Accuracy of Self-Injection Locking Radar System for Vital Signs Detection During the COVID-19 Pandemic at a Hospital in Taiwan: Measuring Vital Signs Accurately with SIL Radar for Hospital Healthcare.

BACKGROUND Self-injection locking (SIL) radar uses continuous-wave radar and an injection-locked oscillator-based frequency discriminator that receives and demodulates radar signals remotely to monitor vital signs. This study aimed to compare SIL radar with traditional electrocardiogram (ECG) measurements to monitor respiratory rate (RR) and heartbeat rate (HR) during the COVID-19 pandemic at a single hospital in Taiwan. MATERIAL AND METHODS We recruited 31 hospital staff members (16 males and 15 females) for respiratory rates (RR) and heartbeat rates (HR) detection. Data acquisition with the SIL radar and traditional ECG was performed simultaneously, and the accuracy of the measurements was evaluated using Bland-Altman analysis. RESULTS To analyze the results, participates were divided into 2 groups (individual subject and multiple subjects) by gender (male and female), or 4 groups (underweight, normal weight, overweight, and obesity) by body mass index (BMI). The results were analyzed using mean bias errors (MBE) and limits of agreement (LOA) with a 95% confidence interval. Bland-Altman plots were utilized to illustrate the difference between the SIL radar and ECG monitor. In all BMI groups, results of RR were more accurate than HR, with a smaller MBE. Furthermore, RR and HR measurements of the male groups were more accurate than those of the female groups. CONCLUSIONS We demonstrated that non-contact SIL radar could be used to accurately measure HR and RR for hospital healthcare during the COVID-19 pandemic.

"I Don't Think It's on Anyone's Radar": The Workforce and System Barriers to Healthcare for Indigenous Women Following a Traumatic Brain Injury Acquired through Violence in Remote Australia.

Aboriginal and Torres Strait Islander women experience high rates of traumatic brain injury (TBI) as a result of violence. While healthcare access is critical for women who have experienced a TBI as it can support pre-screening, comprehensive diagnostic assessment, and referral pathways, little is known about the barriers for Aboriginal and Torres Strait Islander women in remote areas to access healthcare. To address this gap, this study focuses on the workforce barriers in one remote region in Australia. Semi-structured interviews and focus groups were conducted with 38 professionals from various sectors including health, crisis accommodation and support, disability, family violence, and legal services. Interviews and focus groups were audiotaped and transcribed verbatim and were analysed using thematic analysis. The results highlighted various workforce barriers that affected pre-screening and diagnostic assessment including limited access to specialist neuropsychology services and stable remote primary healthcare professionals with remote expertise. There were also low levels of TBI training and knowledge among community-based professionals. The addition of pre-screening questions together with professional training on TBI may improve how remote service systems respond to women with potential TBI. Further research to understand the perspectives of Aboriginal and Torres Strait Islander women living with TBI is needed.

Lapses in the Person Radar: ADHD Symptoms Predict Difficulty in Interpersonal Distancing.

OBJECTIVE: Across contexts, from social cognition to the COVID-19 pandemic response, individual variation in the regulation of interpersonal distance has typically been viewed as a voluntary choice. Here we examine the frequency of unintentional lapses in interpersonal distancing, and their relationship with childhood ADHD symptoms. METHOD: We administered a novel measure of difficulty with interpersonal distancing across three undergraduate samples (total N = 1,225), in addition to measures of recalled childhood ADHD symptoms, mind wandering, and hyperfocus. RESULTS: Almost all (>97%) participants reported unintentional lapses in maintaining interpersonal distance, with 16% experiencing such lapses frequently. Thirty percent of the variance in these reports was accounted for by attentional traits: Inattentive and hyperactive/impulsive ADHD symptoms jointly predicted difficulties with interpersonal distancing, with the former relationship fully mediated by hyperfocus and spontaneous mind wandering. CONCLUSION: Both inattentive and hyperactive/impulsive ADHD symptoms confer vulnerability to frequent unintentional lapses in interpersonal distancing.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar.

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland-Altman consistency analysis statistical method with the algorithm's respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt.

This paper describes a novel way to measure, process, analyze, and compare respiratory signals acquired by two types of devices: a wearable sensorized belt and a microwave radar-based sensor. Both devices provide breathing rate readouts. First, the background research is presented. Then, the underlying principles and working parameters of the microwave radar-based sensor, a contactless device for monitoring breathing, are described. The breathing rate measurement protocol is then presented, and the proposed algorithm for octave error elimination is introduced. Details are provided about the data processing phase; specifically, the management of signals acquired from two devices with different working principles and how they are resampled with a common processing sample rate. This is followed by an analysis of respiratory signals experimentally acquired by the belt and microwave radar-based sensors. The analysis outcomes were checked using Levene's test, the Kruskal-Wallis test, and Dunn's post hoc test. The findings show that the proposed assessment method is statistically stable. The source of variability lies in the person-triggered breathing patterns rather than the working principles of the devices used. Finally, conclusions are derived, and future work is outlined.

Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring.

Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still unmet needs in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time-frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under pre- and post-exercise conditions and were repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements were observed in terms of precision, accuracy, and stability. The performance was reflected by achieving an averaged Pearson correlation coefficient (PCC) of 93.8% on multiple subjects. We believe that the proposed estimation method will help address the needs for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

Non-contact screening system based for COVID-19 on XGBoost and logistic regression.

