Remote sensing of multiple vital signs using a CMOS camera-equipped infrared thermography system and its clinical application in rapidly screening patients with suspected infectious diseases

Integrating RGB-thermal image sensors for non-contact automatic respiration rate monitoring

Respiration rate (RR) holds significance as a human health indicator. Presently, the conventional RR monitoring system requires direct physical contact, which may cause discomfort and pain. Therefore, this paper proposes a non-contact RR monitoring system integrating RGB and thermal imaging through RGB-thermal image alignment. The proposed method employs an advanced image processing algorithm for automatic region of interest (ROI) selection. The experimental results demonstrated a close correlation and a lower error rate between measured thermal, measured RGB, and reference data. In summary, the proposed non-contact system emerges as a promising alternative to conventional contact-based approaches without the associated discomfort and pain.

Current methods for contactless optical patient diagnosis: a systematic review

Many countries around the world face a shortage of medical personnel, leading to work overload or even burnout. This calls for political and scientific solutions to relieve the medical personnel. The measurement of vital signs in hospitals is still predominately carried out manually with traditional contact-based methods, taking over a substantial share of the medical personnel's workload. The introduction of contactless methods for vital sign monitoring (e.g., with a camera) has great potential to relieve the medical personnel. This systematic review's objective is to analyze the state of the art in the field of contactless optical patient diagnosis. This review distinguishes itself from already existing reviews by considering studies that do not only propose the contactless measurement of vital signs but also include an automatic diagnosis of the patient's condition. This means that the included studies incorporate the physician's reasoning and evaluation of vital signs into their algorithms, allowing an automated patient diagnosis. The literature screening of two independent reviewers resulted in a total of five eligible studies. The highest number of studies (three) introduce methods for the risk assessment of infectious diseases, one study introduces a method for the risk assessment of cardiovascular diseases, and one study introduces a method for the diagnosis of obstructive sleep apnea. Overall, high heterogeneity in relevant study parameters is reported among the included studies. The low number of included studies indicates a large research gap and emphasizes the demand for further research on this emerging topic.

Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU

BACKGROUND AND OBJECTIVE

Continuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU.

METHODS

A non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates.

RESULTS

The results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland-Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms.

CONCLUSION

The proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings.

Utility of thermal image scanning in screening for febrile patients in cold climates

BACKGROUND

Infrared thermography (IRT) for fever screening systems was introduced in not only general hospitals, but also orthopedic hospitals as a countermeasure against the spread of coronavirus disease 2019 (COVID-19). Despite the widespread use of IRT, various results have shown low and high efficacies, so the utility of IRT is controversial, especially in cold climates. The aims of this study were to investigate the utility of IRT in screening for fever in a cold climate and to devise suitable fever screening in orthopedic surgery for COVID-19.

METHODS

A total of 390 orthopedic surgery patients were enrolled to the outdoor group and 210 hospital staff members were enrolled to the indoor group. Thermographic temperature at the front of the face in the outdoor group was immediately measured after entering our hospital from a cold outdoor environment. Measurements for the indoor group were made after staying in the hospital (environmental temperature, 28 °C) for at least 5 h. Body temperature was then measured using an axillary thermometer >15 min later in both groups.

RESULTS

In the outdoor group, mean thermographic temperature was significantly lower than axillary temperature and IRT could not detect febrile patients with axillary temperatures >37.0 °C. Mean thermographic temperature was significantly lower in the outdoor group than in the indoor group. Sensitivity was 11.5% for the outdoor group, lower than that for the indoor group.

CONCLUSIONS

We verified that IRT was not accurate in a cold climate. IRT is inadequate as a screening method to accurately detect febrile individuals, so we believe that stricter countermeasures for second screening need to be employed to prevent nosocomial infections and disease clusters of COVID-19, even in orthopedic hospitals.

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention-A Survey

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.

Contactless Vital Sign Monitoring System for In-Vehicle Driver Monitoring Using a Near-Infrared Time-of-Flight Camera

