ABSTRACT
In December 2019, several cases of pneumonia caused by SARS-CoV-2 were identified in the city of Wuhan (China), which was declared by the WHO as a pandemic in March 2020 because it caused enormous problems to public health due to its rapid transmission of contagion. Being an uncontrolled case, precautions were taken all over the world to moderate the coronavirus that undoubtedly was very deadly for any person, presenting several symptoms, among them we have fever as a common symptom. A biosecurity measure that is frequently used is the taking of temperature with an infrared thermometer, which is not well seen by some specialists due to the error they present, therefore, it would not represent a safe measurement. In view of this problem, in this article a thermal image processing system was made for the measurement of body temperature by means of a drone to obtain the value of body temperature accurately, being able to be implemented anywhere, where it is intended to make such measurement, helping to combat the spread of the virus that currently continues to affect many people. Through the development of the system, the tests were conducted with various people, obtaining a more accurate measurement of body temperature with an efficiency of 98.46% at 1.45 m between the drone and the person, in such a way that if it presents a body temperature higher than 38° C it could be infected with COVID-19. © 2023 IEEE.
ABSTRACT
This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 2023 IEEE.
ABSTRACT
COVID-19 is making a huge impact both in terms of the economy and human lives. Many lost their lives due to COVID-19 which is found in most of the nations. The number of positive symptoms is increasing rapidly all over the world. To safeguard us from the virus, some protocols have been addressed by WHO in which people has to wear a mask and make a social distancing when moved in public. Therefore, social distancing places an important role in preventing us from the spread of the diseases. The minimum distance between to be maintained is informed at 6 feet informed by the health organizations. When people gathered on a group social distancing could not be maintained even if manual or any kind of technology implemented. Temperature measurement on mass gathering was also a tedious process where the monitoring is essential. Multiple methods such as thermal cameras, temperature sensors for monitoring the personnel has not been efficient. In the proposed work to monitor the social distancing between the persons an ultrasonic sensor is placed to detect the obstacle and an IR sensor to make the rover move. An encoder is used to calculate the distance based on the rpm of the wheel. Based on this input the distance is checked within this limit the obstacle is detected, an alert signal is made using the buzzer. A thermal sensor is used to measure the temperature of the person and an LCD display shows the temperature of the person and distance between obstacles. The proposed system has resulted in identifying the distance and helps in reducing the spread during the pandemic situation. © 2023 IEEE.
ABSTRACT
The COVID-19 pandemic has spread all over the world. People go to public or crowded areas (i.e., schools, universities, hospitals, and government agencies), they take a lot of time to be checked the fever symptoms because of coronavirus. Therefore, this paper presents a method to automatically detect the body temperature by distance based on the recursive least square estimation. An infrared thermal camera is utilized to measure both human and environmental temperatures in real-time within a two-meter distance. The recursive least square approach is applied to estimate parameters for these correct temperatures. A microcontroller is integrated to read, compute, and send the measured temperatures to both web browsers and smartphones using the message queuing telemetry protocol. Moreover, the module of radio frequency identification is utilized for identification of the personal information. To validate our proposed temperature measurement system, fifteen male healthy students are invited to record their body temperature. The experimental result showed that our proposed approach was the correct temperature compared with the commercial device (37 ± 0.17 ° C). However, our proposed system is more stable than the commercial device: the standard deviation of the commercial device and ours is 0.41 C and 0.09°C, respectively. The measured temperature of each person is monitored and stored in the cloud. It is easily accessed by web browsers and smartphones. In addition, our proposed system can show a warning if the measured temperature is greater than the threshold. This work promises to automatically initial selection for suspected cases of COVID-19 disease to reduce the infection of this pandemic. © 2022 IEEE.
ABSTRACT
To limit the spread of COVID-19, thermal screening cameras were installed everywhere. These cameras observe many thermal faces. These thermal face data are generally used to monitor strange temperatures for COVID-19 screening or to maintain social distancing. Big data of Thermal face generated everywhere should be used in the more practical functions. We proposed a method to measure non-contact breathing signals using thermal face data. In addition, breathing signals data estimated from thermal face data was converted to DICOM waveform Information Object Definitions (IODs) for interoperability management of medical data. The proposed method was tested on a golden reference (chest belt) with a mean accuracy of 93.52 %. a proposed method that can extract breathing signals using thermal screening cameras that are widely available around the world and manage data as healthcare interoperability information can show important potential in the public, telemedicine field in the future. © 2022 IEEE.
