ABSTRACT
Body temperature is a significant vital sign that can provide great insight as to the state of health of a person. Nowadays, body temperatures are monitored as often as a precaution for the COVID-19 virus. This can be achieved with the use of wearables, which can be non-invasive and convenient for anybody to use. This study aims to design and construct a wearable that can accurately detect the body temperature of a person using the MLX90614 sensor as well as an I2C enabled LCD to allow the user to monitor their temperature at a moment's notice. © 2023 IEEE.
ABSTRACT
Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.
ABSTRACT
The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.
ABSTRACT
A device comprising an oximeter and a module for detecting body temperature has been designed so that a person can readily check his or her health in crucial situations. This was accomplished by programming Arduino to output values measured by sensors such as the MAX30102 (Particle Sensor) and GY-906-BCC (Infrared Sensor). We've all been dealing with a global pandemic for the past year. As a result, there have been numerous coronavirus discoveries. The COVID-19 virus primarily affects an individual's respiratory system, lowering the patient's oxygen levels, and it causes a rise in body temperature. This approach can be quite valuable in such situations and can aid in the regular monitoring of an individual's health. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
ABSTRACT
This paper projects machine learning as a valuable tool for the restriction of the Covid-19 pandemic escalation in the global scenario. The proposed system involves detection of masked or unmasked people and a temperature sensing system for ensuring Covid-19 appropriate protocol is followed to allow only healthy person(s) in public/crowded places. The integration of Arduino Uno and MLX90614 non-contact temperature sensor, along with a MobileNetV2 machine learning model, is performed for complete execution. The system will classify a person as a masked or unmasked individual using ML techniques and detect their body temperature. If the individual meets the appropriate requirements, the system will enable them to access via the gate, which will be controlled by a servo motor in conjunction with a temperature sensor module. © 2022 IEEE.
ABSTRACT
One of the efforts to enforce health protocols is the use of body temperature checkers in every public place. Just like on campus, body temperature is checked using a thermometer gun. Problems encountered when checking the health protocol system included temperature data collection and the identity of visitors who entered the campus. Therefore, so that temperature control can be done automatically and visitor history can be viewed and saved automatically every day, body temperature detection and personal identity recognition through E-ID card and photo based on IoT is made. The realization of temperature checks can use the MLX90614 sensor which has the advantage of being able to read body temperature without requiring direct contact between the body and the sensor and is integrated using RFID which uses E-ID as an identity tag and ESP32CAM to take pictures of visitors' faces to be recorded and sent data to the internet. The purpose of this research is to design a body temperature detector and identify self-identity through IoT-based E-ID and Photos and explain the work system and performance of body temperature detectors. From the results of testing for body temperature detection and self-identification through IoT-based E-ID and photos, the results show that this system is able to retrieve temperature data, E-ID, and facial photos. The standard error that occurs during this measurement is 0.03 and the temperature difference between the two tools is 0.18° C. © 2022 IEEE.
ABSTRACT
Physicians and the government struggled considerably to limit the spread of COVID-19 during the pandemic, as there was no treatment or vaccinations available. The temperature detection system has only been used to monitor the temperature. This research work proposes an automated temperature detection system, if the temperature is lower than the predetermined temperature (35 degree Celsius), the person will be allowedinside any public place;otherwise, the access will be refused, and then the buzzing system has been used to warn the authorities by producing an alarm sound, and then the area will be instantly sterilized. Thus, by sensing through the sensors, the proposed system assists in limiting the direct transmission of COVID-19 and the fast spreading condition during pandemic scenarios. © 2022 IEEE.
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 serious problem faced by the world now is the pandemic caused by Covid-19. Currently, body temperature and olfactory screening are still important, especially for safety in closed rooms. This study aims to design and build a simple prototype of an automatic door integrated with contactless temperature and olfactory detection devices. This device consists of an MLX 90614 sensor as a temperature detector, an IR FC-51 sensor as a fragrance object detector, an Arduino UNO as a processor, and an LED display that displays instructions and screening results. The door automatically opens if the temperature and smell are normal and vice versa. The implementation results of this prototype provide the best detection distance of up to 2.5 centimeters with 5% error. While the FC-51 sensor is able to detect up to a distance of 5 centimeters. The performance of the MLX 90614 sensor in detecting temperatures is not significantly different from the detection results of the GXGOI thermogun, which is only around 0.17 degrees Celsius. In general, the entire device part works as expected with 100% accuracy. This simple prototype is expected to inspire screening techniques to prevent the spread of Covid19 in closed rooms. © 2022 IEEE.
