Empirical Review of Various Thermography-based Computer-aided Diagnostic Systems for Multiple Diseases

The lifestyle led by today's generation and its negligence towards health is highly susceptible to various diseases. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and high-cost treatment. Thermography-based technology, aided with machine learning, for screening inflammation in the human body is non-invasive and cost-wise appropriate. It requires very little equipment, especially in rural areas with limited facilities. Recently, Thermography-based monitoring has been deployed worldwide at various organizations and public gathering points as a first measure of screening COVID-19 patients. In this article, we systematically compare the state-of-the-art feature extraction approaches for analyzing thermal patterns in the human body, individually and in combination, on a platform using three publicly available Datasets of medical thermal imaging, four Feature Selection methods, and four well-known Classifiers, and analyze the results. We developed and used a two-level sampling method for training and testing the classification model. Among all the combinations considered, the classification model with Unified Feature-Sets gave the best performance for all the datasets. Also, the experimental results show that the classification accuracy improves considerably with the use of feature selection methods. We obtained the best performance with a features subset of 45, 57, and 39 features (from Unified Feature Set) with a combination of mRMR and SVM for DB-DMR-IR and DB-FOOT-IR and a combination of ReF and RF for DB-THY-IR. Also, we found that for all the feature subsets, the features obtained are relevant, non-redundant, and distinguish normal and abnormal thermal patterns with the accuracy of 94.75% on the DB-DMR-IR dataset, 93.14% on the DB-FOOT-IR dataset, and 92.06% on the DB-THY-IR dataset. [ FROM AUTHOR] Copyright of ACM Transactions on Intelligent Systems & Technology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Performance and Early Results from the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectroscopy Mission

The Earth Surface Mineral Dust Source Investigation (EMIT) acquires new observations of the Earth from a state-of-the-art, optically fast F/1.8 visible to short wavelength infrared imaging spectrometer with high signal-to-noise ratio and excellent spectroscopic uniformity. EMIT was launched to the International Space Station from Cape Canaveral, Florida, on July 14, 2022 local time. The EMIT instrument is the latest in a series of more than 30 imaging spectrometers and testbeds developed at the Jet Propulsion Laboratory, beginning with the Airborne Imaging Spectrometer that first flew in 1982. EMIT's science objectives use the spectral signatures of minerals observed across the Earth's arid and semi-arid lands containing dust sources to update the soil composition of advanced Earth System Models (ESMs) to better understand and reduce uncertainties in mineral dust aerosol radiative forcing at the local, regional, and global scale, now and in the future. EMIT has begun to collect and deliver high-quality mineral composition determinations for the arid land regions of our planet. Over 1 billion high-quality mineral determinations are expected over the course of the one-year nominal science mission. Currently, detailed knowledge of the composition of the Earth's mineral dust source regions is uncertain and traced to less than 5,000 surface sample mineralogical analyses. The development of the EMIT imaging spectrometer instrumentation was completed successfully, despite the severe impacts of the COVID-19 pandemic. The EMIT Science Data System is complete and running with the full set of algorithms required. These tested algorithms are open source and will be made available to the broader community. These include calibration to measured radiance, atmospheric correction to surface reflectance, mineral composition determination, aggregation to ESM resolution, and ESM runs to address the science objectives. In this paper, the instrument characteristics, ground calibration, in-orbit performance, and early science results are reported. © 2023 IEEE.

Imbalance of Peripheral Temperature, Sympathovagal, and Cytokine Profile in Long COVID.

A persistent state of inflammation has been reported during the COVID-19 pandemic. This study aimed to assess short-term heart rate variability (HRV), peripheral body temperature, and serum cytokine levels in patients with long COVID. We evaluated 202 patients with long COVID symptoms categorized them according to the duration of their COVID symptoms (≤120 days, n = 81; >120 days, n = 121), in addition to 95 healthy individuals selected as controls. All HRV variables differed significantly between the control group and patients with long COVID in the ≤120 days group (p < 0.05), and participants in the long COVID ≤120 days group had higher temperatures than those in the long COVID >120 days group in all regions analysed (p < 0.05). Cytokine analysis showed higher levels of interleukin 17 (IL-17) and interleukin 2 (IL-2), and lower levels of interleukin 4 (IL-4) (p < 0.05). Our results suggest a reduction in parasympathetic activation during long COVID and an increase in body temperature due to possible endothelial damage caused by the maintenance of elevated levels of inflammatory mediators. Furthermore, high serum levels of IL-17 and IL-2 and low levels of IL-4 appear to constitute a long-term profile of COVID-19 cytokines, and these markers are potential targets for long COVID-treatment and prevention strategies.

