) and particle swarming optimization (PSO). Finally, the values of heart rate and HRV are obtained with signal processes of using band-pass filter, fast Fourier transform (FFT), and power spectrum analysis in the time and frequency domains, respectively. The non-contact measurement performance of the proposed method can effectively not only avoid infection doubts and obtain heart rate and HRV quickly, but also provide better physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE), than those of recent published papers.

TransPhys: Transformer-based unsupervised contrastive learning for remote heart rate measurement

Remote Photoplethysmography (rPPG) is a non-contact heart rate measurement method based on facial video. Because the rPPG signal is highly susceptible to interference from external conditions such as light changes and head movements, the rPPG signal extracted in natural scenes has a low signal to noise ratio and cannot accurately calculate the heart rate value. Aiming at these problems, this paper constructs a self-supervised machine learning network based on Transformer, TransPhys, to achieve robust measurement of heart rate. Using the self-supervised method of contrastive learning, first perform data augmentation, input the enhanced frame into Stem, and extract coarse local spatial features. Through Spatial-Temporal Transformer, calculate the pulse signal of the face area, and finally output the PPG signal. Compared with other supervised machine learning methods, this method can be trained and achieve ideal results without using any labels, which means that this method can be well generalized to practical applications. The experimental results show that this method is better than the current mainstream self-supervised methods.

A novel non-contact heart rate measurement method based on EEMD combined with FastICA

Objective. The imaging photoplethysmography (IPPG), which is applied to heart rate measurement, is susceptible to interference caused by illumination variations during photography. To improve the accuracy, robustness, and real-time performance of IPPG, a new method combined with modified ensemble empirical mode decomposition (EEMD) and fast independent component analysis (FastICA) was proposed. Approach. On the modified EEMD, to reduce the error caused by the residual white noise of the algorithm, it was characterized by recurrently updating zero-mean white noise for ensemble averaging based on the standard deviation of the input signal. On the modified FastICA, its constructed nonlinear function was superseded by Huber’s approximation function to improve the robustness and running speed. Main results. Comparison experiments were conducted between the MAHNOB-HCI database and own collected data. In the hybrid natural light and computer screen light scenario, the mean absolute error (MAE) of heart rate amounted to 0.93 beats per minute and the correlation coefficient reached 0.85. In the experiments of MAHNOB-HCI database, the MAE amounted to 6.03 beats per minute and the correlation coefficient reached 0.75. Furthermore, the modified method decreased the running time by approximately ten times compared to the original algorithms. Significance. Various experimental results demonstrated that the proposed method significantly improves the accuracy, timeliness, and interference resistance.

Implementation of 1D convolutional neural network for improvement remote photoplethysmography measurement

<span lang="EN-US">Remote photoplethysmography (rPPG) for non-contact heart rate measurement has been widely developed and shows good development. However, motion artifact due to changes in illumination and subject movement is still the main problem. Especially when measurements are taken in real conditions. In these conditions, it will be vulnerable to rPPG signal readings with poor signal quality. So, in this paper, it is proposed to classify the signal quality using one dimensional convolutional neural network (1D CNN). The classification is carried out based on the extraction of the temporal features of the rPPG signal that has been obtained from the plane orthogonal to skin algorithm and the magnitude of the subject's movement when measured. The classification results are entered into a compensated network if the signal obtained shows moderate quality. The compensated network will provide a more accurate estimate of hr value. The test was carried out using a dataset of 10 subjects, each measured with 3 different types of illumination. In the experiments conducted, the system's performance showed an improvement compared to the POS algorithm alone. The experiment found that the mean absolute error measurement was 2.78, and the mean error was relative at 3.67%.</span>

Study on Real-Time Heart Rate Detection Based on Multi-People

Heart rate is an important vital characteristic which indicates physical and mental health status. Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly. Therefore, the study of non-contact heart rate measurement methods is of great importance. Based on the principles of photoelectric volumetric tracing, we use a computer device and camera to capture facial images, accurately detect face regions, and to detect multiple facial images using a multi-target tracking algorithm. Then after the regional segmentation of the facial image, the signal acquisition of the region of interest is further resolved. Finally, frequency detection of the collected Photoplethysmography (PPG) and Electrocardiography (ECG) signals is completed with peak detection, Fourier analysis, and a Wavelet filter. The experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.