ABSTRACT
Face mask and body temperature detection is necessary for current pandemic period. Detecting face mask and body temperature helps in decreasing or to avoid spreading of COVID cases especially in crowded areas. The main purpose of face mask recognition and temperature prediction system is to find whether a person is wearing a mask or not and to check the body temperature. With the help of deep neural network based Convolution Neural Network algorithm, face mask has been recognized. For body temperature, LM35 temperature sensor is used. This system undergoes data pre-processing, training, detecting face mask and temperature. By using MobileNet V2, Frontcascadexml file, tensor flow and keras software library the face mask is detected. Then, the result is send to the Arduino microcontroller and displays that the face mask is detected or not by using LED. If the mask is not weared by the person, buzzer will be alarmed. Similar procedure was carried out for monitoring the temperature of a person using LM35 temperature sensor. The main advantages of MobileNet V2 are higher performance, lesser network size and minimum number of parameter are required. © 2022 IEEE.
ABSTRACT
Nowadays, the COVID-19 pandemic has changed our lives. Some biosecurity measures have been implemented, including the use of face masks and the detection of body temperature;however, there are a lot of outbreaks, and the world cannot overcome this illness. In multiple cases, it is very difficult to measure the temperature and verify the correct use of face masks in everyone. Therefore, this paper proposes a real-time access control system based on body temperature detection and the correct use of face masks. This system uses a Raspberry Pi 4, which integrates temperature measurement using a thermal imager, the detection of the correct use of face masks using Convolutional Neural Networks (CNN), with a model built based on TensorFlow and MobileNetV2 that works on the video obtained from a thermographic camera using OpenCV and the Real Real-Time Streaming Protocol (RSTP). The system includes four modules: body temperature detection, processes, access, and visual interface. As a result, the access control system establishes six classification cases: high temperature and low temperature in faces without a mask, with an incorrectly placed mask, and with a correctly placed mask. The results show a system performance greater than 95% in all cases with a neural network model trained with a learning rate of 10E-4 and 15 epochs. © 2022 IEEE.
ABSTRACT
For corporate and private groups, providing security and secure access to workplaces has long been a top priority. From keypads to fingerprint sensors, there have been advancements in the way security is delivered over the years. Even these, though, have their flaws and weaknesses. Computer Vision is a more powerful and modern technique which can be integrated into a security system for the purpose of increasing the overall level of security. This project aims to create a security system that utilizes this software as well as a temperature sensing module to enable secure, monitored and contact-less, access. The facial authentication is achieved with a help of a webcam connected to the system and a python program on which this is executed, after which the main control is transferred to the Arduino UNO Microcontroller board which tests the two incoming inputs and provides access based on its decision. A training model is employed which studies the given images of the users and detects them when entry is requested. © 2022 IEEE.
ABSTRACT
In the advent of a global pandemic, the necessity for early COVID-19 suspect detection and quarantine is of paramount importance. Medical research indicates that a high fever provides a general litmus of whether or not a person is infected with Coronavirus. Among several available solutions, thermal imaging has proven to be a better contactless screening procedure. It enables fast and easy detection of fever from a reasonable distance. In this research, a solution named Thermique is proposed. It is a cheap, easy to mass-produce, and automated AI-enabled thermal screening platform that combines facial detection, instant contactless temperature scanning, and RFID logging, while also providing an integrated defense against the spread of COVID-19 in a particular facility. Consisting of only off-the-shelf electronic components, this solution can be implemented with a significantly minimized cost, compared to its similar-function providing alternatives available on the market. To design and implement Thermique, a system architecture was developed for the platform, the details of which are highlighted within this paper. After the development of the prototype, several analytical evaluations of the system have been conducted, including the system's performance, and overall usability. © 2022 ACM.
ABSTRACT
Coronavirus is a novel virus that is responsible for causing the disease, COVID-19, which is deadly. It was first detected in December 2019, in the city of Wuhan, China and due to its contagious nature, people all over the wor1d are now infected. COVID spreads very fast and easily through air, from one person to another, affecting almost the entire population of the world in a short span. Wearing a mask in public is very much necessary as a means of preventive measure against the viral disease. Moreover, body temperature is an important factor to be identified to determine whether an individual is affected from the virus. Manually checking if a person wears a mask in outdoors or determining the temperature of an individual in a crowded area, is a tedious task and requires an urgent need for solution. Internet technology introduction into the world is beneficial and it can transmit the data without any human interaction which is best suited for this Covid-19 situation. This article provides the road map to how this technology can be utilized for a better cause. In this work, an IoT based framework is designed to ensure the restriction of entry of a Covid affected individual into the premise, by detecting if the mask is worn and his temperature is normal, to avoid the spread of this disease. Moreover, using this method the safety of the staff in the checking process at the entry point is protected. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
During the Covid-19 pandemic, teaching and learning activities were carried out virtually. It has been running for more than one year. When the trend of Covid-19 cases decreased, onsite learning began to be trialed by implementing strict health protocols. One of the important parameters for the first screening is body temperature because 99% of Covid-19 patients have fever. Therefore, a student temperature measurement mechanism is needed before entering the school area. A number of temperature detectors should be located to prevent queues. A distributed real-time monitoring system as well as data records are required for daily evaluations. Therefore, in this study, a distributed system for measuring body temperature was designed and implemented with data recording. This system runs online real-time on an internet network client server application. This system consists of four temperature detectors connected to a mini-computer as data control and an access point to a dedicated network. All sensor nodes can send data simultaneously. A web server application is provided for data storage and access to the client. From testing the proposed system, it is known that the system can send real-time data with a delay of <150 ms on several measurements and other measurements >150 ms because it really depends on the quality of internet service. The application can run an alarm function if it finds a temperature exceeding the threshold. This system has been implemented in one of a private school in the city of Bandung. With this system, it is hoped that it can support onsite learning activities in schools. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.