Stress detection and monitoring based on low-cost mobile thermography.

The high level of stress in modern life is one of the huge problems of the 21st century society, especially in the context of the Covid-19 pandemic. With the pandemic, the need for inexpensive, portable and easy-to-use health monitoring tools (mental and physical) has increased. Of particular importance here is mobile (smartphone) thermography, as it enables the initial detection and self-control of stress, which being intensified nowadays, is the cause of many diseases, depression and health problems. The smartphone thermal imaging camera responds to the strict sanitary guidelines, offering contact-free, painless and non-invasive operation. Additionally, it is included in the group of low-cost solutions available for home use. It is an alternative to commonly used (often expensive and unavailable to everyone): EMG, ECG, EEG, GSR or other high-cost stress detection tools. Thermal imaging by analyzing abnormalities or temperature changes allows for detection application. Therefore, the aim of this work is to determine the possibilities of a low-budget mobile thermal imaging camera in detecting stress, detecting and analyzing stress by identifying the characteristics of psychophysiological signals with the individual characteristics of the participants, along with the correlation. The participants' reactions to the film introducing stress tension up to the climax of the action were recorded thermographically. Data was processed in OpenCV. In the usual observation, stress often remained unnoticed. However, the thermographic analysis provided detailed information on the impact of the film's stressful situation on the participants, with the possibility of distinguishing the stages of stress. The results of the preliminary pilot study were presented, which indicated the variability of temperature and heart rate as important indicators of stress - with the simultaneous significance of individual characteristics of the participant. Smartphone stress thermography is a promising method of monitoring human stress, especially at home.

Treatment of patients with Coronavirus infection COVID-19 at the post-hospital stage using hyperbaric oxygenation

Introduction. It is commonly known that patients who have been exposed to the coronavirus in the past and are in the recovery stage now, experience a decline in health-related quality of life, decreased productivity and decrease in cognitive functions, energy and breathing discomfort. aim. Efficiency analysis of hyperbaric oxygenation therapy for patients with syndrome on outpatient treatment stage of rehabilitation. This article also gives consideration to the management problems of patients who have been exposed to COVID-19 in the outpatient treatment stage. We consider disease process and theoretical determinants for the progress of pathological conditions as the result of earlier disease. material and methods. 110 outpatients who have been exposed to the coronavirus in the past and treated with hyperbaric oxygenation therapy are included in the research. Results estimation was based on a patient-reported outcomes questionnaire and simple, noninvasive methods, including remote thermography. results and discussion. The hyperbaric oxygenation method has shown us high efficiency for reducing residual symptoms of COVID-19. Conclusion. Inclusion of hyperbaric oxygenation into Post-COVID-19 rehabilitation program leads to immediate treatment effects, decreasing faintness and fatigability, increasing exercise tolerance, decreasing breathing discomfort, and, as a consequence helping to speed up recovery and getting back to social life and work- related activities. noninvasive methods, including remote thermography. results and discussion. The hyperbaric oxygenation method has shown us high efficiency for reducing residual symptoms of COVID-19. Conclusion. Inclusion of hyperbaric oxygenation into Post-COVID-19 rehabilitation program leads to immediate treatment effects, decreasing faintness and fatigability, increasing exercise tolerance, decreasing breathing discomfort, and, as a consequence helping to speed up recovery and getting back to social life and work- related activities. noninvasive methods, including remote thermography. results and discussion. The hyperbaric oxygenation method has shown us high efficiency for reducing residual symptoms of COVID-19. Conclusion. Inclusion of hyperbaric oxygenation into Post-COVID-19 rehabilitation program leads to immediate treatment effects, decreasing faintness and fatigability, increasing exercise tolerance, decreasing breathing discomfort, and, as a consequence helping to speed up recovery and getting back to social life and work- related activities.

