Improving heart rate monitoring in the obese with time-of-flight photoplethysmography (TOF-PPG): a quantitative analysis of source-detector-distance effect.

Commercial photoplethysmography (PPG) sensors rely on the measurement of continuous-wave diffuse reflection signals (CW-DRS) to monitor heart rate. Using Monte Carlo modeling of light propagation in skin, we quantitatively evaluate the dependence of continuous-wave photoplethysmography (CW-PPG) in commercial wearables on source-detector distance (SDD). Specifically, when SDD increases from 0.5 mm to 3.3 mm, CW-PPG signal increases by roughly 846% for non-obese (NOB) skin and roughly 683% for morbidly obese (MOB) skin. Ultimately, we introduce the concept of time-of-flight PPG (TOF-PPG) which can significantly improve heart rate signals. Our model shows that the optimized TOF-PPG improves heart rate monitoring experiences by roughly 47.9% in NOB and 93.2% in MOB when SDD = 3.3 mm is at green light. Moving forward, these results will provide a valuable source for hypothesis generation in the scientific community to improve heart rate monitoring.

Evaluation and patient experience of wireless noninvasive fetal heart rate monitoring devices.

INTRODUCTION: In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring. MATERIAL AND METHODS: Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire. RESULTS: A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices. CONCLUSIONS: Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.

Intrapartum fetal heart rate monitoring: Rationalise, refine or replace?

Monitoring the fetal heartbeat underpins assessment of fetal wellbeing in labour. Although commonly employed in clinical practice, shortcomings remain. A recent review of clinical practice guidelines highlights the variation in definitions of the fetal heart rate that will lead to differences in interpretation. Will intrapartum care be improved by greater consensus around clinical practice guidelines through rationalisation or refinement of guidelines, or will the future see this technique replaced by more accurate forms of fetal monitoring?

Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.

The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.

Remote Multi-Person Heart Rate Monitoring with Smart Speakers: Overcoming Separation Constraint.

Heart rate is a key vital sign that can be used to understand an individual's health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system's accuracy in sensing two users' heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity.

Standardized motion detection and real time heart rate monitoring of aerobics training based on convolution neural network.

In order to make the teaching and training of aerobics more standardized, it is necessary to use scientific means to detect and monitor the movement standardization in teaching and training and the change of human heart rate in the training process, but at present, there are some difficulties in both detection and monitoring, Therefore, this paper proposes to use the advantages of convolutional neural network to solve the current aerobics teaching problems of motion detection and heart rate monitoring. In the process of operation, the complete aerobics video needs to be divided into several different images, the standardized action image background needs to be eliminated, and then the visual error caused by the difficult action image needs to be corrected. On the premise of image processing, the convolutional neural network is used to pre train the image, and the skeleton map of the human body is constructed in the computer. In the process of practical operation, the use of convolutional neural network for heart rate monitoring has many advantages. It can not only save the time of contact with the human body, but also integrate various information of the time dimension, reducing a lot of computing steps, saving a lot of computing resources for practical work, and promoting the improvement of system output signal quality to a certain extent. The result of the experiment also proves that the convolutional neural network can improve the accuracy of students' movement detection and heart rate change monitoring in aerobics teaching and training.

Heart rate monitoring to detect acute pain in non-verbal patients: a study protocol for a randomized controlled clinical trial.

BACKGROUND: Autism entails reduced communicative abilities. Approximately 30% of individuals with autism have intellectual disability (ID). Some people with autism and ID are virtually non-communicative and unable to notify their caregivers when they are in pain. In a pilot study, we showed that heart rate (HR) monitoring may identify painful situations in this patient group, as HR increases in acutely painful situations. OBJECTIVES: This study aims to generate knowledge to reduce the number of painful episodes in non-communicative patients' everyday lives. We will 1) assess the effectiveness of HR as a tool for identifying potentially painful care procedures, 2) test the effect of HR-informed changes in potentially painful care procedures on biomarkers of pain, and 3) assess how six weeks of communication through HR affects the quality of communication between patient and caregiver. METHODS: We will recruit 38 non-communicative patients with autism and ID residing in care homes. ASSESSMENTS: HR is measured continuously to identify acutely painful situations. HR variability and pain-related cytokines (MCP-1, IL-1RA, IL-8, TGFß1, and IL-17) are collected as measures of long-term pain. Caregivers will be asked to what degree they observe pain in their patients and how well they believe they understand their patient's expressions of emotion and pain. Pre-intervention: HR is measured 8 h/day over 2 weeks to identify potentially painful situations across four settings: physiotherapy, cast use, lifting, and personal hygiene. INTERVENTION: Changes in procedures for identified painful situations are in the form of changes in 1) physiotherapy techniques, 2) preparations for putting on casts, 3) lifting techniques or 4) personal hygiene procedures. DESIGN: Nineteen patients will start intervention in week 3 while 19 patients will continue data collection for another 2 weeks before procedure changes are introduced. This is done to distinguish between specific effects of changes in procedures and non-specific effects, such as caregivers increased attention. DISCUSSION: This study will advance the field of wearable physiological sensor use in patient care. TRIAL REGISTRATION: Registered prospectively at ClinicalTrials.gov (NCT05738278).