Subject(s)
Biosensing Techniques , COVID-19 , Pedestrians , COVID-19/epidemiology , COVID-19/prevention & control , Crowding , Humans , Pandemics/prevention & controlABSTRACT
Since the outbreak of COVID-19 pandemic, enormous development has been done in computer vision systems in face masks and temperature detection. To enforce the mandate for wearing the mask in public places, the authors have suggested a solution using transfer learning (MobileNetV2) architecture as a foundation for image classification. The proposed solution is embedded devices with Raspberry Pi4 and python packages viz TensorFlow, OpenCV, and Keras. In this Work, a combination of transfer learning and IoT has been worked out using live streaming to detect an appropriate use of face masks along with body temperature. This technique includes the Single Shot Single box and Multi-box identification of the face mask. Further, a person can check the Temperature at the entrance. This temperature sensor is in noncontact mode, which senses a temperature from 2 cm to 5 cm and is displayed through LCD. It sends the data to the cloud for visualization and analysis, and an alert message is sent in a real-time environment based on that analysis.
ABSTRACT
Corona virus is a pandemic that has hit globally (COVID-19) and has affected every country. All countries in the world are taking precautionary measures to suppress the spread of this case. Each work unit or business in each country must comply with the policies of the respective state authorities. For this reason, each business unit must implement a fairly strict health protocol for both employees and customers who will enter the office or business area. This is to ensure health protocols by screening employees or customers who will enter the business area. The indicator of measuring the health condition of employees is by knowing whether the employee's body temperature is in accordance with the permitted threshold. Every employee who will enter will be selected with this tool, and if the body temperature exceeds the threshold, then they are not allowed to enter the business area. An internet of things based electronic equipment is designed to help business units implement health protocols. Using ESP32 as a microcontroller and infrared temperature measurement as a marker to be able to decide whether someone is sick or not © 2021 IEEE.
ABSTRACT
Social distancing is the one of the preferred ways to get out of the current situation and it has become difficult to follow it. Most people use public transport as a medium to travel. It becomes very difficult to maintain an effective social distance in public transports. So, we have planned to give a solution to address this problem by ensuring safeness in buses. Here, we kept a fixed limit for persons entering into the bus and before that they had to pass the temperature check and mask detection checks. The device consists of a Raspberry pi Module which acts as a system, a Camera module, PIR Sensors, Infrared Temperature Sensors, LCD Monitor. LCD Monitor shows the details of the number of persons that can be accommodated in a particular bus and the number of people present inside the bus. Temperature sensors, such as infrared temperature sensors, are used to detect the temperature of people entering the bus. It is captured and determined whether or not the person is wearing their mask in the entrance by the Camera Module. PIR Sensors are used to monitor the count of the persons entering or exiting the bus which is displayed in the LCD monitor. So, these work together and make the entry screening while entering a transport system. © 2022 IEEE.
ABSTRACT
Today our whole world is entangled with the most dreadful disease Corona which is caused by the successor of SARS known as SARS-Cov-2 virus. Coronavirus is the influenza-like respiratory disease causing damage to the respiratory system of the humans through the ACE2 receptors which acts as an entry gate for the virus to enter. The Corona virus was identified in late 2019 in the city of Wuhan, China which later spread to the most of the territories in China. The spread was first identified by the Bluedot which is a Saas service designed to track and detect the spread of infectious disease. When the other countries came to know the severity of the virus they made various steps to prevent the spread of the virus. The initial symptoms of coronavirus are rise in temperature, loss of taste and smell and short breathness. As the entry level check many institutions and offices, checks the body temperature of the people and checks whether the person is wearing a mask or not. To make this process fully automatic without human intervention the use of AI enabled IR camera sensor with the Arduino UNO is made. The detection of temperature can be made possible by the use of the computer leveraging vision techniques which is equipped with the Raspberry-pi camera module. The process is based on the thermal imaging of the person which can detect the elevated temperature of the person and prevents them from entering into the institution or offices thereby the spread due to the possibly affected persons can be avoided thereby the spread can be controlled. The system not only identifies the person with high temperature but also checks whether the person is wearing a mask or not. The real time analysis of the system is the major advantage of the proposed system.