BACKGROUND: The coronavirus disease (COVID-19) effected a global health crisis in 2019, 2020, and beyond. Currently, methods such as temperature detection, clinical manifestations, and nucleic acid testing are used to comprehensively determine whether patients are infected with the severe acute respiratory syndrome coronavirus 2. However, during the peak period of COVID-19 outbreaks and in underdeveloped regions, medical staff and high-tech detection equipment were limited, resulting in the continued spread of the disease. Thus, a more portable, cost-effective, and automated auxiliary screening method is necessary. OBJECTIVE: We aim to apply a machine learning algorithm and non-contact monitoring system to automatically screen potential COVID-19 patients. METHODS: We used impulse-radio ultra-wideband radar to detect respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital and compared them with 144 radar monitoring data from healthy controls. Then, the XGBoost and logistic regression (XGBoost + LR) algorithms were used to classify the data according to patients and healthy subjects. RESULTS: The XGBoost + LR algorithm demonstrated excellent discrimination (precision = 92.5%, recall rate = 96.8%, AUC = 98.0%), outperforming other single machine learning algorithms. Furthermore, the SHAP value indicates that the number of apneas during REM, mean heart rate, and some sleep parameters are important features for classification. CONCLUSION: The XGBoost + LR-based screening system can accurately predict COVID-19 patients and can be applied in hotels, nursing homes, wards, and other crowded locations to effectively help medical staff.

A Mixed-Signal Chip-Based Configurable Coherent Photoacoustic-Radar Sensing Platform for In Vivo Temperature Monitoring and Vital Signs Detection.

For precise health status monitoring and accurate disease diagnostics in the current COVID-19 pandemic, it is essential to detect various kinds of target signals robustly under high noise and strong interferences. Moreover, the health monitoring system is preferred to be realized in a small form factor for convenient mass deployments. A CMOS-integrated coherent sensing platform is proposed to achieve the goal, which synergetically leverages quadrature coherent photoacoustic (PA) detection and coherent radar sensing for achieving universal healthcare. By utilizing configurable mixed-signal quadrature coherent PA detection, high sensitivity and enhanced specificity can be achieved. In-phase (I) and quadrature (Q) templates are specifically designed to accurately sense and precisely reconstruct the target PA signals in a coherent mode. By mixed-signal implementation leveraging an FPGA to generate template waveforms adaptively, accurate tracking and precise reconstruction on the target PA signal can be attained based on the early-late tracking principle. The multiplication between the received PA signal and the templates is implemented efficiently in analog-domain by the Gilbert cell on-chip. In vivo blood temperature monitoring was realized based on the integrated PA sensing platform fabricated in a 65-nm CMOS process. With an integrated radar sensor deployed in the indoor scenario, noncontact monitoring on respiration and heartbeat rates can be attained based on electromagnetic (EM) sensing. By complementary usage of PA-EM sensing mechanisms, comprehensive health status monitoring and precise remote disease diagnostics can be achieved for the currentglobal COVID-19 pandemic and the future pervasive healthcare in the Internet of Everything (IoE) era.

COVID RADAR app: Description and validation of population surveillance of symptoms and behavior in relation to COVID-19.

BACKGROUND: Monitoring of symptoms and behavior may enable prediction of emerging COVID-19 hotspots. The COVID Radar smartphone app, active in the Netherlands, allows users to self-report symptoms, social distancing behaviors, and COVID-19 status daily. The objective of this study is to describe the validation of the COVID Radar. METHODS: COVID Radar users are asked to complete a daily questionnaire consisting of 20 questions assessing their symptoms, social distancing behavior, and COVID-19 status. We describe the internal and external validation of symptoms, behavior, and both user-reported COVID-19 status and state-reported COVID-19 case numbers. RESULTS: Since April 2nd, 2020, over 6 million observations from over 250,000 users have been collected using the COVID Radar app. Almost 2,000 users reported having tested positive for SARS-CoV-2. Amongst users testing positive for SARS-CoV-2, the proportion of observations reporting symptoms was higher than that of the cohort as a whole in the week prior to a positive SARS-CoV-2 test. Likewise, users who tested positive for SARS-CoV-2 showed above average risk social-distancing behavior. Per-capita user-reported SARS-CoV-2 positive tests closely matched government-reported per-capita case counts in provinces with high user engagement. DISCUSSION: The COVID Radar app allows voluntarily self-reporting of COVID-19 related symptoms and social distancing behaviors. Symptoms and risk behavior increase prior to a positive SARS-CoV-2 test, and user-reported case counts match closely with nationally-reported case counts in regions with high user engagement. These results suggest the COVID Radar may be a valid instrument for future surveillance and potential predictive analytics to identify emerging hotspots.

An Overview of Signal Processing Techniques for Remote Health Monitoring Using Impulse Radio UWB Transceiver.

Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR- UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate's health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.

A Novel Non-contact Self-Injection-Locked Radar for Vital Sign Sensing and Body Movement Monitoring in COVID-19 Isolation Ward.

BACKGROUND: The outbreak of Coronavirus disease (COVID-19) pandemic has become the most serious global health issue. Isolation policy in hospitals is one of the most crucial protocols to prevent nosocomial infection of COVID-19. It is important to monitor and assess the physical conditions of the patients in isolation. METHODS: Our institution has installed the novel non-contact wireless sensor for vital sign sensing and body movement monitoring for patients in COVID-19 isolation ward. RESULTS: We have collected and compared data between the radar record with the nurse's handover record of two patients, one recorded for 13 days and the other recorded for 5 days. The P value by Fisher's exact test were 0.139 (temperature, P > 0.05) and 0.292 (heart beat rate, P > 0.05) respectively. CONCLUSIONS: This is the first report about the application experience of this equipment. Therefore we attempted to share the experience and try to apply this equipment in COVID-19 patients in future to offer the more reliable and safe policy.