Non-contact Physiological Monitoring Research Articles

Prospective clinical validation of a noncontact vital signs measurement smartphone application in emergency department

Abstract Background Remote photoplethysmography (rPPG) is a noncontact optical measurement technique that can be used to detect blood volume changes in the microvascular bed of tissue, using a RGB camera. We developed a smartphone application that leverages rPPG in order to monitor the heart rate and respiratory rate of patients using a 30s video selfie. Due to the widespread availability of cameras in smartphones, tablets and laptops, such an application allows to automate noncontact physiological monitoring and recording, improving the quality and accessibility of this essential clinical information. Purpose This study aimed to evaluate the accuracy of such remote measurements compared with regular contact based measurement methods (pulse oximeter) in real-life clinical settings. Methods We recruited 443 patients who consulted the emergency department in 4 French hospitals. After obtaining written informed consent, the patients filled in a questionnaire collecting demographic information including age, gender and the Fitzpatrick skin phototype. Each patient was asked to sit in front of the tablet, with the reference pulse oximeter installed on their finger. All measurements were 30s long. When starting the measurement, the medical staff started to count the patient's respiratory cycles. After 15s, the first COBOX result was available, and then updated every second. At the end of the 30s, the medical staff reported the number of respiratory cycles which were doubled to obtain a respiratory rate in cycles per minute (cpm). Results The mean age was 44 ± 18 years old, 45% was male. A total of 6311 pairs of values of heart rate taken by COBOX (HRCOBOX) and by the reference pulse oximeter (HRreference) and 453 pairs of values of respiratory rate taken by COBOX (RRCOBOX) and by the reference manual count (RRreference) were collected. The proportion of heart rates > 100bpm (beats per minute) was 4.4% and of respiratory rates > 20cpm was 22.9%. The number of insufficient quality measurements was 838 for heart rate (13%). The medical staff could not count reliably the respiratory rate in 20 sessions (4.4%). 30 sessions (6.9%) had a measurement of insufficient quality. For the heart rate, the Pearson correlation between COBOX and the reference was 97.2%. The root mean square error (RMSE) was 3.1bpm. For the respiratory rate, the Pearson correlation between COBOX and the reference was 78.3%. The RMSE was 2.9cpm. Conclusion Our work aimed to evaluate the accuracy of such remote measurements compared with existing contact point measurement methods in real-life clinical settings. Our results described a very good accuracy and correlation between the rPPG system and the gold standard, thus paving the way for more precise and more accessible remote patient monitoring. Such a technology has the potential to be transformative for telemedicine, especially in medical deserts where the access to both physicians and medical devices is lacking.Heart rate resultsRespiratory rate results

Bioinspired Stretchable MXene Deformation-Insensitive Hydrogel Temperature Sensors for Plant and Skin Electronics.

Temperature sensing is of high value in the wearable healthcare, robotics/prosthesis, and noncontact physiological monitoring. However, the common mechanic deformation, including pressing, bending, and stretching, usually causes undesirable feature size changes to the inner conductive network distribution of temperature sensors, which seriously influences the accuracy. Here, inspired by the transient receptor potential mechanism of biological thermoreceptors that could work precisely under various skin contortions, we propose an MXene/Clay/poly(N-isopropylacrylamide) (PNIPAM) (MCP) hydrogel with high stretchability, spike response, and deformation insensitivity. The dynamic spike response is triggered by the inner conductive network transformation from the 3-dimensional structure to the 2-dimensional surface after water being discharged at the threshold temperature. The water discharge is solely determined by the thermosensitivity of PNIPAM, which is free from mechanical deformation, so the MCP hydrogels can perform precise threshold temperature (32 °C) sensing under various deformation conditions, i.e., pressing and 15% stretching. As a proof of concept, we demonstrated the applications in plant electronics for the real-time surface temperature monitoring and skin electronics for communicating between human and machines. Our research opens venues for the accurate temperature-threshold sensation on the complicated surface and mechanical conditions.

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit.

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning-based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.

Non-contact physiological monitoring of post-operative patients in the intensive care unit

Prolonged non-contact camera-based monitoring in critically ill patients presents unique challenges, but may facilitate safe recovery. A study was designed to evaluate the feasibility of introducing a non-contact video camera monitoring system into an acute clinical setting. We assessed the accuracy and robustness of the video camera-derived estimates of the vital signs against the electronically-recorded reference values in both day and night environments. We demonstrated non-contact monitoring of heart rate and respiratory rate for extended periods of time in 15 post-operative patients. Across day and night, heart rate was estimated for up to 53.2% (103.0 h) of the total valid camera data with a mean absolute error (MAE) of 2.5 beats/min in comparison to two reference sensors. We obtained respiratory rate estimates for 63.1% (119.8 h) of the total valid camera data with a MAE of 2.4 breaths/min against the reference value computed from the chest impedance pneumogram. Non-contact estimates detected relevant changes in the vital-sign values between routine clinical observations. Pivotal respiratory events in a post-operative patient could be identified from the analysis of video-derived respiratory information. Continuous vital-sign monitoring supported by non-contact video camera estimates could be used to track early signs of physiological deterioration during post-operative care.