We demonstrate a Contactless Vital Sign Monitoring (CVSM) system and road-test the system for in-cabin driver monitoring using a near-infrared indirect Time-of-Flight (ToF) camera. The CVSM measures both heart rate (HR) and respiration rate (RR) by leveraging the simultaneously measured grayscale and depth information from a ToF camera. For a camera-based driver monitoring system (DMS), key challenges from varying background illumination and motion-induced artifacts need to be addressed. In this study, active illumination and depth-based motion compensation are used to mitigate these two challenges. For HR measurements, active illumination allows the system to work under various lighting conditions, while our depth-based motion compensation has the advantage of directly measuring the motion of the driver without making prior assumptions about the motion artifacts. In addition, we can extract RR directly from the chest wall motion, circumventing the challenge of acquiring RR from the near-infrared photoplethysmography (PPG) signal of low signal quality. We investigate the system’s performance in various scenarios, including monitoring both drivers and passengers while driving on highways and local roads. Our results show that our CVSM system is ambient light agnostic, and the success rates of HR measurements on the highway are 82% and 71.9% for the passenger and driver, respectively. At the same time, we show that the system can measure RR on users driving on a highway with a mean deviation of −1.4 breaths per minute (BPM). With reliable HR and RR measurement in the vehicle, the CVSM system could one day be a key enabler to sudden sickness or drowsiness detection in DMS.

Are Non-Contact Thermometers an Option in Anaesthesia? A Narrative Review on Thermometry for Perioperative Medicine

Measurement of core body temperature—clinical thermometry—provides critical information to anaesthetists during perioperative care. The value of this information is determined by the accuracy of the measurement device used. This accuracy must be maintained despite external influences such as the operating room temperature and the patient’s thermoregulatory defence. Presently, perioperative thermometers utilise invasive measurement sites. The public health challenge of the COVID-19 pandemic, however, has highlighted the use of non-invasive, non-contact infrared thermometers. The aim of this article is to review common existing thermometers used in perioperative care, their mechanisms of action, accuracy, and practicality in comparison to infrared non-contact thermometry used for population screening during a pandemic. Evidence currently shows that contact thermometry varies in accuracy and practicality depending on the site of measurements and the method of sterilisation or disposal between uses. Despite the benefits of being a non-invasive and non-contact device, infrared thermometry used for population temperature screening lacks the accuracy required in perioperative medicine. Inaccuracy may be a consequence of uncontrolled external temperatures, the patient’s actions prior to measurement, distance between the patient and the thermometer, and the different sites of measurement. A re-evaluation of non-contact thermometry is recommended, requiring new studies in more controlled environments.

Deterioration Mechanisms and Advanced Inspection Technologies of Aluminum Windows

Aluminum windows are crucial components of building envelopes since they connect the indoor space to the external environment. Various external causes degrade or harm the functioning of aluminum windows. In this regard, inspecting the performance of aluminum windows is a necessary task to keep buildings healthy. This review illustrates the deterioration mechanisms of aluminum windows under various environmental conditions with an intention to provide comprehensive information for developing damage protection and inspection technologies. The illustrations reveal that moisture and chloride ions have the most detrimental effect on deteriorating aluminum windows in the long run, while mechanical loads can damage aluminum windows in a sudden manner. In addition, multiple advanced inspection techniques potential to benefit assessing aluminum window health state are discussed in order to help tackle the efficiency problem of traditional visual inspection. The comparison among those techniques demonstrates that infrared thermography can help acquire a preliminary defect profile of inspected windows, whereas ultrasonic phased arrays technology demonstrates a high level of competency in analyzing comprehensive defect information. This review also discusses the challenges in the scarcity of nanoscale corrosion information for insightful understandings of aluminum window corrosion and reliable window inspection tools for lifespan prediction. In this regard, molecular dynamics simulation and artificial intelligence technology are recommended as promising tools for better revealing the deterioration mechanisms and advancing inspection techniques, respectively, for future directions. It is envisioned that this paper will help upgrade the aluminum window inspection scheme and contribute to driving the construction of intelligent and safe cities.

Non-contact measurement system for COVID-19 vital signs to aid mass screening—An alternate approach

The entire world is facing a pandemic after the COVID-19 outbreak reported in Wuhan City in China. The number of newly infected cases and deaths are increasing on an hourly basis. The birth and spread of the n-coronavirus is a mystery to the world. Social distancing, staying home, and washing hands frequently with soap and water are the present norms for not getting infected or spreading it. The symptoms of a COVID-19 infected person are high fever, nasal congestion, aches and pains, difficulty in breathing, loss of smell and taste, and sore throat. The standard approach for screening any COVID-19 patient is to measure the body temperature, usually by infrared temperature sensors. This primary indication makes the person to be required to undergo the COVID-19 test. In most of the cases, the test results provide false positive and true negative kind of misclassification. Delay in finding the COVID-19 carriers makes it a challenging task for any healthcare administration to reduce the growth of positive cases.