Temperature screening and detection of Covid-19 in school-aged children: A retrospective cohort study assessing the efficacy of temperature screening in schools

In July 2020, Corinth School District was the first in Mississippi to return to the classroom setting. Coronavirus disease 2019 (Covid-19) protocols were developed to maintain the safety of students. These included mandatory masking, seating charts, desk spacing, sanitizing protocols, lunch within classrooms, alteration of extracurriculars, cancellation of assemblies, and quarantine policies. Temperature screenings were also performed. Students registering as febrile would undergo Covid-19 testing. To evaluate the efficacy of temperature scanning as a surveillance method for Covid-19 in the school setting, deidentified data was obtained from the Corinth School District. Overall incidence and grade level incidence of Covid-19 were calculated in children attending school from July 27, 2020 to September 25, 2020. Data were examined for a correlation between documented fevers and Covid-19 positivity. Reports provided by the school district were investigated for positive test groupings signifying a school-related outbreak. Of 28 children with fevers at school, zero tested positive for Covid-19. Twenty-six children tested positive for Covid-19;none were febrile at school. The incidence of Covid-19 in our population during the study period was 1.03%. Incidence in elementary students was 0.34%, 0.93% in middle school, and 2.51% in high school students. There were no school outbreaks during the study period. Both relative risk and odds ratio were calculated as equal to zero (0.00). Temperature scanning is not a sensitive screening method for Covid-19 in school children.Copyright © 2023 Policy Studies Organization.

Characterization of tungiasis infection and morbidity using thermography in Kenya revealed higher disease burden during COVID-19 school closures.

BACKGROUND: Tungiasis is a neglected tropical skin disease caused by the sand flea Tunga penetrans. Female fleas penetrate the skin, particularly at the feet, and cause severe inflammation. This study aimed to characterize disease burden in two highly affected regions in Kenya, to test the use of thermography to detect tungiasis-associated inflammation and to create a new two-level classification of disease severity suitable for mapping, targeting, and monitoring interventions. METHODS: From February 2020 to April 2021, 3532 pupils age 8-14 years were quasi-randomly selected in 35 public primary schools and examined for tungiasis and associated symptoms. Of the infected pupils, 266 were quasi-randomly selected and their households visited, where an additional 1138 family members were examined. Inflammation was assessed using infra-red thermography. A Clinical score was created combining the number of locations on the feet with acute and chronic symptoms and infra-red hotspots. RESULTS: The overall prevalence of tungiasis among all the school pupils who were randomly selected during survey rounds 1 and 3 was 9.3% [95% confidence interval (CI): 8.4-10.3]. Based on mixed effects logistic models, the odds of infection with tungiasis among school pupils was three times higher in Kwale (coastal Kenya) than in Siaya [western Kenya; adjusted odds ratio (aOR) = 0.36, 95% CI: 0.18-0.74]; three times higher in males than in females (aOR = 3.0, 95% CI: 2.32-3.91) and three times lower among pupils sleeping in a house with a concrete floor (aOR = 0.32, 95% CI: 0.24-0.44). The odds of finding an infected person among the household population during surveys before the COVID-19 pandemic was a third (aOR = 0.32, 95% CI: 0.19-0.53) of that when schools were closed due to COVID-19 restrictions and approximately half (aOR = 0.44, 95% CI: 0.29-0.68) in surveys done after school re-opening (round 3). Infection intensity was positively correlated with inflammation as measured by thermography (Spearman's rho = 0.68, P < 0.001) and with the clinical score (rho = 0.86, P < 0.001). Based on the two-level classification, severe cases were associated with a threefold higher level of pain (OR = 2.99, 95% CI: 2.02-4.43) and itching (OR = 3.31, 95% CI: 2.24-4.89) than mild cases. CONCLUSIONS: Thermography was a valuable addition for assessing morbidity and the proposed two-level classification of disease severity clearly separated patients with mild and severe impacts. The burden of tungiasis was considerably higher in households surveyed during COVID-19 restrictions suggesting underlying risks are found in the home environment more than in school.

Measurement and Processing of Thermographic Data of Passing Persons for Epidemiological Purposes.

Non-contact temperature measurement of persons during an epidemic is the most preferred measurement option because of the safety of personnel and minimal possibility of spreading infection. The use of infrared (IR) sensors to monitor building entrances for infected persons has seen a major boom between 2020 and 2022 due to the COVID-19 epidemic, but with questionable results. This article does not deal with the precise determination of the temperature of an individual person but focuses on the possibility of using infrared cameras for monitoring the health of the population. The aim is to use large amounts of infrared data from many locations to provide information to epidemiologists so they can have better information about potential outbreaks. This paper focuses on the long-term monitoring of the temperature of passing persons inside public buildings and the search for the most appropriate tools for this purpose and is intended as the first step towards creating a useful tool for epidemiologists. As a classical approach, the identification of persons based on their characteristic temperature values over time throughout the day is used. These results are compared with the results of a method using artificial intelligence (AI) to evaluate temperature from simultaneously acquired infrared images. The advantages and disadvantages of both methods are discussed.

