ABSTRACT
Attendance marking in a classroom is a tedious and time-consuming task. Due to a large number of students present, there is always a possibility of proxy. In recent times, the task of automatic attendance marking has been extensively addressed via the use of fingerprint-based biometric systems, radio frequency identification tags, etc. However, these RFID systems lack the factor of dependability and due to COVID-19 use of fingerprint-based systems is not advisable. Instead of using these conventional methods, this paper presents an automated contactless attendance system that employs facial recognition to record student attendance and a gesture sensor to activate the camera when needed, thereby consuming minimal power. The resultant data is subsequently stored in Google Spreadsheets, and the reports can be viewed on the webpage. Thus, this work intends to make the attendance marking process contactless, efficient and simple. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
As the COVID-19 pandemic ravaged the planet at a standstill, remote employment seemed inescapable. Still, for some businesses that rely on the on-site presence of employees, this was a lethal blow. As time passed, restrictions got looser and allowed people to strike a balance between on-site and remote work. Thus, tracking people's indoor movements for purposes involving activity inference, security, and contact tracing is more crucial than ever before. This research explores the applicability of (Radio Frequency Identification) RFID contactless smart cards in tracking people's movement within an enclosed establishment by building a proof-of-concept prototype that allows the mentioned purposes. Furthermore, the system underwent multiple test phases to verify that the system meets the functional and non-functional requirements listed to ensure the system's operational success. Consequently, the test results prove that: 1) the system is behaving as intended;2) the system is secure from known high-risk vulnerabilities;and 3) the system satisfies user requirements and standards, thus fulfilling the functional and non-functional requirements for a human-tracking movement system. © 2023 by the authors.
ABSTRACT
The concept of IoT in the current world where speed, accuracy and efficiency are of a high importance, can do wonders if implemented in a structured manner, into a machine, project, hardware, idea which can improve technology. So, IoT has its application in the Events. Events can be of many types and there is a need of man power to handle the events efficiently. People gather in huge numbers if there is a political event, whereas there is limited audience in a cultural show or less people in a marriage function. Any of such events, if handled smartly, can ease the tasks of humans, as well as provide speed and accuracy and ensure proper event management and organization. This project demonstrates a hardware for the entry-exit of people for any event, through the technology of Radio Frequency Identification (RFID), Wireless Fidelity (Wi-Fi), and main heart as ESP 8266 Controller. The software simulation in Cisco Packet Tracer shows a general event organization related to a hotel or government-based area, where different sections are integrated to control and handle the event in a smart way. The use of RFID indicates the contactless operation for monitoring the attendee entry-exit, due to the current COVID-19 protocols. So, such systems are safe and smart to execute. © 2022 IEEE.
ABSTRACT
Considering the public safety in current COVID-19 out-break, an IOT (Internet of Things) based non-contact temperature monitoring system integrated with RFID authentication system with an interactive Android application, and a web-portal to manage users and temperature records has been proposed. Temperature screening has become essential for all the industries, educational institutions, factories and corporate sector. This system is an online real-time non-contact monitoring system with an interactive android application and user-friendly web portal that help end-users to monitor and keep a record of temperature variations of registered users on daily/weekly/monthly basis. The temperature records are saved in a real-time database which is embedded with the user's RFID card information. In case of an alert (high temperature), a notification is sent to the authorized personnel on their cellphones or their desktop systems via web portal. An alarm is also generated immediately on the device (buzzer and blinking LEDs) to indicate high temperature, alerting the nearby security staff. As per the survey and testing of the device under different temperature environments it has been found that the proposed system has an overall accuracy of 99%. © 2022 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
In this study, we develop a system to provide information on the sterilization of baggage carts and arriving passenger baggage to airport (Hereafter referred as arrival baggage) by using ultraviolet (UV) sterilization and information communication technology as border quarantine measures at airports. This system sterilizes arrival baggage and baggage carts by UV irradiation, and allows passengers to easily view the sterilization information recognized by radio frequency indentation technology. This is to provide safety and security not only to passengers, but also to staff, who may come into direct contact with the arrival baggage, of airport, airline, customs, and so on. In addition, the passengers can be provided with baggage tracking information, such as the current location and estimated delivering time of the baggage. This makes it possible to keep social distancing at baggage claims as an infection prevention. Furthermore, we verify the feasibility of the developed system and identify the issues to be addressed for its practical application through a demonstration of proof of concept at Central Japan International Airport. © 2022 International Association of Traffic and Safety Sciences
ABSTRACT
This work revolves around proper handling and monitoring of crowds at big events like concerts and public gatherings. To ensure appropriate management of the crowd at these events, a system is proposed and designed. The system consists of a series of modules namely a RFID based identification system for entry of only registered audience and a blood oxygen level and heart rate measurement unit which utilizes MAX30100 sensor to further check the health conditions. Along with these, an ultrasonic technology-based proximity monitoring unit (HC-SR04 module) is used to ensure the fulfilment of social distancing norms. This multi-module crowd management and monitoring system is tested in real-Time and the results are verified based on physical response as well as with the help of serial monitor values. The modules for this system are initially constructed on Fritzing, then implemented in real-life. The ThingSpeak platform and Arduino IDE are used to store the data and program the micro-controllers (Arduino and NodeMCU) respectively. © 2022 IEEE.