Few more vital signs like heart rate, oxygen saturation, respiratory rate, and body temperature are more relevant to make the person take the COVID test. In most of the screening tests at crowded places like airports, railways stations, and industries, the primary signs vital in detecting COVID are missed. Moreover, the measurement of all these physiological parameters requires dedicated measuring devices and skilled healthcare professionals. The cost of implementation and procurement gives more financial burden during this economic crisis. An alternate approach to measuring all these vital signs is extracting feature points from the thermal and or visible light reflected from the face of subjects. These feature sets are given to convolutional neural network (CNN) models for training and the trained model can predict the signs from the test inputs.

The preliminary readings would be instrumental in suggesting the person undergo the COVID-19 test. It will also act as a continuous monitoring system to read the health condition of vulnerable and treatment undergoing persons. Such systems can be incorporated in any surveillance system and immigration zones to find overseas travelers’ health conditions. The risk of affecting healthcare field workers can be reduced. The possible implementation of health drones creates a pathway to Tele-diagnosis.

Contactless Vital Sign Monitoring System for Heart and Respiratory Rate Measurements with Motion Compensation Using a Near-Infrared Time-of-Flight Camera

This study describes a contactless vital sign monitoring (CVSM) system capable of measuring heart rate (HR) and respiration rate (RR) using a low-power, indirect time-of-flight (ToF) camera. The system takes advantage of both the active infrared illumination as well as the additional depth information from the ToF camera to compensate for the motion-induced artifacts during the HR measurements. The depth information captures how the user is moving with respect to the camera and, therefore, can be used to differentiate where the intensity change in the raw signal is from the underlying heartbeat or motion. Moreover, from the depth information, the system can acquire respiration rate by directly measuring the motion of the chest wall during breathing. We also conducted a pilot human study using this system with 29 participants of different demographics such as age, gender, and skin color. Our study shows that with depth-based motion compensation, the success rate (system measurement within 10% of reference) of HR measurements increases to 75%, as compared to 35% when motion compensation is not used. The mean HR deviation from the reference also drops from 21 BPM to −6.25 BPM when we apply the depth-based motion compensation. In terms of the RR measurement, our system shows a mean deviation of 1.7 BPM from the reference measurement. The pilot human study shows the system performance is independent of skin color but weakly dependent on gender and age.

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.

Heat risk exacerbation potential for neurology patients during the COVID-19 pandemic and related isolation

COVID-19 may increase the risk of heat-related symptoms during hot weather since vulnerable populations, including the elderly and those with neurological disabilities, must continue to self-isolate, often indoors. Within the chronic neurological patient population, indoor conditions in summer months present a hazard because of impaired and/or altered thermoregulation, including poor hydration status due to both autonomic and behavioral dysfunction(s). To address this increased risk, telemedicine protocols should include an assessment of the patient's environmental parameters, and when combined with physiological data from wearable devices, identify those with neurological diseases who are at higher risk of heat illness. Personalized medicine during times of self-isolation must be encouraged, and using smart technology in ambient assisted living solutions, including e-health to monitor physiological parameters are highly recommended, not only during extreme weather conditions but also during times of increased isolation and vulnerability.

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Infrared thermal imaging (IRI) is a contact-less technology able to monitor human skin temperature for biomedical applications and in real-life contexts. Its capacity to detect fever was exploited for mass screening during past epidemic emergencies as well as for the current COVID-19 pandemic. However, the only assessment of fever may not be selective for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Hence, novel approaches for IRI data analysis have been investigated. The present review aims to describe how IRI have been employed during the last epidemics, highlighting the potentialities and the limitations of this technology to contain the contagions. Specifically, the methods employed for automatic face recognition and fever assessment and IRI's performances in mass screening at airports and hospitals are reviewed. Moreover, an overview of novel machine learning methods for IRI data analysis, aimed to identify respiratory diseases, is provided. In addition, IRI-based smart technologies developed to support the healthcare during the COVID-19 pandemic are described. Finally, relevant guidelines to fully exploit IRI for COVID-19 identification are defined, to improve the effectiveness of IRI in the detection of the SARS-CoV-2 infection.