Increased Handpiece Speeds without Air Coolant: Aerosols and Thermal Impact.

This study assessed the impact of increased speed of high-speed contra-angle handpieces (HSCAHs) on the aerosolization of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surrogate virus and any concomitant thermal impact on dental pulp. A bacteriophage phantom-head model was used for bioaerosol detection. Crown preparations were performed with an NSK Z95L Contra-Angle 1:5 (HSCAH-A) and a Bien Air Contra-Angle 1:5 Nova Micro Series (HSCAH-B) at speeds of 60,000, 100,000, and 200,000 revolutions per minute (rpm), with no air coolant. Bioaerosol dispersal was measured with Φ6-bacteriophage settle plates, air sampling, and particle counters. Heating of the internal walls of the pulp chambers during crown preparation was assessed with an infrared camera with HSCAH-A and HSCAH-B at 200,000 rpm (water flows ≈15 mL min-1 and ≈30 mL min-1) and an air-turbine control (≈23.5 mL min-1) and correlated with remaining tissue thickness measurements. Minimal bacteriophage was detected on settle or air samples with no notable differences observed between handpieces or speeds (P > 0.05). At all speeds, maximum settled aerosol and average air detection was 1.00 plaque-forming units (pfu) and 0.08 pfu/m3, respectively. Irrespective of water flow rate or handpiece, both maximum temperature (41.5°C) and temperature difference (5.5°C) thresholds for pulpal health were exceeded more frequently with reduced tissue thickness. Moderate and strong negative correlations were observed based on Pearson's correlation coefficient, between remaining dentine thickness and either differential (r = -0.588) or maximum temperature (r = -0.629) measurements, respectively. Overall, HSCAH-B generated more thermal energy and exceeded more temperature thresholds compared to HSCAH-A. HSCAHs without air coolant operating at speeds of 200,000 rpm did not increase bioaerosolization in the dental surgery. Thermal risk is variable, dependent on handpiece design and remaining dentine thickness.

Proposal for a method of controlling heart rate during step exercise

The COVID-19 has caused many elderly people to become inactive. Lack of exercise is thought to increase the risk of developing lifestyle-related diseases. In addition, many elderly people are unaware of the amount of exercise they should be doing, and often exercise excessively, exercise ineffectively, or do not know the amount of exercise that is appropriate for them. In Chapter 2 of this paper, we confirm that heart rate can be predicted using LSTM from thermographic images during step exercise. In Chapter 3, we conducted a basic study on how to measure heart rate during step exercise and control it to a constant heart rate. © 2022 IEEE.

Proposal for a method of controlling heart rate during step exercise

The COVID-19 has caused many elderly people to become inactive. Lack of exercise is thought to increase the risk of developing lifestyle-related diseases. In addition, many elderly people are unaware of the amount of exercise they should be doing, and often exercise excessively, exercise ineffectively, or do not know the amount of exercise that is appropriate for them. In Chapter 2 of this paper, we confirm that heart rate can be predicted using LSTM from thermographic images during step exercise. In Chapter 3, we conducted a basic study on how to measure heart rate during step exercise and control it to a constant heart rate. © 2022 IEEE.

Comparative Analysis of Medical Imaging Techniques Used for the Detection of Thyroid Gland with an Emphasis on Thermogram

In today's scenario, every human being in the world is scared of the COVID-19 pandemic, and everyone in the world want early medication for COVID-19. So in this paper, a study of numerous medical imaging techniques used for detection of thyroid gland in the human being in different stages of human life is presented. Early thyroid illness discovery is that the main necessary in growing the speed of diagnosing cure and survival of the affected creature. There are a various medical imaging techniques used to detect thyroid diseases in human being. Some techniques are used to diagnose stages of thyroid cancer in humans. This paper is used to explain the procedure for the diagnosis of images, investigation of images, pros, cons, and limitations of imaging techniques. A comparative study of various medical imaging techniques explains the Thermogram image is the noninvasive system that detects the relative temperature variations in patients form thyroid diseases. In this paper, survey of the various algorithm implemented is studied for thermography, MRI, ultrasound, and mammography from the literature review and it is observed that detection of thyroid abnormalities using different techniques not only decides many factors such as segmentation of the region of thyroid gland, image quality, and extraction features and classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automated Thermal Screening for COVID-19 Using Machine Learning

