Smart Home Application for Challenged Along with Human Temperature Detector

A smart home is a component of the Internet of Things (IoT) technology implementations that help people with their daily activities. To link devices to the Internet of Things, a variety of communication methods can be used. Impairments restrict the activities that disabled people can participate in. This paper proposes an automation system that enables disabled people to control televisions (TVs), lights, and fans, any other electrical devices at home, using just voice commands without moving. The Google Assistant feature for mobile phones is used to achieve voice recognition on electronic components. This system also contains the concept of human temperature measurement where the temperature sensor, fixed to the door, checks the temperature of the person and opens when it is normal. This prevents the user from getting infected by the illness, keeping in mind the present situation of covid19. © 2023 IEEE.

Preliminary Investigation on Wrist Temperature Control by Wearable Peltier Armband

Due to COVID-19 pandemic, body temperature measurement in commercial facilities is performed using a non-contact method. However, if the human body can be controlled in some way to disguise body temperature, a thermometer would have difficulty detecting an entrant with a fever. In this study, we propose a method to control body temperature measured at the wrist in order to demonstrate the vulnerability of temperature measurement at the wrist. Our device lowers body temperature by cooling the upper arm, thereby cooling blood flow and reducing the intensity of infrared radiation detected by a thermometer. The implemented device was used to cool the upper arm under three different conditions. The results showed that cooling the blood flow in the upper arm can lower the body temperature at the wrist. The cooled body temperature was difficult to maintain after the end of cooling, irrespective of the cooling intensity and cooling time. © 2022 ACM.

A Traceable Spectral Radiation Model of Radiation Thermometry

Featured ApplicationRadiation thermometry of real objects under real conditions.Despite great technical capabilities, the theory of non-contact temperature measurement is usually not fully applicable to the use of measuring instruments in practice. While black body calibrations and black body radiation thermometry (BBRT) are in practice well established and easy to accomplish, this calibration protocol is never fully applicable to measurements of real objects under real conditions. Currently, the best approximation to real-world radiation thermometry is grey body radiation thermometry (GBRT), which is supported by most measuring instruments to date. Nevertheless, the metrological requirements necessitate traceability;therefore, real body radiation thermometry (RBRT) method is required for temperature measurements of real bodies. This article documents the current state of temperature calculation algorithms for radiation thermometers and the creation of a traceable model for radiation thermometry of real bodies that uses an inverse model of the system of measurement to compensate for the loss of data caused by spectral integration, which occurs when thermal radiation is absorbed on the active surface of the sensor. To solve this problem, a hybrid model is proposed in which the spectral input parameters are converted to scalar inputs of a traditional scalar inverse model for GBRT. The method for calculating effective parameters, which corresponds to a system of measurement, is proposed and verified with the theoretical simulation model of non-contact thermometry. The sum of effective instrumental parameters is presented for different temperatures to show that the rule of GBRT, according to which the sum of instrumental emissivity and instrumental reflectivity is equal to 1, does not apply to RBRT. Using the derived models of radiation thermometry, the uncertainty of radiation thermometry due to the uncertainty of spectral emissivity was analysed by simulated worst-case measurements through temperature ranges of various radiation thermometers. This newly developed model for RBRT with known uncertainty of measurement enables traceable measurements using radiation thermometry under any conditions.

Research on two-dimensional code scanning and automatic recording device for human body temperature measurement based on stm32

With the outbreak and normal development of COVID-19, the effective detection and recording of body temperature has become a new focus of our attention. At present, there is no complete system to measure temperature, automatic record and specific information at home and abroad. To this end, combined with professional knowledge, our team designed a two-dimensional code scanning and human body temperature automatic recording device with STM32F1 as the core. The device STM32F1 development board is the main control chip. By connecting the WIFI module through the serial port, STM32F1 uses the function of wireless communication. Through the communication protocol, the link between the router and the ESC cloud server of Ali Cloud is utilized. The router or mobile data is transmitted to the user side (APP, applets) according to the specified communication protocol. Inside the development board, the code of each part is written to complete the device integrating code scanning and temperature measurement, which can be displayed and alarm through the node (OLED display screen). This will play a good role in preventing the spread of COVID-19. The system can be used in hospitals, communities, railway stations, shopping malls and many other public places. © 2022 IEEE.

Real-time Face Mask Detection and Contactless Body Temperature Measurement on Edge Device Using Deep Learning

The world is severely affected by COVID-19 disease and it has became a threat to everyone. One of the effective methods to prevent infection of this disease is to wear a face mask in public places. The body temperature of a person is an important indicator of COVID-19 infection. Many public places or services give entry to the people only if they wear a mask and have body temperature in a normal range. In areas like college labs, internet cafes and malls, they keep a daily log of visiting persons with details such as name, date, body temperature etc. In this work, a system is proposed that can be utilized to remind people to wear a face mask and monitor them. It can also measure body temperature using an IR temperature sensor and alert respective authorities if it is high. In the proposed system, convolutional neural network MobileNetV2 is used for face mask detection deployed on NVIDIA Jetson Nano. © 2022 IEEE.