Review of the efficacy of infrared thermography for screening infectious diseases with applications to COVID-19

Purpose: The recent coronavirus disease 2019 (COVID-19) pandemic, which spread across the globe in a very short period of time, revealed that the transmission control of disease is a crucial step to prevent an outbreak and effective screening for viral infectious diseases is necessary. Since the severe acute respiratory syndrome (SARS) outbreak in 2003, infrared thermography (IRT) has been considered a gold standard method for screening febrile individuals at the time of pandemics. The objective of this review is to evaluate the efficacy of IRT for screening infectious diseases with specific applications to COVID-19. Approach: A literature review was performed in Google Scholar, PubMed, and ScienceDirect to search for studies evaluating IRT screening from 2002 to present using relevant keywords. Additional literature searches were done to evaluate IRT in comparison to traditional core body temperature measurements and assess the benefits of measuring additional vital signs for infectious disease screening. Results: Studies have reported on the unreliability of IRT due to poor sensitivity and specificity in detecting true core body temperature and its inability to identify asymptomatic carriers. Airport mass screening using IRT was conducted during occurrences of SARS, Dengue, Swine Flu, and Ebola with reported sensitivities as low as zero. Other studies reported that screening other vital signs such as heart and respiratory rates can lead to more robust methods for early infection detection. Conclusions: Studies evaluating IRT showed varied results in its efficacy for screening infectious diseases. This suggests the need to assess additional physiological parameters to increase the sensitivity and specificity of non-invasive biosensors.

Myths and methodologies: Degrees of freedom - limitations of infrared thermographic screening for Covid-19 and other infections

Around the planet, in many different scenarios, skin temperature is being used as a surrogate measure of deep body (core) temperature in the assessment of whether an individual is infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease (Covid-19), as indicated by the presence of fever. The key question is whether this is a valid methodology. If it is not, we run the risk of falsely excluding individuals from places they may want, or need, to go. We also run the risk of falsely allowing people into places where they can spread the undetected infection they have. In this review, we explore these and associated questions. We establish the limited utility of the current methodology for the mass screening of individuals for Covid-19 related fever using infrared thermography. We propose the development of an alternative method that may prove to be more sensitive.

Contact-free sensor signals as a new digital biomarker for cardiovascular disease: chances and challenges

Multiple sensor systems are used to monitor physiological parameters, activities of daily living and behaviour. Digital biomarkers can be extracted and used as indicators for health and disease. Signal acquisition is either by object sensors, wearable sensors, or contact-free sensors including cameras, pressure sensors, non-contact capacitively coupled electrocardiogram (cECG), radar, and passive infrared motion sensors. This review summarizes contemporary knowledge of the use of contact-free sensors for patients with cardiovascular disease and healthy subjects following the PRISMA declaration. Chances and challenges are discussed. Thirty-six publications were rated to be of medium (31) or high (5) relevance. Results are best for monitoring of heart rate and heart rate variability using cardiac vibration, facial camera, or cECG; for respiration using cardiac vibration, cECG, or camera; and for sleep using ballistocardiography. Early results from radar sensors to monitor vital signs are promising. Contact-free sensors are little invasive, well accepted and suitable for long-term monitoring in particular in patient's homes. A major problem are motion artefacts. Results from long-term use in larger patient cohorts are still lacking, but the technology is about to emerge the market and we can expect to see more clinical results in the near future.

Applications of CMOS Devices for the Diagnosis and Control of Infectious Diseases

Emerging infectious diseases such as coronavirus disease of 2019 (COVID-19), Ebola, influenza A, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) in recent years have threatened the health and security of the global community as one of the greatest factors of mortality in the world. Accurate and immediate diagnosis of infectious agents and symptoms is a key to control the outbreak of these diseases. Rapid advances in complementary metal-oxide-semiconductor (CMOS) technology offers great advantages like high accuracy, high throughput and rapid measurements in biomedical research and disease diagnosis. These features as well as low cost, low power and scalability of CMOS technology can pave the way for the development of powerful devices such as point-of-care (PoC) systems, lab-on-chip (LoC) platforms and symptom screening devices for accurate and timely diagnosis of infectious diseases. This paper is an overview of different CMOS-based devices such as optical, electrochemical, magnetic and mechanical sensors developed by researchers to mitigate the problems associated with these diseases.

Steroid 5 alpha-reductase 2 enzyme variants, biomass exposure and tobacco use in Mexican patients with prostate cancer

The presence of the genetic variants of the steroid 5-alpha reductase 2 enzyme, which is encoded by the SRD5A2 gene, has been associated with an increased risk of developing prostate cancer among certain ethnic groups. However, these molecular studies have not been conducted on the Mexican population. The analysis of the genetic variants, rs9282858 and rs523349, was performed in 101 males with prostate cancer and 100 healthy controls classified as males without prostate abnormalities (n=60) and males with benign prostatic hyperplasia (n=40), to identify a probable association with this cancer type in the Northeast Mexican population. An association was identified between prostate cancer and biomass exposure [P=0.012; odds ratio (OR), 2.89; confidence interval (CI)=1.21-6.88] and tobacco use (P=0.028; OR=1.88; CI=1.07-3.31), while no association was observed between cancer development and the rs9282858 variant, or between a protective effect and the rs523349 variant. Notably, an association was identified between rs523349 and biomass exposure (P=0.013, OR=3.17; CI=1.23-8.17 for the G risk allele, and OR=0.32, CI=0.12-0.81 for the C protective allele) using the dominant genetic model. To the best of our knowledge, the present study was the first of its type to investigate the Mexican population with prostate cancer.