Non-Contact Heart Rate Measurement Based on Adaptive Notch Filter and Elimination of Respiration Harmonics

Continuous-wave Doppler radar is an established method for non-contact heart rate estimation. The output signal of the Doppler radar includes the fundamental wave of a heartbeat, heartbeat harmonics, the fundamental wave of respiration, respiration harmonics, and artifacts due to body movement. The respiration harmonics and artifacts degrade the accuracy of heart rate estimation. To this end, a novel heart rate estimation method is proposed that uses adaptive notch filters (ANFs). First, the proposed system estimates respiration frequency. Next, cascaded notch filters, the notch frequencies of which are controlled by the estimated respiration frequency, eliminate respiration harmonics; thus, the accuracy of heart rate estimation improves. In addition, the proposed method uses an ANF based on all-pass filter and an adaptive algorithm to estimate heart rate. The ANF is robust against disturbances, allowing the proposed method to improve the estimation accuracy in the presence of moving artifacts. The simulation results for human subjects show that MAPE, MAE, MSE, and RMSE of the proposed method are 5.24%, 4.00, 28.38, and 5.26, respectively; thus, they can be reduced up to fourth-order respiration harmonics. In an experiment on a dog not restrained in a cage, the proposed method, handling up to the fifth-order respiration harmonic, yields MAPE of 7.89%, MAE of 5.34, MSE of 96.11, and RMSE of 9.80. Thus, the proposed method shows better estimation accuracy than any other conventional method.

An Efficient Heart Rate Measurement System Using Medical Radar and LSTM Neural Network

This research proposes a noncontact heart rate measurement method using medical radar and artificial intelligence techniques. This technique has a significant role in the design and development of a wireless system that monitors the body’s vital signs. Firstly, based on a signal model describing chest surface movement, we propose a method to create a dataset for the training process using the long-short-term memory model. Secondly, a novel method to extract chest motion from the received radar signal is proposed. Finally, the heart rate will be estimated by using the obtained model and the received motion signal. The performance of the proposed method is evaluated through the root mean square error parameter as well as compared with other methods. Experimental results evaluated according to Bland–Altman achieved an accuracy of 96.67%.

비접촉식 심박 측정 시스템 개발

본 논문에서는 비접촉식 심박 측정을 위한 mmWave 레이더 기반의 시스템을 제안한다. 시스템은 심박 측정 레이더 센서 모듈과 신호 처리 과정으로 구성된다. 시스템을 구현하기 위해 심박수를 추정하는 과정을 설계하였으며, 심박수를 실시간으로 측정하기 위한 모니터링 프로그램을 작성하였다.BR 제안한 시스템의 성능 검증을 위해 피검자 3명에 대해 총 6회의 측정 실험을 하였으며, 레이더 기반 시스템과 접촉식 방식의 시스템으로 심박수를 측정하고 비교하였다. 실험 결과 제안한 시스템을 사용하여 안정적으로 심박수를 측정할 수 있었다.

Deep learning-based remote-photoplethysmography measurement from short-time facial video

Objective. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model. Approach. This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets. Main results. The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively. Significance. Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time.

A Non-contact Heart Rate Measurement Approach Based on Modified EEMD Algorithm with Updated White-noise Iterating

At present, Imaging Photoplethysmography (IPPG) has attracted extensive attention on heart rate measurement, because of its non-contact, non-invasive and safe characteristics. However, in reality, the IPPG signal is extremely feeble and susceptible to be submerged by ambient interference such as illumination. To this end, we propose a new IPPG signal processing approach to enhance the anti-interference capability of measuring heart rate through a modified EEMD algorithm with updated white-noise iterating. In the modified EEMD algorithm, based on the standard deviation of the input IPPG signal, zero-mean white noises are generated randomly for iterative ensemble averaging. Through this new method, the fault caused by the residual white noise of traditional EEMD is solved. Lastly, through the comparative experiments of our own acquisition statistics, the approach proposed by this paper has significantly improved the performance in terms of accuracy, timeliness and anti-interference under various scenarios. The mean absolute error reaches 1.4 beats per minute and the correlation coefficient r reaches 0.8089. In addition, the modified EEMD algorithm reduces the running time by more than 10 times compared to the original method, approximately 0.16s per time.