In the last two years, millions of lives have been lost due to COVID-19. Despite the vaccination programmes for a year, hospitalization rates and deaths are still high due to the new variants of COVID-19. Stringent guidelines and COVID-19 screening measures such as temperature check and mask check at all public places are helping reduce the spread of COVID-19. Visual inspections to ensure these screening measures can be taxing and erroneous. Automated inspection ensures an effective and accurate screening. Traditional approaches involve identification of faces and masks from visual camera images followed by extraction of temperature values from thermal imaging cameras. Use of visual imaging as a primary modality limits these applications only for good-lighting conditions. The use of thermal imaging alone for these screening measures makes the system invariant to illumination. However, lack of open source datasets is an issue to develop such systems. In this paper, we discuss our work on using machine learning over thermal video streams for face and mask detection and subsequent temperature screening in a passive non-invasive way that enables an effective automated COVID-19 screening method in public places. We open source our NTIC dataset that was used for training our models and was collected at 8 different locations. Our results show that the use of thermal imaging is as effective as visual imaging in the presence of high illumination. This performance stays the same for thermal images even under low-lighting conditions, whereas the performance with visual trained classifiers show more than 50% degradation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Non-fever COVID-19 Detection by Infrared Imaging

This study proposed an infrared image-based method for febrile and non-febrile people screening to comply with the society needs for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on the face infrared imaging for early COVID-19 detection in people with and without fever;(ii) Recruiting 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RTqPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used with a convolutional neural network (CNN) to develop the algorithm that took face infrared images as input and classified the tested individuals into three groups: fever (high risk), non-febrile (medium risk), and without fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 C were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected non-febrile COVID group. The COVID-19 (+) main risk factor was to be in the non-febrile medium-risk group, compared with age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spatio-temporal variations of night-time lights at early stages of the COVID-19 epidemic in the United States

The COVID-19 epidemic swept the world and continued to spread. Without effective medical treatments and vaccine during the early stage of the pandemic, local governments in various countries had to lock down cities and adopt non-pharmaceutical interventions (NPIs), such as the stay-at-home order, social distancing, and so on. NPIs against the COVID-19 epidemic have significantly changed socioeconomic activities in cities. However, characteristics and patterns of urban socio-economic activities under this influence are still unclear. Benefiting from the development of earth observation technologies, such large-scale changes in socioeconomic activities are enough to be captured by satellites through remotely sensed night-time lights (NTL). In this study, we selected 20 major cities in the United States including New York, Chicago and Los Angeles to analyze spatio-temporal variations of NTL caused by the lockdown of cities. The first round of COVID-19 epidemic occurred in the United States in mid-March 2020. Since March 2020, American cities have successively issued stay-at-home orders, but there are differences in the time and strictness of policy implementation. Large cities have a higher population density and a higher intensity of social activities, so they are more susceptible to infectious diseases. The diversity of lockdown dates and strictness of lockdowns in cities in the United States are conducive to investigating the spatio-temporal variations of NTL. We acquired monthly averaged NPP VIIRS products of February, March and April, 2020, which are from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (NPP). We further analyzed the spatial pattern, distance decay and disparities in land use types of changes in NTL. Results show that NTL generally dimmed by 5-8% in U.S. cities caused by the lockdown of cities. There are 6 cities where the luminous brightness has dropped by more than 10%: Chicago, Dallas, Denver, Detroit, Minneapolis, and St. Louis. Among them, Minneapolis has the largest decrease in luminous brightness, with a decrease of about 40% in March. The spatial change of NTL shows obvious "core-periphery" pattern that the reduction of NTL declines with the distance from the city center. This is mainly because the central area of the city is a concentrated commercial area. After the closure of the city, commercial activities have dropped significantly, resulting in an obvious reduction in NTL around city centers. The reduction of NTL varies among diverse urban land use types. In New York, NTL decreased the most on land for residence and aviation facilities by 12% and 11%, respectively. In Chicago, NTL generally decreased by 20% in all types of urban land, and NTL recovered after one month of the lockdown of cities in other urban land except sports facilities land. This study only analyzes the spatio-temporal changes of NTL. In the future, it can be combined with multi-source data to explain the driving force of NTL changes. Nighttime light remote sensing effectively reflects urban socio-economic dynamics with an important application in monitoring and assessing socio-economic impacts of emergencies. © 2022 National Remote Sensing Bulletin. All rights reserved.