Return to school in the COVID-19 era: considerations for temperature measurement

COVID-19 pandemics required a reorganisation of social spaces to prevent the spread of the virus. Due to the common presence of fever in the symptomatic patients, temperature measurement is one of the most common screening protocols. Indeed, regulations in many countries require temperature measurements before entering shops, workplaces, and public buildings. Due to the necessity of providing rapid non-contact and non-invasive protocols to measure body temperature, infra-red thermometry is mostly used. Many countries are now facing the need to organise the return to school and universities in the COVID-19 era, which require solutions to prevent the risk of contagion between students and/or teachers and technical/administrative staff. This paper highlights and discusses some of the strengths and limitations of infra-red cameras, including the site of measurements and the influence of the environment, and recommends to be careful to consider such measurements as a single "safety rule" for a good return to normality.

Non-contact thermography-based respiratory rate monitoring in a post-anesthetic care unit

In patients at high risk of respiratory complications, pulse oximetry may not adequately detect hypoventilation events. Previous studies have proposed using thermography, which relies on infrared imaging, to measure respiratory rate (RR). These systems lack support from real-world feasibility testing for widespread acceptance. This study enrolled 101 spontaneously ventilating patients in a post-anesthesia recovery unit. Patients were placed in a 45° reclined position while undergoing pulse oximetry and bioimpedance-based RR monitoring. A thermography camera was placed approximately 1 m from the patient and pointed at the patient’s face, recording continuously at 30 frames per second for 2 min. Simultaneously, RR was manually recorded. Offline imaging analysis identified the nares as a region of interest and then quantified nasal temperature changes frame by frame to estimate RR. The manually calculated RR was compared with both bioimpedance and thermographic estimates. The Pearson correlation coefficient between direct measurement and bioimpedance was 0.69 (R² = 0.48), and that between direct measurement and thermography was 0.95 (R² = 0.90). Limits of agreement analysis revealed a bias of 1.3 and limits of agreement of 10.8 (95% confidence interval 9.07 to 12.5) and − 8.13 (− 6.41 to − 9.84) between direct measurements and bioimpedance, and a bias of −0.139 and limits of agreement of 2.65 (2.14 to 3.15) and − 2.92 (− 2.41 to 3.42) between direct measurements and thermography. Thermography allowed tracking of the manually measured RR in the post-anesthesia recovery unit without requiring patient contact. Additional work is required for image acquisition automation and nostril identification.

Bonuses and pitfalls of a paperless drive-through screening and COVID-19: A field report

As the world witnessed the rapid spread of SARS-CoV-2, the World Health Organization has called for governing bodies worldwide to intensify case findings, contact tracing, monitoring, and quarantine or isolation of contacts with COVID-19. Drive-through (DT) screening is a form of case detection which has recently gain preference globally. Proper implementation of this system can help remediate the outbreak.

Infection Screening System Using Thermography and CCD Camera with Good Stability and Swiftness for Non-contact Vital-Signs Measurement by Feature Matching and MUSIC Algorithm

Screening systems for infectious diseases based on fever have been implemented at international airports to prevent the spread of infection for over a decade. Currently, only Infrared Thermography (IRT) is used for screening and measuring facial skin temperature, which is one of clinical indicators of potential infection. Aiming at higher accuracy in screening, our group adopted heart rate (HR) and respiration rate (RR) for the first time as the new screening parameters. In our previous study, we proposed a screening system based on dual image sensors, which include IRT and a charged-coupled devices (CCD) camera. The sensors can measure three vital signs simultaneously, namely HR, RR, and facial skin temperature. For the measurement of RR in this system, stability and swiftness must be applied for application in airports. In this study, we introduce feature matching and multiple signal classification (MUSIC) algorithm in this system. Feature matching between thermal images and RGB images captured by a CCD camera and IRT, respectively, is used to detect the nose and mouth in IRT, which helps extract respiration signals corresponding to airflow from breathing. In addition, the MUSIC algorithm improves the accuracy of RR frequency estimations in limited time respiration signal and achieves swiftness. The proposed method improves stability by simultaneously detecting the nose and mouth in thermal images, and enhances the accuracy of estimated RR using the MUSIC algorithm. By using this system, we evaluate the accuracy of the estimated vital signs. The performance of this screening system was evaluated using data obtained from 12 influenza patients and 13 healthy subjects at a clinical facility in Fukushima, Japan.

