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.

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.

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.

Regression Model for Predicting Core body Temperature in Infrared Thermal Mass Screening.

With fever being one of the most prominent symptoms of COVID-19, the implementation of fever screening has become commonplace around the world to help mitigate the spread of the virus. Non-contact methods of temperature screening, such as infrared (IR) forehead thermometers and thermal cameras, benefit by minimizing infection risk. However, the IR temperature measurements may not be reliably correlated with actual core body temperatures. This study proposed a trained model prediction using IR-measured facial feature temperatures to predict core body temperatures comparable to an FDA-approved product. The reference core body temperatures were measured by a commercially available temperature monitoring system. Optimal inputs and training models were selected by the correlation between predicted and reference core body temperature. Five regression models were tested during the study. The linear regression model showed the lowest minimum-root-mean-square error (RSME) compared with reference temperatures. The temple and nose region of interest (ROI) were identified as optimal inputs. This study suggests that IR temperature data could provide comparatively accurate core body temperature prediction for rapid mass screening of potential COVID cases using the linear regression model. Using linear regression modeling, the non-contact temperature measurement could be comparable to the SpotOn system with a mean SD of ± 0.285°C and MAE of 0.240°C.

Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants

Aerial infrared (IR) thermography has been implemented in recent years, proving to be a powerful and versatile technique for performing maintenance at photovoltaic (PV) plants. Its application speed and reliability using unmanned aerial vehicles (UAVs) or drones make it extremely interesting at large PV plants, due to the associated savings in time and costs. Ground-level thermographic inspection is slower and more costly to apply, although it does provide higher optical resolution, due to being conducted closer to the PV modules being inspected. Both techniques used in combination can improve the diagnosis. An IR thermography inspection strategy is proposed for PV plants based on two stages. The first stage of the inspection is aerial, enabling thermal faults to be detected and located quickly and reliably. The second stage of the inspection is done on the ground and applied only to the most relevant incidents revealed in the first stage. This inspection strategy was applied to a 100 kW PV plant, with an improved diagnosis verified via this procedure, as the ground-level inspection detects one-off thermal incidents from objects creating shade and from solar reflections. For PV modules with open circuits or open substrings, the use of one technique or another is immaterial.

Studying the accuracy of infrared thermography for measuring core body temperature

The purpose of this study was to investigate the accuracy of infrared thermography for measuring body temperature. We compared a commercially available infrared thermal imaging camera (FLIR One) with a medical-grade oral thermometer (Welch-Allyn) as a gold standard. Measurements using the thermal imaging camera were taken from both a short distance (10cm) and long distance (50cm) from the subject. Thirty young healthy adults participated in a study that manipulated body temperature. After establishing a baseline, participants lowered their body temperature by placing their feet in a cold-water bath for 30 minutes while consuming cold water. Feet were then removed and covered with a blanket for 30 minutes as body temperature returned to baseline. During the course of the 70-minute experiment, body temperature was recorded at a 10-minute interval. The thermal imaging camera demonstrated a significant temperature difference from the gold standard from both close range (mean error: +0.433°C) and long range (mean error: +0.522°C). Despite demonstrating potential as a fast and non-invasive method for temperature screening, our results indicate that infrared thermography does not provide an accurate measurement of body temperature. As a result, infrared thermography is not recommended for use as a fever screening device. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

USING FACE MASKS IN THE CLASSROOM: THE EFFECT ON THE INDOOR ENVIRONMENT PARAMETERS AND FACE THERMOPHYSIOLOGICAL REACTIONS

Masks have been recommended as a protective tool for effectively combating the COVID-19 pandemic. In many countries, masks are required indoors, but the obligation temporarily and sporadically extends to all public places indoors and outdoors in some regions. Our study investigated the effect of wearing face masks in the classroom on the indoor environmental parameters and the human body experimentally. The study was performed at the Technical University of Sofia with 14 volunteers during regular lecture classes. Two stages were considered: with and without face masks. Measurement of the indoor environment parameters, oxygen (O-2) and carbon dioxide (CO2) concentration was continuously performed. Thermal image analysis was used to obtain the face thermograms of the participants. The results clearly showed the retention effect of the face masks on the exhaled air, leading to lower CO2 concentration in the classroom and higher O-2 concentration and humidity. It was also found that the continuous wear of a face mask for 40-45 min provoked an increment of the facial skin temperature under the mask to 37 degrees CC and even more. The rise of the temperature of the inner cantus of the eye showed that the face mask triggered the body's thermoregulation, causing thermophysiological reactions.

Infrared Thermography for Measuring Elevated Body Temperature: Clinical Accuracy, Calibration, and Evaluation.

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (Δcb), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a Δcb between ±0.03 °C with standard deviation (σΔcb) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37-38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs.

Studying the Accuracy and Function of Different Thermometry Techniques for Measuring Body Temperature.

The purpose of this study was to determine which thermometry technique is the most accurate for regular measurement of body temperature. We compared seven different commercially available thermometers with a gold standard medical-grade thermometer (Welch-Allyn): four digital infrared thermometers (Wellworks, Braun, Withings, MOBI), one digital sublingual thermometer (Braun), one zero heat flux thermometer (3M), and one infrared thermal imaging camera (FLIR One). Thirty young healthy adults participated in an experiment that altered core body temperature. After baseline measurements, participants placed their feet in a cold-water bath while consuming cold water for 30 min. Subsequently, feet were removed and covered with a blanket for 30 min. Throughout the session, temperature was recorded every 10 min with all devices. The Braun tympanic thermometer (left ear) had the best agreement with the gold standard (mean error: 0.044 °C). The FLIR One thermal imaging camera was the least accurate device (mean error: -0.522 °C). A sign test demonstrated that all thermometry devices were significantly different than the gold standard except for the Braun tympanic thermometer (left ear). Our study showed that not all temperature monitoring techniques are equal, and suggested that tympanic thermometers are the most accurate commercially available system for the regular measurement of body temperature.

Dynamic Variations in Infrared Skin Temperature of Weaned Pigs Experimentally Inoculated with the African Swine Fever Virus: A Pilot Study.

African swine fever (ASF) is a devastating viral disease in pigs and is therefore economically important for the swine industry. ASF is characterized by a short incubation period and immediate death, making the early identification of ASF-infected pigs essential. This pilot-scale study evaluates whether the infrared thermography (IRT) technique can be used as a diagnostic tool to detect changes in skin temperature (Tsk) during the early stages of disease development in experimentally ASF-infected pigs. Clinical symptoms and rectal temperatures (Tcore) were recorded daily, and IRT readings during the experimental ASF infection were analyzed. All infected pigs died at 5-8 days post inoculation (dpi), and the incubation period was approximately 4 dpi. The average Tcore increased from 0 dpi (38.9 ± 0.3 °C) to 7 dpi (41.0 ± 0.5 °C) and decreased by 8 dpi (39.8 ± 0 °C). The maximum Tsk of ASF-infected pigs increased from 2 (35.0 °C) to 3 dpi (38.5 °C). The mean maximum Tsk observed from three regions on the skin (ear, inguinal, and neck) significantly increased from 2 to 3 dpi. This study presents a non-contact method for the early detection of ASF in infected pigs using thermal imaging at 3 days after ASF infection.

An attempt to construct the individual model of daily facial skin temperature using variational autoencoder.

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

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.

A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.