Reliability and agreement in intrapartum fetal heart rate monitoring interpretation: A systematic review.

INTRODUCTION: Fetal heart rate (FHR) monitoring is routine in intrapartum care worldwide and one of the most common obstetrical procedures. Intrapartum FHR monitoring helps assess fetal wellbeing and interpretation of the FHR help form decisions for clinical management and intervention. It relies on the observers' subjective assessments, with variation in interpretations leading to variations in intrapartum care. The purpose of this systematic review was to summarize and evaluate extant inter- and intrarater reliability research on the human interpretation of intrapartum FHR monitoring. MATERIAL AND METHODS: We searched for the terms "fetal heart rate monitoring," "interpretation agreement" and related concepts on Embase, Medline, Maternity and Infant Care Database and CINAHL. The last search was made on January 31, 2022. The protocol for the study was prospectively registered in PROSPERO (CRD42021260937). Studies that assess inter- and intrarater reliability and agreement of health professionals' intrapartum FHR monitoring were included and studies including other assessment of fetal wellbeing excluded. We extracted data in reviewer pairs using quality appraisal tool for studies of diagnostic reliability (QAREL) forms. The data retrieved from the studies are presented as narrative synthesis and in additional tables. RESULTS: Forty-nine articles concerning continuous FHR monitoring were included in the study. For interrater reliability and agreement, in total 577 raters assessed 6315 CTG tracings. There was considerable heterogeneity in quality and measures across the included articles. We found higher reliability and agreement for the basic FHR features than for overall classification and higher agreement for intrarater reliability and agreement than for their interrater counterparts. CONCLUSIONS: There is great variation in reliability and agreement measures for continuous intrapartum FHR monitoring, implying that intrapartum CTG should be used with caution for clinical decision making given its questionable reliability. We found few high-quality studies and noted methodological concerns in the studies. We recommend a more standardized approach to future reliability studies on FHR monitoring.

Measurements of pulse rate using facial video cameras from smart devices in patients diagnosed with atrial fibrillation.

Clinical applications of passive long-term heart rate (HR) monitoring in patients with cardiac arrhythmias include adequate drug titration of atrioventricular (AV) nodal drugs and assessment of medical compliance with treatment. A majority of patients treated with beta-blockers, especially patients with atrial fibrillation (AF), require some degree of drug titration during the first 6 months of treatment to ensure that adequate HR control and medicine compliance has been achieved. Failing to achieve adequate rate control in patients with AF can lead to worsening symptoms, heart failure exacerbations, and potentially tachycardia-induced cardiomyopathy. Enabling video-based monitoring during telehealth patient visits could facilitate providers to measure heart rate (HR) without the need for a dedicated home device (smartwatch, SPO2 device, or others). Videoplethysmography (VPG) is a monitoring technology that measures pulse rate by utilizing front-facing cameras embedded in smart devices. VPG provides a remote and contactless cardiac monitoring solution. We conducted a clinical experiment to evaluate the accuracy of VPG in measuring HR while running on two portable devices: Samsung S10 smartphones and S3 tablets. We used a single­lead ECG to measure the heart rate at the time of the VPG recordings in AF patients. We employed the Bland-Altman method to measure the level of agreement between videoplethysmography and ECG-based measurements of HR. The findings reveal that the mean difference in videoplethysmography and ECG-based heart rate was inferior to 1 bpm across the 2 devices with confidence intervals ranging from 3 to 12 BPM. Our facial video-based HR monitoring solution could assist providers in measuring heart rates in their patients with AF during remote telehealth visits.

Touchless Heart Rate Monitoring from an Unmanned Aerial Vehicle Using Videoplethysmography.