The Use of Digital Health in the Detection and Management of COVID-19

Digital health is uniquely positioned to enhance the way we detect and manage infectious diseases. This commentary explores the potential of implementing digital technologies that can be used at different stages of the COVID-19 outbreak, including data-driven disease surveillance, screening, triage, diagnosis, and monitoring. Methods that could potentially reduce the exposure of healthcare providers to the virus are also discussed.

Contactless Vital Signs Measurement System Using RGB-Thermal Image Sensors and Its Clinical Screening Test on Patients with Seasonal Influenza

Background: In the last two decades, infrared thermography (IRT) has been applied in quarantine stations for the screening of patients with suspected infectious disease. However, the fever-based screening procedure employing IRT suffers from low sensitivity, because monitoring body temperature alone is insufficient for detecting infected patients. To overcome the drawbacks of fever-based screening, this study aims to develop and evaluate a multiple vital sign (i.e., body temperature, heart rate and respiration rate) measurement system using RGB-thermal image sensors. Methods: The RGB camera measures blood volume pulse (BVP) through variations in the light absorption from human facial areas. IRT is used to estimate the respiration rate by measuring the change in temperature near the nostrils or mouth accompanying respiration. To enable a stable and reliable system, the following image and signal processing methods were proposed and implemented: (1) an RGB-thermal image fusion approach to achieve highly reliable facial region-of-interest tracking, (2) a heart rate estimation method including a tapered window for reducing noise caused by the face tracker, reconstruction of a BVP signal with three RGB channels to optimize a linear function, thereby improving the signal-to-noise ratio and multiple signal classification (MUSIC) algorithm for estimating the pseudo-spectrum from limited time-domain BVP signals within 15 s and (3) a respiration rate estimation method implementing nasal or oral breathing signal selection based on signal quality index for stable measurement and MUSIC algorithm for rapid measurement. We tested the system on 22 healthy subjects and 28 patients with seasonal influenza, using the support vector machine (SVM) classification method. Results: The body temperature, heart rate and respiration rate measured in a non-contact manner were highly similarity to those measured via contact-type reference devices (i.e., thermometer, ECG and respiration belt), with Pearson correlation coefficients of 0.71, 0.87 and 0.87, respectively. Moreover, the optimized SVM model with three vital signs yielded sensitivity and specificity values of 85.7% and 90.1%, respectively. Conclusion: For contactless vital sign measurement, the system achieved a performance similar to that of the reference devices. The multiple vital sign-based screening achieved higher sensitivity than fever-based screening. Thus, this system represents a promising alternative for further quarantine procedures to prevent the spread of infectious diseases.

Development of a low-cost, portable, pediatric infection screening system using simultaneous measurement of multiple vital signs

The primary cause of death among children under age 5 years is acute respiratory infection, such as pneumonia. Detection of infection at the earliest point of contagion is necessary, to reduce morbidity and prevent infectious disease epidemics; therefore, identifying abnormal vital signs is essential. For early detection of pediatric infections, we developed a low-cost, portable, rapid screening system of pediatric infection. The system simultaneously measures three vital signs: heart rate (HR), respiration rate (RR), and body temperature (Temp) within 10 seconds using a pulse sensor, Doppler radar, and an infrared thermopile. Vital sign signal processing and computation are conducted using an Arduino Nano microprocessor, enabling the small, lightweight, and portable design of this system. Moreover, the cost-effectiveness of the system facilitates system applications in developing countries, which have the highest levels of pediatric mortality. We conducted trial measurement in Bayangol Health Center, Ulaanbaatar, Mongolia in 2019. A total of 50 children (age 1-14 years, 26 boys/24 girls) were enrolled in this study. Bland-Altman plot and Pearson correlation methods were used to evaluate the accuracy of the proposed system. The correlation coefficients were calculated as HR: r=0.92, RR: r=0.8, and Temp: r=0.82, with p<; 0.01. The system appears promising for rapid and convenient detection of infection in children.