ABSTRACT
This paper presents the developed device created for detection and tracking of people and their faces and temperatures. The solution does not require internet access to operate, so it can be used anywhere. The so called Covid camera is designed to automate the process of measuring temperature and verifying that a person is wearing a mask. The results obtained when testing the device in real conditions are promising and will form the basis for further application research. It is worth noting that the solution was tested in real conditions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost. © 2023 CRL Publishing. All rights reserved.
ABSTRACT
Viral infections severely attack the physically frail elderly population, resulting in fatal drawbacks. The fact of having massive elderly population growth in Europe gives high priority to the detection of physical frailty and infectious diseases. This paper presents a safe, accurate, fast temperature detection system that could be integrated into homes or assisted living residences. The presented work aims to detect one of the symptoms of contagious diseases: elevated body temperature. In order to do so, we worked on recognizing eyes in thermal face images followed by scanning the detected eyes region for inner canthus temperature. Eyes detection was done by training four different sizes of You Only Look Once 5th version (YOLOv5) object detection algorithm: nano, small, medium and large. A total of 4,255 thermal images were implemented for the training process after merging two different datasets and applying data augmentation techniques. Results show a similar mAP score (99.5%) for the different trained models. The large YOLOv5 model was the fastest, working at 115 FPS. © 2022 IEEE.
ABSTRACT
The worldwide impact of the COVID-19 epidemic has been immense. Economic, educational, industrial, and other sectors all took a hit as a result of COVID-19. Unaware of how to address this, the health care industry was also hit. In the absence of a known cure, the most effective way to slow the spread of this fatal illness is to wear a face mask when doing so. Wearing a face mask when in public or conversing with people is also mandated by the WHO. Additionally, the most common symptom is high fever, which occurs in those who are unwell with this condition. As a result, we describe a system that can distinguish face masks using a regular RGB camera and identify persons with high body temperatures using a thermal camera with an 80x60 resolution. Tracking safety violations and encouraging the use of face masks may be achieved by using this method. © 2022 IEEE.
ABSTRACT
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.
ABSTRACT
With the recent societal impact of COVID-19, companies and government agencies alike have turned to thermal camera based skin temperature sensing technology to help screen for fever. However, the cost and deployment restrictions limit the wide use of these thermal sensing technologies. In this work, we present SIFTER, a low-cost system based on a RGB-thermal camera for continuous fever screening of multiple people. This system detects and tracks heads in the RGB and thermal domains and constructs thermal heat map models for each tracked person, and classifies people as having or not having fever. SIFTER can obtain key temperature features of heads in-situ at a distance and produce fever screening predictions in real-time, significantly improving screening through-put while minimizing disruption to normal activities. In our clinic deployment, SIFTER measurement error is within 0.4°F at 2 meters and around 0.6°F at 3.5 meters. In comparison, most infrared thermal scanners on the market costing several thousand dollars have around 1°F measurement error measured within 0.5 meters. SIFTER can achieve 100% true positive rate with 22.5% false positive rate without requiring any human interaction, greatly outperforming our baseline [1], which sees a false positive rate of 78.5%. © 2022 IEEE.
ABSTRACT
COVID-19 affects anyone without distinguishing ages, presents symptoms such as fever, dry cough, and shortness of breath. All this presents the infected person, some have mild symptoms while others have strong symptoms. This virus began at the end of 2019 in China, being a very contagious viral infection was declared a pandemic by the WHO so that governments take the corresponding measures. Even so, this virus affected the entire health system that, in April 2020, registered more than 80,000 deaths worldwide, where some countries had the highest mortality rate. In view of this problem, in this article the design of a body temperature measurement system made through an unmanned aerial vehicle was made to measure the body temperature of people and observe if they have a fever, with this, it would be avoided that people continue to be infected when they enter an enclosed place. Through the design of the body temperature measurement system, it is observed that its function is precise and its manipulation is friendly, it classifies people according to the measured value of the temperature, if it presents a normal value it will be allowed to enter the place, but its present a high value will be denied entry and will give notice for a previous evaluation, The important thing about this system is that it can be used anywhere where the temperature measurement is intended. © 2022 IEEE.
