Non-contact Imaging Photoplethysmography Research Articles

Non-contact imaging of peripheral hemodynamics during cognitive and psychological stressors

Peripheral hemodynamics, measured via the blood volume pulse and vasomotion, provide a valuable way of monitoring physiological state. Camera imaging-based systems can be used to measure these peripheral signals without contact with the body, at distances of multiple meters. While researchers have paid attention to non-contact imaging photoplethysmography, the study of peripheral hemodynamics and the effect of autonomic nervous system activity on these signals has received less attention. Using a method, based on a tissue-like model of the skin, we extract melanin text {C}_{m} and hemoglobin text {C}_{HbO} concentrations from videos of the hand and face and show that significant decreases in peripheral pulse signal power (by 36% ± 29%) and vasomotion signal power (by 50% ± 26%) occur during periods of cognitive and psychological stress. Via three experiments we show that similar results are achieved across different stimuli and regions of skin (face and hand). While changes in peripheral pulse and vasomotion power were significant the changes in pulse rate variability were less consistent across subjects and tasks.

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Video-based noncontact detection of heart rate has a wide range of applications in the field of medicine and health. However, this method is susceptible to noise interference, making it difficult to effectively extract blood volume pulse (BVP) signals. To overcome this problem, a new method of noncontact heart rate estimation that can suppress noise interference is proposed in this paper. First, the established data acquisition system conducts video collection, and the captured videos are divided into multiple small regions. Subsequently, the initial signals of BVP are extracted in accordance with the chrominance features extracted through multi-channel data fusion. The BVP signals are separated using the FastICA algorithm. The kurtosis value and signal-to-noise ratios of the power spectrum of the separated signals are analyzed to determine the effective separation component. Results show that this method can extract and process pulse signals, effectively suppressing non-periodic interference. The experiment also proves that the method has good consistency with the measurement of pulse oximeter and has good stability and accuracy in the detection of heart rate of the human body.

Using Non-Contact Imaging Photoplethysmography to Recover Diurnal Patterns in Heart Rate.

Daily patterns in cardiovascular signals can reveal important information about physiological processes, health and well-being. Traditionally, contact sensors have been used to collect longitudinal data of this kind. However, recent advances in non-contact imaging techniques have led to algorithms that can be used to measure vital signs unobtrusively. Imaging methods are highly scalable due to the availability of webcams and computing devices making them attractive for longitudinal, in-situ measurement. Using a software tool we captured over 1,000 hours of non-contact heart rate measurements, via imaging photoplethysmography. Using these data we were able to recover diurnal patterns in heart rate during the working day. Non-contact sensing techniques hold much promise but also raise ethical issues that need to be addressed seriously within the biomedical engineering community.

A New Approach for Noncontact Imaging Photoplethysmography Using Chrominance Features and Low-Rank in the IoT Operating Room

Advancements in image-processing and medical imaging technologies and other research areas have resulted in the growing importance of surgical navigation systems in the field of minimally invasive surgery. However, sophisticated auxiliary equipment is a prerequisite for full heart-rate monitoring in operative systems. A system puts forward to address this issue with a wireless sensor network architecture comprising acquisition modules, router gateways, remote servers, and a medical monitoring center. Factors such as variations in ambient light and face shaking can also easily affect heart rate detection based on face videos, thus resulting in inaccurate estimations of heart rate from the blood volume pulse (BVP) signals. This study proposes to address this concern by employing a novel method for non-contact heart rate estimation to overcome noise interference. First, chrominance features are selected to extract BVP signals, and the low-rank and sparse matrix decomposition methods are applied to overcome the detrimental effects of noise and interference while ensuring that valuable data are preserved.Next, the data are relayed to the server via the gateway. Finally, users can log on to the health-related cloud platform and gain information regarding their health status in real time. Experimental results reveal the advantages of the proposed technique over conventional face-based heart-rate estimation methods, including the capability to decrease dependence on sophisticated auxiliary equipment, avoid direct skin contact that can cause discomfort to patients undergoing surgery, and improve the comfort of surgical operations. Moreover, the proposed heart-rate measurement technique can contribute to improve the construction of smart cities.

Spectral Estimation Methods for Evaluating iPPG Pulse Rate Variability.

Non-contact measurement of physiological parameters, like pulse rate variability (PRV), has numerous applications in medicine and affective computing. PRV is an informative measure of autonomic nervous system activity. Spectral estimation from unevenly sampled, non-stationary data is integral to pulse rate variability frequency-domain analysis. We present the first comparison of results of PRV computation using the Lomb-Scargle method and Bayesian Spectral Estimation. The Lomb-Scargle method performs well, even in the presence of missing beats. However, the Bayesian Spectral Estimation method has advantages when tracking changes in amplitude and frequency. We illustrate these characteristics with results from synthetic data and real non-contact imaging photoplethysmography measurements.