Non-contact monitoring of heart rate variability using medical radar for the evaluation of dynamic changes in autonomic nervous activity during a head-up tilt test

Electrocardiography (ECG) is a mandatory standard for monitoring electrical activity of the heart in many clinical settings such as intensive care and emergency units. However, in situations wherein the skin is damaged, such as acute burn injuries, it is impossible to efficiently attach electrodes to the skin. In this study, we developed a non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject. The heart rate variability (HRV) was analysed using an autoregressive model (AR) from the measured beat-to-beat intervals during a head-up tilt test. To investigate the feasibility of the proposed system, we compared medical radar and ECG recording by using Lin's correlation coefficient and Bland-Altman analysis, which showed a negligible mean difference from the substantial agreement of Lin's correlation coefficient of 0.9 between the radar and ECG. The non-contact radar clearly monitored dynamic changes in HRV indices induced by the head-up tilt test. This type of non-contact HRV-sensing technique as an alternative approach has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings.

Stable Contactless Sensing of Vital Signs Using RGB-Thermal Image Fusion System with Facial Tracking for Infection Screening

Infrared thermography (IRT) has been used to screen febrile passengers in international airports for over a decade. However, fever-based infection screening using IRT suffered from low sensitivity because measurements can be affected by ambient temperature, humidity, etc. In our previous study, we proposed an RGB-thermal image fusion system to measure vital signs i.e., the RGB camera detects tiny changes in color from facial skin, associated with blood flow, to estimate heart rate, and IRT senses temperature changes around the nasal area, caused by respiration, to measure respiratory rate). The inclusion of heart and respiratory rates lead to increased screening accuracy. In the present study, to promote the widespread use of our system in real-world settings, a face detection and tracking method was developed and implemented into the system, thereby enabling the accurate and stable measurement of vital signs. We assessed heart and respiratory rate estimation via an RGB-thermal image fusion system using Bland-Altman plots and statistical analysis.

Validation of a wearable biosensor device for vital sign monitoring in septic emergency department patients in Rwanda

Objective

Critical care capabilities needed for the management of septic patients, such as continuous vital sign monitoring, are largely unavailable in most emergency departments (EDs) in low- and middle-income country (LMIC) settings. This study aimed to assess the feasibility and accuracy of using a wireless wearable biosensor device for continuous vital sign monitoring in ED patients with suspected sepsis in an LMIC setting.

Methods

This was a prospective observational study of pediatric (≥2 mon) and adult patients with suspected sepsis at the Kigali University Teaching Hospital ED. Heart rate, respiratory rate and temperature measurements were continuously recorded using a wearable biosensor device for the duration of the patients’ ED course and compared to intermittent manually collected vital signs.

Results

A total of 42 patients had sufficient data for analysis. Mean duration of monitoring was 32.8 h per patient. Biosensor measurements were strongly correlated with manual measurements for heart rate (r = 0.87, p < 0.001) and respiratory rate (r = 0.75, p < 0.001), although were less strong for temperature (r = 0.61, p < 0.001). Mean (SD) differences between biosensor and manual measurements were 1.2 (11.4) beats/min, 2.5 (5.5) breaths/min and 1.4 (1.0)°C. Technical or practical feasibility issues occurred in 12 patients (28.6%) although were minor and included biosensor detachment, connectivity problems, removal for a radiologic study or exam, and patient/parent desire to remove the device.

Conclusions

Wearable biosensor devices can be feasibly implemented and provide accurate continuous heart rate and respiratory rate monitoring in acutely ill pediatric and adult ED patients with sepsis in an LMIC setting.

Dengue Fever Detecting System Using Peak-detection of Data from Contactless Doppler Radar

Infectious diseases, such as dengue fever and Middle East respiratory syndrome, have become prevalent worldwide in recent times. To conduct highly accurate and effective infection screening, we are working on the development of a contactless infection screening system using Doppler radar and thermography. In our previous work, three parameters (face temperature, heartbeat rate, and respiration rate) were used to judge whether a subject was infected. However, facial temperature measurements may be vastly different from temperatures measured at the axilla owing to influence from the immediate environment. In this study, heartbeat rate (HR), respiration rate (RR), and standard deviation of heartbeat interval (SDHI) were used to quantify the infection screening system without using facial temperature as a parameter. We found that respiratory sinus arrhythmia (RSA) diminished in patients who had dengue fever. We gathered data from 47 patients with dengue fever using a 10-GHz Doppler radar system at the National Hospital of Tropical Diseases (NHTD) in Hanoi, Vietnam. To evaluate the accuracy, the data of these patients were compared to that of 23 unaffected subjects. We observed that a linear discriminant analysis (LDA) was effective at detecting the dengue fever conditions, and the detection accuracy was approximately 97.6%.