Surveillance Rover to Maintain Social Distancing in Crowded Areas

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.

Implementation of PRESENT Algorithm on Contactless Access Control using Raspberry Pi

This research develops a contactless and secure access control system based on face recognition and body temperature measurement. This research aims to establish a security system that also fulfills health protocols for COVID-19 spreading, in this case, the limitation of physical contact. The PRESENT algorithm, a lightweight block cipher encryption-decryption algorithm, is implemented to keep the transmitted data safe. The face recognition method consists of the Viola-Jones face detection algorithm and LBPH face recognition algorithm. The body temperature is measured using a contactless sensor. The performance tests show the accuracies of recognizing faces are 68% under 198 Lux lighting and 52% under 105 Lux lighting. The precision of measuring body temperature using the sensor reaches 98,85%. Based on the sniffing attack test of the system, the encrypted data transmitted from the system to the web-based database is safe from attackers. Besides the face spoofing attack tests, the system will not authenticate attackers with face photos or face videos. © 2022 IEEE.

Measuring the Distance Body Temperature in Real-Time based on the Recursive Least Square Estimation

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.

X-Linked Inhibitor of Apoptosis (XIAP) deficiency presenting as hemophagocytic lymphohistiocytosis triggered by drug allergy

X-linked inhibitor of apoptosis (XIAP) deficiency is a primary immunodeficiency associated with recurrent hemophagocytic lymphohistiocytosis (HLH) episodes. The clinical phenotypes of XIAP deficiency vary, ranging from splenomegaly to life-threatening inflammation. We report a case of XIAP deficiency with unusual late-onset HLH presentation likely triggered by a drug allergy. A previously healthy adolescent boy presented to the hospital with fever and rash seven days after starting antibiotics for a neck abscess. Laboratory evaluation demonstrated cytopenias, elevated liver enzymes, and increased inflammatory markers. Initially, antibiotics were discontinued due to concern for drug rash. He continued to deteriorate clinically and became hypotensive. Additional testing revealed decreased NK cell function, as well as elevated ferritin, triglycerides, and soluble IL-2 receptor. SLAM-Associated Protein (SAP) and XIAP evaluation by flow cytometry demonstrated decreased XIAP expression. Subsequently, genetic testing revealed a known pathogenic mutation in BIRC4 (c.421_422del), confirming the diagnosis of XIAP deficiency.Copyright © 2023

COVID-19 Automatic Indoor Safety Control through Mask, Temperature and Distance Monitoring

The COVID-19 pandemic has affected a variety of aspects of our everyday life. Most activities like entertainment, healthcare, education and businesses have been reshaped due to the safety guidelines. Proper monitoring in indoor areas is essential to limit the spread of COVID-19. This paper presents a low-cost prototype system that addresses the indoor safety issue by combining a mask detector and temperature measurement system with a smart wearable band which alerts people to maintain social distance in close vicinity. The focus is on ensuring safe distance, wearing a mask, and no entry for people with high temperatures. Firstly, the mask and temperature system has an Arduino NANO that works as the primary device. The Arduino is connected with an ESP32-Cam that sends the image to a client where we have trained and developed a machine learning model using thousands of masked and unmasked pictures. Following, the model uses an image classification algorithm with the tensorflow.js model and gives us the result with an accuracy percentage. Secondly, the temperature is measured with the help of an MLX90614 non-contact sensor. The temperature of a person is also shown on the monitor at of. Finally, a wearable device is presented with a NodeMCU 8266 Wi-Fi module. It uses Received Signal Strength Indicator (RSSI) value to detect another similar device and alerts through a vibrator and buzzer if the social distance rules are violated. We evaluated the system in real-life scenarios, and the mask detection system gives an average accuracy of 98.7%. We have presented an in-depth analysis of the Mask Detection System, showing different mask types, the accuracy of the machine learning algorithm, temperature measurements and results. Similarly, the distance measurement system is presented with several factors. © 2022 IEEE.

Research on 5.5 μm Infrared Filter Applied to Infrared Thermometer

With the expansion of novel coronavirus pneumonia's influence on the world, people's dependence on infrared thermometer guns is increasing. In order to improve the measurement accuracy of the infrared temperature measuring gun and meet the requirements of rapid and accurate measurement of human body temperature, the core components for the infrared temperature measuring gun are developed and prepared in this paper. The film fogging phenomenon caused by the anisotropy of metal germanium and semiconductor properties is analysed and solved by measuring the atomic force microscope image and infrared spectrum of the film, the 5.5-micron infrared filter with high transmittance and good film quality was prepared by electron beam evaporation, resistance evaporation and ion source assisted deposition. © 2022 IEEE.

A System Design with Deep Learning and IoT to Ensure Education Continuity for Post-COVID

In this paper, an IoT and deep learning-based comprehensive study to reduce the effects of COVID-19 on the education system is presented. The proposed system consists of an edge device, IoT nodes, and a neural network that runs on a server. The purpose of the proposed system is to protect students and staff against infectious diseases and increase the students performance during classes by monitoring the environmental conditions via an IoT-based sensor network, during the current pandemic to ensure the use of masks in closed areas by training a customized deep learning model, and to monitor the student attendance data by deep learning and IoT-based solution. Furthermore, effective heating and cooling can be done to save energy by transmitting the environmental conditions of the indoor environment to the relevant destinations. The experiment is conducted with five different networks to classify the faces in the images as masked or unmasked, and their performances were examined. The networks were trained on the Face Mask Detection Dataset which contains a total of 7553 masked and unmasked images. The best results were obtained as 99.5% for the F1 Score and 99% for MCC by the model trained on the InceptionV3 network. IEEE

Robotic Arm Aided Thermal Screening System

With the onset of the Covid-19 pandemic, the health of people has become more of a concern. With this, temperature measurement has gained even more significance. Non-contact thermometers give the advantage of being used in extreme infectious environments, lightweight, repeatability, and many more. Thermal screening helps in identifying people with a high body temperature who are potentially at risk. This research work focuses on the non-contact human body temperature measurement with the assistance of a robotic arm. The robotic arm is used to dispense the power of mobility to the system. The robotic arm, interfaced with Raspberry PI, is used to dispense the power of mobility to the system. Non-contact infrared temperature sensor, MLX90614, is interfaced with Arduino Nano and is used to measure human body temperature. The temperature obtained from the thermal gun is fed to the serial monitor app in the mobile that is connected through the USB cable to Arduino Nano. The temperature sensor's data is displayed on a mobile phone in Celsius unit. The format in which the sensor data is displayed is programmed using Arduino IDE. © 2022 IEEE

Automatic Hand Sanitizer and Body Temperature Measurement System with Low Liquid Hand Sanitizer Condition Notification

Coronavirus disease (Covid-19) still exists, but the implementation of health protocols, namely maintaining distance, wearing masks, and washing hands (3M) is not optimal and has received more attention lately. Previous studies applied the IoT concepts to hand sanitizer systems and automatic body temperature measurements. However, this research has not been optimal for system integration in solving problems. Existing research only focuses on temperature sensor readings which can be monitored in real-time by installing third-party applications first. Therefore, we make an automatic hand sanitizer and body temperature measurement system equipped with low liquid hand sanitizer condition notification by implementing modifications using PHP MySQL website and notification feature via Twilio bot integrated with WhatsApp. The feature is based on previous research proposals. Based on the test results, the performance of the system prototype is considered good. The system prototype can produce an average of 1.1 mL as the recommended average hand sanitizer in one use of less than 1.74 mL. Then the system prototype has a hand sanitizer fluid accuracy of 90.4% and an accuracy of MLX90614 temperature sensor of 99.9%. © 2022 IEEE.

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.

A Deep Learning-Based Tool for Face Mask Detection and Body Temperature Measurement

Due to the COVID-19 pandemic outbreak, wearing a mask and ensuring normal body temperature in overcrowded areas such as workplaces have become obligatory. In this paper, a deep learning-based tool for automatic mask detection and temperature measurement at the entrance of workplaces was developed to save costs of manual supervision and reduce human contact for safety concerns. Using Python, image/video processing techniques related to face and object detection are used to process image input from a webcam. A deep learning algorithm called MobileNetV2 was used to build the face mask detector model. Moreover, a non-contact thermal sensor, the MLX90614, along with Arduino, was employed to measure body temperature. The mask detection and temperature measurements are displayed correctly on a Graphical User Interface (GUI). Besides, an additional function related to the Internet of Things (IoT) was implemented, which sends high-temperature alerts to smartphones. It has been verified that the model can achieve an accuracy of about 98%. The developed system experiences a limitation when other objects are used to cover the mouth and nose in that they may still be classified as masks. However, compared to the mask detection systems available commercially, it can provide correct detection results when using the hand to pretend to be wearing a mask. © 2022 IEEE.

Dynamic Group Difference Coding Based on Thermal Infrared Face Image for Fever Screening

Recently, noncontact temperature measurement methods based on infrared face perception have received widely attentions since fever screening plays an important role in the early prediction of respiratory infections, such as SARS, H1N1, and COVID-19. However, the performance of these methods always significantly degrades when facing the changes of environment. Thus, the majority of these methods leverage the block-body and sensors to reduce the influence of environment changes. It is a pity that the increased instrument complexity leads to higher costs and failure rate. To address the aforementioned issues, this article presents a novel fever screening method, named dynamic group difference coding (DGDC), which is based on the analysis about the influencing factors. The key idea of DGDC is to compute the temperature differences between the target person and the recently passed crowd (dynamic group). Specifically, we develop the face temperature encoder (FTE) to describe the face temperature and thus construct the difference matrix of the embedding feature between the target person and the dynamic group. Multilayer perceptions (MLP) are employed to capture the intrinsic information by characterizing the difference matrix in vertical and horizontal directions, respectively. Finally, we provide a dataset of thermal infrared face (TIF) images and conduct extensive experiments to demonstrate the advantages of the proposed method over the competing methods. © 1963-2012 IEEE.

An Interdisciplinary Design Project for Undergraduate Engineering Training - Portable Real-Time Polymerase Chain Reaction System

Our world has been permanently changed by the pandemic outbreak of COVID-19 starts around the end of 2019. In the first few months of 2020, the whole world was in urgent need of an effective, easy, and quick method for the identification of the infection of the new virus. Polymerase Chain Reaction (PCR) machine, which can test DNA samples by rapidly making millions of copies of a specific DNA sample through the PCR process, including the COVID-19 virus, can perfectly fit this demand. In this study, a design project on PCR is introduced for undergraduate education in electrical and mechanical engineering. The objective of this project is to develop a low-cost, ease-of-use, wallet-size, portable real-time PCR (RT-PCR) machine for accurate testing of various bacteria or viruses. The key function of the PT-PCR system is to precisely control and maintain the temperature of the bio-sample solution within a range between 55℃ and 95℃. The RT-PCR system is centrally controlled by a microcontroller Raspberry Pi 3. It receives temperature measurements from thermistors and operates the heating lid, the thermoelectric module, and the cooling fan to regulate the temperatures required in repetitive thermal cycles. This project provides students opportunities in studying and practicing a wide range of engineering technics and skills, including mechanical design, electronics design, microcomputer programming, system control, power electronics, sensors and actuators, data acquisition and processing, cellphone app development. Students can gain comprehensive understanding of the design of multiphysics system after they overcome various challenges emerging in the project. From the view of engineering education, the process of this project development has demonstrated the importance and benefits of adopting complex interdisciplinary engineering problems for student teams to solve, especially those involve contemporary issues. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.

YOLO Based Thermal Screening Using Artificial Intelligence (Al) for Instinctive Human Facial Detection

Covid19 remains the world's greatest public health emergency. It has become indispensable to measure the temperature of people entering or leaving croweded places to ease the identification of potentially infected and to isolate them from spreading and preventing the spread of the ongoing global pandemic of coronavirus disease. This research work is focusing on thermal screening for an automated scanner using Artificial Intelligence (AI) for instinctive temperature measurement on human faces. The framework used for facial detection is known as YOLOv5 which is a family of compound-scaled object detection models trained on the COCO, a large-scale object detection, segmentation, and captioning dataset. YOLOv5 is able to detect several different objects simultaneously by using its available pre-trained models and robustness of detecting faces even at the vicinity of face masks. The research presents the application, training procedure and capability of the Yolov5. This system is not only used for the human face detection, but also for the detection of some commonly-used objects as an extension to its overall application and performance. Yolov5 is readily available to be implemented in Python, the core programming language working under an Ubuntu-based Operating System providing users the best experience. One of the important outcomes of this research work is the development of a Graphical User Interface (GUI) to work alongside the main programme flow. © 2022 IEEE.

Dynamic Group Difference Coding Based on Thermal Infrared Face Image for Fever Screening

Recently, noncontact temperature measurement methods based on infrared face perception have received widely attentions since fever screening plays an important role in the early prediction of respiratory infections, such as SARS, H1N1, and COVID-19. However, the performance of these methods always significantly degrades when facing the changes of environment. Thus, the majority of these methods leverage the block-body and sensors to reduce the influence of environment changes. It is a pity that the increased instrument complexity leads to higher costs and failure rate. To address the aforementioned issues, this article presents a novel fever screening method, named dynamic group difference coding (DGDC), which is based on the analysis about the influencing factors. The key idea of DGDC is to compute the temperature differences between the target person and the recently passed crowd (dynamic group). Specifically, we develop the face temperature encoder (FTE) to describe the face temperature and thus construct the difference matrix of the embedding feature between the target person and the dynamic group. Multilayer perceptions (MLP) are employed to capture the intrinsic information by characterizing the difference matrix in vertical and horizontal directions, respectively. Finally, we provide a dataset of thermal infrared face (TIF) images and conduct extensive experiments to demonstrate the advantages of the proposed method over the competing methods.