Contactless Heart Rate Measurement System Based RGB Camera, Cascade Object Detector and Independent Component Analysis

This paper focuses on the development of non-contact heart rate measurement based on video analysis. This project implements an RGB camera (OV2640 with ESP32 Cam) and an FTDI Programmer which enables the sensor to record videos from a subject at a distance of 20 cm. The Region of Interest (ROI) of the facial skin tissue was detected by using Cascade Object Detector (COD) which can detect object categories whose aspect ratio does not vary significantly. Then, the Independent Component Analysis (ICA) was applied by extracting distinct source signals (red, blue and green channels) from the observed mixed colour signals. Next, the average pixel of ROI was calculated based on red, green and blue facial pixel at given time. Finally, the heart rate was extracted from the frequency spectrum of the average pixel. The performance of the developed contactless system was compared with the fingertip pulse oximeter (Oxitech) by means of percentage of error (PE). The lowest range of PE is between 0 to 5.62 % whereas the highest PE is 22.54 %. The highest error was due to incorrectly captured ROI of facial skin area as well as due to the movement made during the recording session. Meanwhile, the average time for the developed system to process the heart rate of the subject is 4.51 minutes. In future, a robust ROI algorithm can be adopted to reduce error contributed by incorrectly ROI selection. Other than that, this project has a potential to contribute to the area of contactless measurement of oxygen saturation.

Remote investigation and assessment of vital signs (RIA-VS)—proof of concept for contactless estimation of blood pressure, pulse, respiratory rate, and oxygen saturation in patients with suspicion of COVID-19

Background Vital signs are critical in assessing the severity and prognosis of infections, for example, COVID-19, influenza, sepsis, and pneumonia. This study aimed to evaluate a new method for rapid camera-based non-contact measurement of heart rate, blood oxygen saturation, respiratory rate, and blood pressure. Methods Consecutive adult patients attending a hospital emergency department for suspected COVID-19 infection were invited to participate. Vital signs measured with a new camera-based method were compared to the corresponding standard reference methods. The camera device observed the patient's face for 30 s from ∼1 m. Results Between 1 April and 1 October 2020, 214 subjects were included in the trial, 131 female (61%) and 83 male (39%). The mean age was 44 years (range 18–81 years). The new camera-based device’s vital signs measurements were, on average, very close to the gold standard but the random variation was larger than the reference methods. Conclusions The principle of contactless measurement of blood pressure, pulse, respiratory rate, and oxygen saturation works, which is very promising. However, technical improvements to the equipment used in this study to reduce its random variability is required before clinical implementation. This will likely be a game changer once this is sorted out. Clinical trial registration Universal Trial Number (UTN) U1111-1251-4114 and the ClinicalTrials.gov Identifier NCT04383457.

Heart Rate Measurement Based on 3D Central Difference Convolution with Attention Mechanism.

Remote photoplethysmography (rPPG) is a video-based non-contact heart rate measurement technology. It is a fact that most existing rPPG methods fail to deal with the spatiotemporal features of the video, which is significant for the extraction of the rPPG signal. In this paper, we propose a 3D central difference convolutional network (CDCA-rPPGNet) to measure heart rate, with an attention mechanism to combine spatial and temporal features. First, we crop and stitch the region of interest together through facial landmarks. Next, the high-quality regions of interest are fed to CDCA-rPPGNet based on a central difference convolution, which can enhance the spatiotemporal representation and capture rich relevant time contexts by collecting time difference information. In addition, we integrate the attention module into the neural network, aiming to strengthen the ability of the neural network to extract video channels and spatial features, so as to obtain more accurate rPPG signals. In summary, the three main contributions of this paper are as follows: (1) the proposed network base on central difference convolution could better capture the subtle color changes to recover the rPPG signals; (2) the proposed ROI extraction method provides high-quality input to the network; (3) the attention module is used to strengthen the ability of the network to extract features. Extensive experiments are conducted on two public datasets—the PURE dataset and the UBFC-rPPG dataset. In terms of the experiment results, our proposed method achieves 0.46 MAE (bpm), 0.90 RMSE (bpm) and 0.99 R value of Pearson’s correlation coefficient on the PURE dataset, and 0.60 MAE (bpm), 1.38 RMSE (bpm) and 0.99 R value of Pearson’s correlation coefficient on the UBFC dataset, which proves the effectiveness of our proposed approach.