Infrared Thermography Sensor for Disease Activity Detection in Rheumatoid Arthritis Patients

A recent review of thermography studies in rheumatoid arthritis shows limited data about disease activity and mostly focuses on differences between the thermography of rheumatoid arthritis patients and typical subjects. A retrospective study compared patients with high disease activity (n = 50), moderate disease activity (n = 16), and healthy participants (n = 42), taking into account demographic, clinical, laboratory, and thermography parameters. We applied an infrared thermography sensor and a fingers examination protocol. Outcomes included the mean temperature of five fingers of a hand: In static, post-cooling, post-rewarming, the total change in mean temperature of fingers due to cold provocation, the total change in mean temperature of fingers due to rewarming, the area under the cooling curve, the area under the heating curve, the difference between the area under the rewarming and the cooling curve, and temperature intensity distribution maps. For patients with high disease activity, a lower area under the heating curve and a lower difference between the area under the rewarming curve and the cooling curve were observed, as well as a smaller total change in mean temperature due to rewarming, compared to patients with moderate disease activity (p < 0.05). Our study findings could be helpful in patients with an equivocal clinical examination.

Use of infrared thermography in the detection of superficial phlebitis in adult intensive care unit patients: A prospective single-center observational study

Common methods to detect phlebitis may not be sufficient for patients in the intensive care unit (ICU). The goal of this study was to investigate the feasibility of infrared (IR) thermography to objectively detect phlebitis in adult ICU patients. We included a total of 128 adult ICU-patients in a pilot and subsequent validation study. Median [interquartile range] age was 62 [54–71] years and 88 (69%) patients were male. Severity of phlebitis was scored using the visual infusion phlebitis (VIP)-score, ranging from 0 (no phlebitis) to 5 (thrombophlebitis). The temperature difference (ΔT) between the insertion site and a proximal reference point was measured with IR thermography. In 78 (34%) catheters early phlebitis and onset of moderate phlebitis was observed (VIP-score of 1–3). In both the pilot and the validation study groups ΔT was significantly higher when the VIP-score was ≥1 compared to a VIP-score of 0 (p<0.01 and p<0.001, respectively). Multivariate analysis identified ΔT (p<0.001) and peripheral venous catheter (PVC) dwell time (p = 0.001) as significantly associated with phlebitis. IR thermography may be a promising technique to identify phlebitis in the ICU. An increased ΔT as determined with thermography may be a risk factor for phlebitis.

Monitoring of Cardiorespiratory Signals Using Thermal Imaging: A Pilot Study on Healthy Human Subjects

Heart rate (HR) and respiratory rate (RR) are important parameters for patient assessment. However, current measurement techniques require attachment of sensors to the patient’s body, often leading to discomfort, stress and even pain. A new algorithm is presented for monitoring both HR and RR using thermal imaging. The cyclical ejection of blood flow from the heart to the head (through carotid arteries and thoracic aorta) leads to periodic movements of the head; these vertical movements are used to assess HR. Respiratory rate is estimated by using temperature fluctuations under the nose during the respiratory cycle. To test the viability and feasibility of this approach, a pilot study was conducted with 20 healthy subjects (aged 18⁻36 and 1 aged 50 years). The study consisted of two phases: phase A (frontal view acquisitions) and phase B (side view acquisitions). To validate the results, photoplethysmography and thoracic effort (piezoplethysmography) were simultaneously recorded. High agreement between infrared thermography and ground truth/gold standard was achieved. For HR, the root-mean-square errors (RMSE) for phases A and B were 3.53 ± 1.53 and 3.43 ± 1.61 beats per minute, respectively. For RR, the RMSE between thermal imaging and piezoplethysmography stayed around 0.71 ± 0.30 breaths per minute (phase A). This study demonstrates that infrared thermography may be a promising, clinically relevant alternative for the assessment of HR and RR.

[Use of infrared thermography in ophthalmology]

Thermoregulation disorders are associated with Body temperature fluctuation. Both hyper- and hypothermia are evidence of an ongoing pathological process. Contralateral symmetry in the Body heat spread is considered normal, while asymmetry, if above a certain level, implies an underlying pathology. Infrared thermography (IRT) is employed in many medical fields including ophthalmology. The earliest attempts of eye surface temperature evaluation were made in the 19^th century. Over the last 50 years, different authors have been using this method to assess ocular adnexa, however, the technique remains insufficiently studied. The reported IRT data is often contradictory, which may be due to heterogeneity (in terms of severity) of patient groups and disparities between research parameters.