An Internet of Things (IoT) Based Image Process Screening to Prevent COVID-19 in Public Gatherings

The global community is now coping with such a significant issue as the Covid-19 virus, public gatherings are experiencing certain restrictions in order to stop the virus from spreading further. The issue takes on a bigger significance during religious pilgrimages such as the Hajj and the Umrah, when tens of thousands, if not hundreds of thousands, of people gather in holy cities to participate in religious rituals. During such a time period, it is quite difficult to single out an infected person from among the big crowd that is there. The current screening approach only includes a single element of identity, which means that there is a possibility that the screening process may fail because there will not be enough identification. The use of thermal imaging provides a higher level of accuracy when compared to more conventional ways of testing for viral infections in the detection of these symptoms in crowded locations. The primary method that is utilised to determine whether or not a person is infected with the virus is an image processing algorithm that is built in MATLAB. The first step in the process of acquiring an image is to divide the video that is being captured into individual frames. Following this step, the frames that have been focussed are processed in a number of ways. The temperature of a person's body may be estimated by taking a thermal image and then using the RGB separation feature on it. In order to categorise and sort the data, the k-means approach was used as part of the segmentation operation. In addition to eliminating the skin frequency, it also gets rid of the background noise, which often has a higher frequency than the skin frequency. The Viola–Jones technique, which may be used to identify the person's breathing rate, can be used to locate the end of a person's nose, specifically the tip of the nose. The Cascaded Adaboost Classifier is an option that may be used to finish the classification process after the operation has been completed. The suggested method has an accuracy rate of 89.23 percent and a simulation period of around 60 seconds, which guarantees the safety of huge groups of people's public health. [ FROM AUTHOR]

Thermal Screening Solution Using Infrared Thermography and Closed-Circuit Television

Closed-circuit television camera (CCTV) and thermal imaging devices are used to detect febrile individuals entering establishments for Coronavirus 2019 (COVID-19) containment. Real-time tracking in post-COVID is manually checked by security personnel, which has risks of less efficiency due to human errors, as advance thermal cameras are unaffordable for some business owners. The main goal is converting an installed CCTV interfaced with infrared sensor to develop an economical thermal screening system with acoustic alarm. In this project, the colored and heatmap images transmitted from the thermal camera were processed through OpenCV. A calibration method was also performed to validate the temperature reading from the thermal camera. The project comes with graphical user interface (GUI) connected into a database, which visually tracks individuals exhibits elevated body temperature. The performance of the system shows above 95% accuracy upon conducting an inexpensive calibration check. The significance of this project is highlighting the effective mitigation of virus spread which offers safe and contactless analysis of potential individuals showing early symptoms of COVID-19. Additional features can be added for future work such as facemask detector, multiple thermal camera setup, and Login Options making the device and application exclusively for business owners. © 2022 IEEE.

Analysis of the influence of external conditions on temperature readings in thermograms and adaptive adjustment of the measured temperature value

Measuring human temperature is a crucial step in preventing the spread of diseases such as COVID-19. For the proper operation of an automatic body temperature measurement system throughout the year, it is necessary to consider outdoor conditions. In this paper, the effect of atmospheric factors on facial temperature readings using infrared thermography is investigated. A thorough analysis of the variation of facial temperature with the prevailing atmospheric conditions was carried out using recordings collected over two years and compared with air temperature values at 1 hour accuracy. A method that takes account of outdoor conditions on temperature readings was proposed. We developed a correction curve with coefficients values based on an analysis of the recordings of people entering the building. Such a method will allow an effective real-time fever screening in public places. © 2022 IEEE.

Internet of Things-based smart helmet to detect possible COVID-19 infections

At the beginning of 2020, while the world was celebrating New Year’s Eve, China’s headquarter of the World Health Organization came across a case of pneumonia in the city of Wuhan, China and was termed as coronavirus. Initially the symptoms were fever, cold, and cough;so thermal screening was done that could cause infection to the medical staff. In this chapter we discuss the design of the system known as smart helmet that has the capability to detect coronavirus automatically by using thermal imaging, which is used to capture the image with less human interaction. The thermal camera technology is integrated with smart helmets and combined with Internet of Things technology for monitoring of the screening process to get the real-time data. It is equipped with facial recognition technology;it can also display personal information of the infectee, which can automatically take temperature and can detect more infectee than normal thermal screening. © 2022 Elsevier Inc. All rights reserved.

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding.

As obesity is a serious problem in the human population, overloading of the horse's thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse's back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.