Motivation: The advancement of preventive medicine and, subsequently, telemedicine drives the need for noninvasive and remote measurements in patients' natural environments. Heart rate (HR) measurements are particularly promising and extensively researched due to their quick assessment and comprehensive representation of patients' conditions. However, in scenarios such as endurance training or emergencies, where HR measurement was not anticipated and direct access to victims is limited, no method enables obtaining HR results that are suitable even for triage. Methods: This paper presents the possibility of remotely measuring of human HR from a series of in-flight videos using videoplethysmography (VPG) along with skin detection, human pose estimation and image stabilization methods. An unmanned aerial vehicle (UAV) equipped with a camera captured ten segments of video footage featuring volunteers engaged in free walking and running activities in natural sunlight. The human pose was determined using the OpenPose algorithm, and subsequently, skin areas on the face and forearms were identified and tracked in consecutive frames. Ultimately, HR was estimated using several VPG methods: the green channel (G), green-red difference (GR), excess green (ExG), independent component analysis (ICA), and a plane orthogonal to the skin (POS). Results: When compared to simultaneous readings from a reference ECG-based wearable recorder, the root-mean-squared error ranged from 17.7 (G) to 27.7 (POS), with errors of less than 3.5 bpm achieved for the G and GR methods. Conclusions: These results demonstrate the acceptable accuracy of touchless human pulse measurement with the accompanying UAV-mounted camera. The method bridges the gap between HR-transmitting wearables and emergency HR recorders, and it has the potential to be advantageous in training or rescue scenarios in mountain, water, disaster, or battlefield settings.

An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring.

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions.

A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring.

Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.

ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects.

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

Speckle Vibrometry for Instantaneous Heart Rate Monitoring.

Instantaneous heart rate (IHR) has been investigated for sleep applications, such as sleep apnea detection and sleep staging. To ensure the comfort of the patient during sleep, it is desirable for IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) to perform on-skin and on-textile IHR monitoring in a sleep setting. Minute motions on the laser-illuminated surface can be captured by a defocused camera, enabling the detection of cardiac motions even on textiles. We investigate supine, lateral, and prone sleeping positions. Based on Bland-Altman analysis between SV cardiac measurements and electrocardiogram (ECG), with respect to each position, we achieve the best limits of agreement with ECG values of [-8.65, 7.79] bpm, [-9.79, 9.25] bpm, and [-10.81, 10.23] bpm, respectively. The results indicate the potential of using speckle vibrometry as a contact-free monitoring method for instantaneous heart rate in a setting where the participant is allowed to rest in a spontaneous position while covered by textile layers.

Benchmarking of Contactless Heart Rate Measurement Systems in ARM-Based Embedded Platforms.

Heart rate monitoring is especially important for aging individuals because it is associated with longevity and cardiovascular risk. Typically, this vital parameter can be measured using wearable sensors, which are widely available commercially. However, wearable sensors have some disadvantages in terms of acceptability, especially when used by elderly people. Thus, contactless solutions have increasingly attracted the scientific community in recent years. Camera-based photoplethysmography (also known as remote photoplethysmography) is an emerging method of contactless heart rate monitoring that uses a camera and a processing unit on the hardware side, and appropriate image processing methodologies on the software side. This paper describes the design and implementation of a novel pipeline for heart rate estimation using a commercial and low-cost camera as the input device. The pipeline's performance was tested and compared on a desktop PC, a laptop, and three different ARM-based embedded platforms (Raspberry Pi 4, Odroid N2+, and Jetson Nano). The results showed that the designed and implemented pipeline achieved an average accuracy of about 96.7% for heart rate estimation, with very low variance (between 1.5% and 2.5%) across processing platforms, user distances from the camera, and frame resolutions. Furthermore, benchmark analysis showed that the Odroid N2+ platform was the most convenient in terms of CPU load, RAM usage, and average execution time of the algorithmic pipeline.

Accuracy of a Smartwatch to Assess Heart Rate Monitoring and Atrial Fibrillation in Stroke Patients.