Accurate measurement of the pulse wave delay with imaging photoplethysmography.

Assessment of the cardiovascular parameters using noncontact video-based or imaging photoplethysmography (IPPG) is usually considered as inaccurate because of strong influence of motion artefacts. To optimize this technique we performed a simultaneous recording of electrocardiogram and video frames of the face for 36 healthy volunteers. We found that signal disturbances originate mainly from the stochastically enhanced dichroic notch caused by endogenous cardiovascular mechanisms, with smaller contribution of the motion artefacts. Our properly designed algorithm allowed us to increase accuracy of the pulse-transit-time measurement and visualize propagation of the pulse wave in the facial region. Thus, the accurate measurement of the pulse wave parameters with this technique suggests a sensitive approach to assess local regulation of microcirculation in various physiological and pathological states.

Measuring pulse rate variability using long-range, non-contact imaging photoplethysmography.

Camera-based measurement of the blood volume pulse via non-contact, imaging photoplethysmography is a very popular approach for measuring pulse rate using a remote imaging sensor. Comparatively less attention has been paid to the usefulness of the method for measuring features of pulse rate variability, and even less focus has been put on the accuracy of any cardiac activity feature that can be achieved at long imager-to-subject distances. In this study, video was recorded from 19 participants, while at rest, at a distance of 25 meters from the imaging sensor. A digital camera was used to record video while cardiovascular measures of both electrical and optical physiological ground truth were recorded. Pulse rate data obtained from the imager using a common blind source separation and periodogram approach were compared to physiological ground truth signals. The quality of the recovered blood volume pulse morphology was sufficient to calculate time-domain measures of pulse rate using inter-pulse interval (IPI) time series. Following this, several features of pulse rate variability were calculated from the IPI time series and compared to those calculated from the corresponding physiological ground truth signals. Use of the time-domain data as compared to the periodogram approach to measure pulse rate reduced the error in the estimate from 1.6 to 0.2 beats per minute. Correlation analysis (r2) between the camera-based measures of pulse rate variability and ECG-derived heart rate variability ranged from 0.779 to 0.973; these results are of comparable outcome to those obtained at imager-to-subject distances of no more than 3 meters. This study demonstrates that pulse rates of less than one beat-per-minute error can be obtained when the recovered blood volume pulse morphology is of adequate quality to resolve systolic onsets for individual cardiac cycles. Further, this approach can yield data of very promising quality for estimating measures of pulse rate variability.

Noncontact imaging photoplethysmography to effectively access pulse rate variability

Noncontact imaging photoplethysmography (PPG) can provide physiological assessment at various anatomical locations with no discomfort to the patient. However, most previous imaging PPG (iPPG) systems have been limited by a low sample frequency, which restricts their use clinically, for instance, in the assessment of pulse rate variability (PRV). In the present study, plethysmographic signals are remotely captured via an iPPG system at a rate of 200 fps. The physiological parameters (i.e., heart and respiration rate and PRV) derived from the iPPG datasets yield statistically comparable results to those acquired using a contact PPG sensor, the gold standard. More importantly, we present evidence that the negative influence of initial low sample frequency could be compensated via interpolation to improve the time domain resolution. We thereby provide further strong support for the low-cost webcam-based iPPG technique and, importantly, open up a new avenue for effective noncontact assessment of multiple physiological parameters, with potential applications in the evaluation of cardiac autonomic activity and remote sensing of vital physiological signs.

Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise

With the advance of computer and photonics technology, imaging photoplethysmography [(PPG), iPPG] can provide comfortable and comprehensive assessment over a wide range of anatomical locations. However, motion artifact is a major drawback in current iPPG systems, particularly in the context of clinical assessment. To overcome this issue, a new artifact-reduction method consisting of planar motion compensation and blind source separation is introduced in this study. The performance of the iPPG system was evaluated through the measurement of cardiac pulse in the hand from 12 subjects before and after 5 min of cycling exercise. Also, a 12-min continuous recording protocol consisting of repeated exercises was taken from a single volunteer. The physiological parameters (i.e., heart rate, respiration rate), derived from the images captured by the iPPG system, exhibit functional characteristics comparable to conventional contact PPG sensors. Continuous recordings from the iPPG system reveal that heart and respiration rates can be successfully tracked with the artifact reduction method even in high-intensity physical exercise situations. The outcome from this study thereby leads to a new avenue for noncontact sensing of vital signs and remote physiological assessment, with clear applications in triage and sports training.