ABSTRACT
In an autonomous and assisted living environment the risk of COVID-19 transmission is very high. The fragile health of the tenants in combination with the indoors environment impose protection measures to be taken. A set of technological solutions can offer protection to the tenants by monitoring the individuals' distance between them, detecting if someone wears a mask or not and measuring his fever. This paper proposes to leverage the social distancing application in Ambient assisted living environments, as well as fever and mask detection, following the precautions proposed by the experts against Covid-19. The evaluation results were quite impressive and a high accuracy was achieved in every proposed module. © 2022 IEEE.
ABSTRACT
A non-contact thermometer is a device that can measure the temperature of the human body without making physical contact. During the Covid-19 pandemic, many crowd centers implemented health protocols by checking body temperature. Therefore, a device is needed to detect body temperature. In previous studies, non-contact thermometers used low-resolution thermal cameras which resulted in not being able to detect mass temperatures and inaccurate readings. In this study, a non-contact thermometer system uses an 80x60 pixel LWIR (Long Wave Infrared) thermal camera and a pi camera to read real-Time human body temperature. The image is processed so that this system only detects body temperature on the face. This system has an output in the form of a human image along with the temperature on the face displayed on the monitor, and there is a warning in the form of a buzzer if there are people who have a temperature above the specified limit. The Lepton 2.5 FLIR (forward looking infrared) thermal camera can optimally read temperatures at a distance of 0.5 meters to 2.5 meters. The temperature reading accuracy of the FLIR Lepton 2.5 thermal camera reaches 98.1%. Temperature readings on the FLIR Lepton 2.5 thermal camera have no effect on light intensity and thermal noise. © 2021 IEEE.
ABSTRACT
Social distancing and temperature screening have been widely employed to counteract the COVID-19 pandemic, sparking great interest from academia, industry and public administrations worldwide. While most solutions have dealt with these aspects separately, their combination would greatly benefit the continuous monitoring of public spaces and help trigger effective countermeasures. This work presents milliTRACE-IR, a joint mmWave radar and infrared imaging sensing system performing unobtrusive and privacy preserving human body temperature screening and contact tracing in indoor spaces. milliTRACE-IR combines, via a robust sensor fusion approach, mmWave radars and infrared thermal cameras. It achieves fully automated measurement of distancing and body temperature, by jointly tracking the subjects's faces in the thermal camera image plane and the human motion in the radar reference system. Moreover, milliTRACE-IR performs contact tracing: a person with high body temperature is reliably detected by the thermal camera sensor and subsequently traced across a large indoor area in a non-invasive way by the radars. When entering a new room, a subject is re-identified among several other individuals by computing gait-related features from the radar reflections through a deep neural network and using a weighted extreme learning machine as the final re-identification tool. Experimental results, obtained from a real implementation of milliTRACE-IR, demonstrate decimeter-level accuracy in distance/trajectory estimation, inter-personal distance estimation (effective for subjects getting as close as 0.2 m), and accurate temperature monitoring (max. errors of 0.5 degrees C). Furthermore, milliTRACE-IR provides contact tracing through highly accurate (95%) person re-identification, in less than 20 seconds.
ABSTRACT
Throughout the COVID-19 pandemic, the most common symptom displayed by patients has been a fever, leading to the use of temperature scanning as a preemptive measure to detect for potential carriers of the virus. Human employees with handheld thermometers have been used to fulfill this task, however this puts them at risk as they cannot be physically distanced, and the sequential nature of this method leads to great inconveniences and inefficiency. The proposed solution is an autonomously navigating robot capable of conversing and scanning people's temperature to detect fevers and help screen for COVID-19. To satisfy this objective, the robot must be able to navigate autonomously, detect and track people, get their temperature reading, and converse with them if it exceeds 38°C. An autonomously navigating mobile robot is used with a manipulator controlled by a face tracking algorithm, and an end effector consisting of a thermal camera, smartphone, and chatbot. In addition, technical challenges encountered, and their engineering solutions will be presented, and recommendations will be made for enhancements that could be incorporated when approaching commercialization. © 2021 IEEE.