(1) Background: Consumer smartwatches may be a helpful tool to screen for atrial fibrillation (AF). However, validation studies on older stroke patients remain scarce. The aim of this pilot study from RCT NCT05565781 was to validate the resting heart rate (HR) measurement and the irregular rhythm notification (IRN) feature in stroke patients in sinus rhythm (SR) and AF. (2) Methods: Resting clinical HR measurements (every 5 min) were assessed using continuous bedside ECG monitoring (CEM) and the Fitbit Charge 5 (FC5). IRNs were gathered after at least 4 h of CEM. Lin's concordance correlation coefficient (CCC), Bland-Altman analysis, and mean absolute percentage error (MAPE) were used for agreement and accuracy assessment. (3) Results: In all, 526 individual pairs of measurements were obtained from 70 stroke patients-age 79.4 years (SD ± 10.2), 63% females, BMI 26.3 (IQ 22.2-30.5), and NIHSS score 8 (IQR 1.5-20). The agreement between the FC5 and CEM was good (CCC 0.791) when evaluating paired HR measurements in SR. Meanwhile, the FC5 provided weak agreement (CCC 0.211) and low accuracy (MAPE 16.48%) when compared to CEM recordings in AF. Regarding the accuracy of the IRN feature, analysis found a low sensitivity (34%) and high specificity (100%) for detecting AF. (4) Conclusion: The FC5 was accurate at assessing the HR during SR, but the accuracy during AF was poor. In contrast, the IRN feature was acceptable for guiding decisions regarding AF screening in stroke patients.

Emerging Technologies for Identifying Atrial Fibrillation.

Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.

Population-Based Screening for Atrial Fibrillation.

Atrial fibrillation (AF) is a common and morbid arrhythmia. Stroke is a major hazard of AF and may be preventable with oral anticoagulation. Yet since AF is often asymptomatic, many individuals with AF may be unaware and do not receive treatment that could prevent a stroke. Screening for AF has gained substantial attention in recent years as several studies have demonstrated that screening is feasible. Advances in technology have enabled a variety of approaches to facilitate screening for AF using both medical-prescribed devices as well as consumer electronic devices capable of detecting AF. Yet controversy about the utility of AF screening remains owing to concerns about potential harms resulting from screening in the absence of randomized data demonstrating effectiveness of screening on outcomes such as stroke and bleeding. In this review, we summarize current literature, present technology, population-based screening considerations, and consensus guidelines addressing the role of AF screening in practice.

Contactless Vital Signs Acquisition Using Video Photoplethysmography, Motion Analysis and Passive Infrared Thermography Devices During Emergency Department Walk-In Triage in Pandemic Conditions.

BACKGROUND: Contactless vital signs (VS) measurement with video photoplethysmography (vPPG), motion analysis (MA), and passive infrared thermometry (pIR) has shown promise. OBJECTIVES: To compare conventional (contact-based) and experimental contactless VS measurement approaches for emergency department (ED) walk-in triage in pandemic conditions. METHODS: Patients' heart rates (HR), respiratory rates (RR), and temperatures were measured with cardiorespiratory monitor and vPPG, manual count and MA, and contact thermometers and pIR, respectively. RESULTS: There were 475 walk-in ED patients studied (95% of eligible). Subjects were 35.2 ± 20.8 years old (range 4 days‒95 years); 52% female, 0.2% transgender; had Fitzpatrick skin type of 2.3 ± 1.4 (range 1‒6), Emergency Severity Index of 3.0 ± 0.6 (range 2‒5), and contact temperature of 36.83°C (range 35.89-39.4°C) (98.3°F [96.6‒103°F]). Pediatric HR and RR data were excluded from analysis due to research challenges associated with pandemic workflow. For a 30-s, unprimed "Triage" window in 377 adult patients, vPPG-MA acquired 377 (100%) HR measurements featuring a mean difference with cardiorespiratory monitor HR of 5.9 ± 12.8 beats/min (R = 0.6833) and 252 (66.8%) RR measurements featuring a mean difference with manual RR of -0.4 ± 2.6 beats/min (R = 0.8128). Subjects' Emergency Severity Index components based on conventional VS and contactless VS matched for 83.8% (HR) and 89.3% (RR). Filtering out vPPG-MA measurements with low algorithmic confidence reduced VS acquired while improving correlation with conventional measurements. The mean difference between contact and pIR temperatures was 0.83 ± 0.67°C (range -1.16-3.5°C) (1.5 ± 1.2°F [range -2.1-6.3°F]); pIR fever detection improved with post hoc adjustment for mean bias. CONCLUSION: Contactless VS acquisition demonstrated good agreement with contact methods during adult walk-in ED patient triage in pandemic conditions; clinical applications will need further study.

Validation of Wearable Device Consisting of a Smart Shirt with Built-In Bioelectrodes and a Wireless Transmitter for Heart Rate Monitoring in Light to Moderate Physical Work.

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes.