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 Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.
ABSTRACT
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.
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
The Covid-19 pandemic that hit us in 2020 changed our lifestyle in every way. There was tremendous damage to people's lives. It is now predicted that other variants of Coronavirus are affecting people's health throughout the world. We must remain vigilant against upcoming dangers. The Indian health ministry has also advised people to take the necessary precautions. In this paper, we will focus on automating temperature and oxygen monitoring using the Internet of Things. According to our proposed model, data generated by the temperature sensor (MLX90614) and oxygen saturation sensor (MAX30102) will be stored in a relational database. Using this data, future data analyses can be conducted. We are also going to visualize the data by building an interactive dashboard using Power BI. Overall, health monitoring will become much more convenient and speedier. © 2023 IEEE.
ABSTRACT
Coronavirus illness 2019 has had a major impact on the entire world over the past two to three years. One important approach for people's protection is to wear masks in public. Furthermore, putting on a mask properly Many public service providers demand that users only utilise the service while properly wearing masks. Only a small number of studies have examined face mask identification using image analysis, nevertheless. We suggest Face Mask, a highly accurate and practical face mask detector, in this study. The suggested Face Mask is a one-stage detector that combines a novel context attention module for detecting face masks with a feature pyramid network to fuse high-level semantic information with various feature maps. We also provide a brand-new cross-class object removal method to reject and predictions with a high intersection of union and low confidence. Additionally, we investigate the viability of integrating Face Mask with a portable or embedded neural network called MobileNet. By utilising1)Contactless temperature sensing,2)we create a fack mask detection alarm system to boost COVID-19 indoor safety.Infrared sensor and contactless temperature sensing subsystems rely on Arduino Uno, while computer vision algorithms are used for mask identification. © 2023 IEEE.
ABSTRACT
The highly contagious COVID'19 virus's extensive distribution caused the pandemic, which intensified the importance of personal cleanliness and health. Wearing a protective face mask, keeping a certain physical distance, and regularly washing your hands with soap or hand sanitizer are a few precautions you may take to stay safe during this pandemic. An automatic touchless temperature-monitoring doorbell can provide guarded and touch-free temperature sensing, thus informing the household members. The widespread usage of outdated touch-type doorbells may result in the transmission of the coronavirus. The aforementioned article describes a novel approach to creating a Novel Doorbell system that can be activated using gestures and simultaneously detects the person's temperature and notifies the home of a suspected infectious disease. © 2023 IEEE.
ABSTRACT
In this project the system has been designed in the manner to detect the body temperature and oxygen saturation level (SpO2) of the person. To detect the body temperature and oxygen pulses we are using the respective sensors. The sensors we are using in this project is MLX90614 ESF thermometer and MAX30100 is integrated with the pulse oximeter. Here we have used the two Node MCU (ESP 32) for the different sensors, one Node MCU is connected to the temperature sensor and another Node MCU is connected to the pulse oximeter. The components we are using in this project is Eco friendly. The software we are using in this project is ARDUINO UNO R3 IDE (Integrated development board) and To show the results we are making use of Blynk app. The maximum errors we get in this project is about 2%. When it comes to health monitoring, it has shown the good results. This is the best way to minimize the spread of the COVID-19. It is a low cost and high functionality which makes it use in the different places like Hospitals, Malls, Sports etc. © 2022 IEEE.
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
Up till now many lives were lost due to various contagious s like swine flu, encephalitis, etc. Also, the whole world has faced the COVID-19 pandemic situation recently. These situations led to the thought of designing such a system that can be installed at the entry and exit gate of a mall to reduce the spread of contagious s. The proposed work is created as a prototype to not only detect the face mask and sense the body temperature but also controlling the crowd inside a mall to maintain social distancing. Additionally, the system is strengthened by a sanitization module after entering to the mall. It is a single integrated solution to all the challenges consisting of all these four modules. Apart from malls, this system can be deployed in places like shops, banks, etc. Once the person is detected at the entry gate, the count of people inside the mall is checked to maintain the social distancing norms. If found a less number of people are inside, it checks for a face mask and body temperature. When all three conditions are satisfied, it opens the door and activates the sanitization module. This system uses a Convolution Neural Network (CNN) for face detection, the MLX90614 temperature sensor for temperature sensing, and a servo motor for door control. All the modules are integrated with Raspberry-pi. The system is tested for all possible conditions and results are produced. It can be customized depending on the area of a mall or the shop and the size of the door. © 2022 IEEE.
ABSTRACT
We are proposing an IoT-based social distancing device as a preventive measure to COVID-19. It uses NodeMCU in conjunction with ultrasonic sensor temperature sensor, while vibrator buzzer is used for an alarming mechanism. The ultrasonic sensor is used to obtain higher accuracy as it uses LOS principle to measure the distance. The alarm will be raised whenever measured distance is found to be less than six feet. Temperature sensor is used to alert the user to isolate them if their body temperature goes above 102 °F, thereby decreasing the transmission possibility of virus in case he is infected with the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
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
ABSTRACT
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
ABSTRACT
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.
ABSTRACT
Amid and post-COVID-19 pandemic, the matter of being in touch with patients to monitor their health matrices became somewhat challenging, especially in the rural areas of countries like Bangladesh and for elderlies. To address this issue, a patient health monitoring system is developed using a Programmable Intelligent Computer (PIC) microcontroller and Global System for Mobile Communications (GSM) protocol with the help of a pulse sensor, IR sensor, photodiodes, temperature sensor, etc., to measure 3 (three) crucial health matrices such as heartbeat/pulse, oxygen saturation level, and body temperature from a fingertip of the patient in 20 s remotely. Whenever the system measures the health matrices, it sends a short message service (SMS) report to a personal caretaker over GSM automatically. If the system finds any anomaly based on predefined threshold levels for each health parameter, it sends a SMS alert report to the designated doctor automatically as well. A prototype of the developed system is made, verified, and tested to be working perfectly as designed and programmed. In the experiment with the developed system, heart rate ranged from 61 to 105 bmp, body temperature ranged from 95.3 to 99.1 ℉, and oxygen saturation was minimum at 97%. According to the set threshold levels, which led to an automatic SMS alert to the caretaker's mobile phone. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
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.
ABSTRACT
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.
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
In today's environment, wearing a mask is required because the coronavirus has infected individuals all over the world. It is a lethal virus that kills everyone. Many restrictions, such as social separation and lockdown, have been enacted by our governments. One of the first signs is a rise in body temperature. It would be impossible to check everyone's temperature in public locations because there is a risk of spreading when people come into contact with each other. This can be achieved using an automated temperature sensor to check the intruder's body temperature. The major goal of this system is to detect and compare whether the individuals are wearing masks and also whether their body temperature is normal. The temperature is measured using a contactless temperature sensor, the MLX90614. If the model detects a high temperature or a person without a mask, the buzzer will sound, and the LED will turn red, indicating that the person is experiencing COVID symptoms. Otherwise, the door will be left open to allow the individual into the room. And the LCD will show all the collected data, such as the exact body temperature, whether it is normal or abnormal, and so on. The LCD provides users with a simple interface. The LEDs are used to provide an indication. To make sensor calibration and debugging easier, the proposed system will be divided into two subsystems: a contactless temperature scanner system and a MATLAB camera system. Individual circuits are designed and tested before being combined to make the final functional prototype. © 2022 IEEE.
ABSTRACT
Covid-19 is a highly contagious disease. Prevention needs to be done to avoid interaction between humans. One of the preventions is checking body temperature. However, a body temperature checker with contact is very risky. Currently, IoT can be applied as a body temperature checking device using the MQTT protocol. However, the MQTT protocol is dependent on the broker, so if the broker is interrupted, it cannot publish information and customers cannot subscribe to topics so that information from censors cannot be received. To overcome this problem, a failover mechanism is needed. In this paper, we develop an Internet of Things (IoT) based automated and contactless body temperature monitoring tool using the MQTT and failover protocols. The failover method is used to support the availability of the MQTT broker. This system is based on a NodeMCU ESP8266 microcontroller with MLX96014 contactless temperature sensor, HC-SR04 Ultrasonic sensor, and Buzzer. The monitoring system is built using Node-Red and stores data using a MySQL database. The test results show the availability of the value reaches a value of 99.96%. Meanwhile, QoS shows that a broker with failover is better than a broker without failover. © 2022 IEEE.