ABSTRACT
The artificial intelligence is a computer expertise which thinks like human being and it does not need human intellect. The artificial intelligence could be categorized as: (i)Reactive machine, (ii)Machines with limited memory, (iii)Machines with a theory of mind, and (iv)Machines with self-awareness. The different applications of artificial intelligence are speech recognition, robot process automation, decision management, etc. The input given is in the form of images, videos, and sound data. The image, video is taken using high-resolution cameras like conventional thermal camera, visible IP camera, and AI-enabled thermal camera. With the advent of artificial intelligence variety of automated detection like thermal temperature detection, infrared temperature detection, mask recognition detection, computer vision was introduced and used. This survey presents a methodical assessment of artificial intelligence methods used in the detection and recognition of face and also for testing fever. A series of algorithms like independent component analysis, local binary pattern histogram, ADA boost cascade, squirrel search, HOG ,face detection and recognition in the literature. This paper highlights the automatic detection of body temperature, facial temperature and room temperature using artificial intelligence as an effective endurance. Persons with fever in public places could be identified and proper action could be taken in advance. This study is expected to provide researchers in AI a general idea of the present the current state of AI applications and inspire them in exploiting In the fight against illnesses like COVID-19, AI has a lot of promise.
ABSTRACT
The AMG8833 sensor can be utilized for a low-cost thermal camera-based body temperature measurement during COVID-19 protocol enforcement. However, the sensor is not accurate enough for body temperature measurement, so fever detection performance becomes poor. The aim of this study is to apply Random Forest as a classifier in a thermal camera body temperature measurement that uses the AMG8833 sensor and evaluate its performance in detecting fever. In addition to the AMG8833, the thermal camera made also uses a webcam for face detection, and a Raspberry Pi as a minicomputer and a place to apply the Random Forest model. That way, the Thermal camera undergoes three processes, namely face detection from the image captured from the webcam, then temperature and fever detection from AMG8833. From the receiver operating curve (ROC) test conducted, Random Forest area under curve (AUC) value is superior compared to the Logistic Regression and Decision Tree methods with a value of 0.977. Furthermore, the sensitivity and specificity values of Random Forest in detecting fever are 88.5% and 99.5%, respectively. This value is higher than a detection system that does not use Random Forest classification for fever detection. © 2021 IEEE.
ABSTRACT
This paper describes a framework for COVID-19 pandemic screening that includes a multi-infrared temperature sensor. Due to the high risk of transmission of the COVID-19 epidemic in closed areas, it is important to secure these areas in terms of epidemics. Symptoms of COVID-19 disease include fever in patients. Thermal cameras or infrared temperature sensors are used to detect this anomaly in real-time. In this study, a study was carried out on which multiple uses of infrared sensors increase the measurement performance. Additionally, the general concept of an intelligent long-range temperature measurement system with facial recognition support is presented, which may be simply integrated with this approach. © 2021 IEEE.
ABSTRACT
A COVID19 indoor safety solution that is based on IOT covers a few relevant aspects. Detection of masks and social distance, by contactless temperature sensing. By using computer vision techniques known as image processing on camera-equipped raspberry pi, Contactless Temperature Sensor relieves need for infrared sensors or thermal cameras, while Distancing Check and Mask Detection are achieved using a camera-equipped Raspberry Pi. We use an IoT-based system to promote compliance with COVID19 safety guidelines and rules and focuses people with high body temperatures stay at home most of the time because of common indoor measures, wearing masks and keeping the distance between people working no more than 1.5-2 meters. Based on the small dimensions, computer with a camera built into the Raspberry Pi has a sensors that measure temperature without contact. This device is chosen due to its affordability and small size. © 2021 IEEE.