Developing the Accuracy of Vital Sign Measurements Using the Lifelight Software Application in Comparison to Standard of Care Methods: Observational Study Protocol.

BackgroundVital sign measurements are an integral component of clinical care, but current challenges with the accuracy and timeliness of patient observations can impact appropriate clinical decision making. Advanced technologies using techniques such as photoplethysmography have the potential to automate noncontact physiological monitoring and recording, improving the quality and accessibility of this essential clinical information.ObjectiveIn this study, we aim to develop the algorithm used in the Lifelight software application and improve the accuracy of its estimated heart rate, respiratory rate, oxygen saturation, and blood pressure measurements.MethodsThis preliminary study will compare measurements predicted by the Lifelight software with standard of care measurements for an estimated population sample of 2000 inpatients, outpatients, and healthy people attending a large acute hospital. Both training datasets and validation datasets will be analyzed to assess the degree of correspondence between the vital sign measurements predicted by the Lifelight software and the direct physiological measurements taken using standard of care methods. Subgroup analyses will explore how the performance of the algorithm varies with particular patient characteristics, including age, sex, health condition, and medication.ResultsRecruitment of participants to this study began in July 2018, and data collection will continue for a planned study period of 12 months.ConclusionsDigital health technology is a rapidly evolving area for health and social care. Following this initial exploratory study to develop and refine the Lifelight software application, subsequent work will evaluate its performance across a range of health characteristics, and extended validation trials will support its pathway to registration as a medical device. Innovations in health technology such as this may provide valuable opportunities for increasing the efficiency and accessibility of vital sign measurements and improve health care services on a large scale across multiple health and care settings.International Registered Report Identifier (IRRID)DERR1-10.2196/14326

Study protocol for an exploratory interventional study investigating the feasibility of video-based non-contact physiological monitoring in healthy volunteers by Mapping Of Lower Limb skIn pErfusion (MOLLIE)

IntroductionSkin perfusion varies in response to changes in the circulatory status. Blood flow to skin is reduced during haemodynamic collapse secondary to peripheral vasoconstriction, whereas increased skin perfusion is frequently...

A Pilot Study for Estimating the Cardiopulmonary Signals of Diverse Exotic Animals Using a Digital Camera.

Monitoring the cardiopulmonary signal of animals is a challenge for veterinarians in conditions when contact with a conscious animal is inconvenient, difficult, damaging, distressing or dangerous to personnel or the animal subject. In this pilot study, we demonstrate a computer vision-based system and use examples of exotic, untamed species to demonstrate this means to extract the cardiopulmonary signal. Subject animals included the following species: Giant panda (Ailuropoda melanoleuca), African lions (Panthera leo), Sumatran tiger (Panthera tigris sumatrae), koala (Phascolarctos cinereus), red kangaroo (Macropus rufus), alpaca (Vicugna pacos), little blue penguin (Eudyptula minor), Sumatran orangutan (Pongo abelii) and Hamadryas baboon (Papio hamadryas). The study was done without need for restriction, fixation, contact or disruption of the daily routine of the subjects. The pilot system extracts the signal from the abdominal-thoracic region, where cardiopulmonary activity is most likely to be visible using image sequences captured by a digital camera. The results show motion on the body surface of the subjects that is characteristic of cardiopulmonary activity and is likely to be useful to estimate physiological parameters (pulse rate and breathing rate) of animals without any physical contact. The results of the study suggest that a fully controlled study against conventional physiological monitoring equipment is ethically warranted, which may lead to a novel approach to non-contact physiological monitoring and remotely sensed health assessment of animals. The method shows promise for applications in veterinary practice, conservation and game management, animal welfare and zoological and behavioral studies.

Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

Radius Correction Technique for Doppler Radar Noncontact Periodic Displacement Measurement

Noncontact physiological monitoring using Doppler radar has been studied extensively. Most commonly, continuous-wave (CW) quadrature Doppler radar is used to measure cardiopulmonary rates. Accurate displacement measurement can provide physiological waveform recovery, which may enable tidal volume and pulse pressure estimation. In this paper, we propose a calibration technique that enables high-accuracy millimeter-order periodic displacement measurements using CW quadrature Doppler radar. Theoretical analysis of center estimation error and its propagation effect are presented. Simulations are performed to show how noise and limited arc length affect error and affect the accuracy of center estimation, as well as improvements after calibration. A high-precision linear stage was employed to create periodic motion for evaluating the performance of calibration technique. Experimental results demonstrate that the proposed calibration technique enables displacement measurement with accuracy within tens of micrometers.

A Portable Plug-and-Play Imaging System for Physiological Monitoring

<p>Non-contact physiological monitoring has garnered interest in the<br />research community. However, studies employ different imaging<br />system configuration, including illuminant profile and wavelength,<br />camera type and frame rate, and distance to the skin. In this paper,<br />we propose an easily customizable imaging system for evaluating<br />physiological monitoring parameters. The system’s design allows<br />plug-and-play compatibility with various illumination sources,<br />camera types, lenses, and optical components. Results using one<br />configuration shows the feasibility of spatial blood perfusion analysis<br />using this imaging system, where areas exhibiting clean blood<br />pulsatility can be identified at the surface level.</p>