ABSTRACT
Because of coronavirus variants, it is necessary to pay attention to epidemic prevention measures in the cultivation or product packaging processes. In addition to giving customers more peace of mind when using the products, it also ensures that operators wear masks, work clothes and gloves in the work area. This paper constructs an access control system for personnel epidemic prevention monitoring, which uses IoTtalk [1] to connect IoT devices (such as magnetic reed switches, intelligent switches, RFID readers, and RFID wristbands), utilizes RFID for personnel identification, and employs real-time streaming protocol [2] to take the image of IP Cam for YOLOv4 [3] identification program. The identification program detects whether the personnel is indeed wearing the required equipment. If the personnel is not wearing the required device, the detector will trigger a push broadcast system constructed by LINE Notify to inform the operator for processing. Moreover, we developed an emergency entry mechanism;if an emergency happens, the personnel can trigger the emergency door opening by swiping the card multiple times within a specified time. This function allows the person to enter without wearing the required equipment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Due to the ongoing COVID-19 pandemic, the use of filtering facepiece respirators (FFRs) is increasingly widespread. Since the masks' wetness can reduce its filtering capabilities, the World Health Organization advises to replace the FFRs if they become too damp, but currently, there is no practical way to monitor the masks' wetness. A low-cost moisture sensor placed inside the FFRs could discriminate a slightly damp mask from a wet one, which must be replaced. In this letter, a radio frequency identification (RFID) tag exploiting an auto-tuning microchip for humidity sensing is designed and tested during an ordinary working day and a physical exercise. The tag returns about 1 unit of the digital metric every 3 mg of water generated by breathing and sweating, and it can identify excessively wet masks from commonly used ones.
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
There has been a steady and significant growth of the advancement in computer vision systems for face masks and temperature tracking. The World Health Organization introduce strict measures to prevent the spread of the coronavirus disease. This paper attempts to create a highly accurate and real-time approach that can effectively detect non-mask trying to enforce to wear mask in order to contribute to community health. For the purpose of detecting face masks, a hybrid model combining deep and regular machine learning will be utilized. We will use OpenCV to recognize faces in real time from a live feed via the Camera module using a dataset that includes images with and without masks and send the data to the cloud for visualization and further analysis. As a main part of the solution, we proposed embedded system with tools utilizing Python, OpenCV, and Tensor Flow with using computer vision and deep learning. To make it cost efficient, quick, scalable, and effective the whole process for detection of face mask is carried out on Raspberry Pi. This project enables improved control over the information already provided and strongly points out the deployment of our method to stop the local transmission from spreading and decrease the possibility of human coronavirus disease carriers. © 2022 IEEE.
ABSTRACT
Patient misdiagnosis is quite a common occurrence in hospitals around the world. It is a mistake that can happen to anyone at any time, and especially during a pandemic crisis when hospital facilities are overwhelmed with increasing number of patients. This problem could stem from improper patient identification whereby patient files are mislabelled or placed in an incorrect patient dossier. It is the responsibility of a hospital and its employees to guarantee that such mistakes do not occur. With respect to this, near-field communication (NFC) technology, which is a short ranged wireless communication technology, has been identified to have great potential to help identify patients in hospitals.This paper demonstrates a solution by designing and developing a patient healthcare management information system that sees the seamless integration of the NFC technology along with and both web and mobile technologies,to provide a holistic solution to tackle the problem of patient misidentification in a hospital environment especially during pandemics such as COVID-19.The Technology Acceptance Model (TAM) was used as evaluation method in order to quantify the proposed systems's usability and acceptance using 5 constructs. Results showed acceptance of the system with a mean score of 4, indicating that the NFC tag-based mHealth Patient Healthcare Tracking System was found to be useful and easy to use. © 2022 IEEE.
ABSTRACT
COVID-19 has affected the livelihood of millions around the world. Pass-infection of the virus between the personnel is a large threat factor. During this pandemic, it's mandatory to wear a mask to prevent the spread of the COVID19. Biometrics and face detection are commonly used to track individual employees' attendance but face recognition methods are ineffective because wearing mask obscures a portion of the face. This biometric can be a medium for the transmission of viruses. The proposed system implements COVID preventive measures such as mask detection and monitors body temperature. In addition, the proposed system checks for authorized persons using RFID technology and employs fingerprint verification application via individual mobile phones for attendance purposes. The system predominantly inspects presence of face masks, then keeps track of body temperature and ultimately controls the automatic door associated with it using RFID technology and android app based fingerprint recognition to allow access to people with authorization. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has presented social challenges to establish the new normal lifestyle in our daily lives. The goal of this paper is to enable easy and low-cost monitoring of cleaning activity to keep a clean environment for preventing infection. Although human activity recognition has been a hot research topic in pervasive computing, existing schemes have not been optimized for monitoring cleaning activities. To address this issue, this paper provides an initial concept and preliminary experimental results of cleaning activity recognition using accelerometer data and RFID tags. In the proposed scheme, machine learning technologies and short range wireless communication are employed for recognizing the time and place of wiping as an example of cleaning activities, because it is an important activity for shared places to avoid infection. This paper reports the evaluation results on the recognition accuracy using the proof-of-concept (PoC) implementation to clarify the required sampling rate and time-window size for further experiments. Also, a real-time feedback system is implemented to provide the monitoring results for users. The proposed scheme contributes for efficient monitoring of cleaning activities for creating the new normal era. © 2022 IEEE.
ABSTRACT
In this work, we present a UHF-RFID-based noninvasive sensor to measure the concentration of ethanol in water using the volume fraction of liquids in mixture solutions. The sensing system operates at the UHF band (860-928 MHz). The concentration of ethanol in water affects the dielectric properties of the solution and therefore the antenna sensitivity of the RFID tag. This sensor operates by measuring the change in permittivity of a solution because of the change in concentration of ethanol in water. We propose a flexible RFID-Tag sensor a low-cost alternative to identify the possible sensitivity of tag changes and is able to detect a variation of 25% in ethanol in 9 ml of deionized water (DI-Water). The solution is useful in avoiding counterfeit ethanol solutions that may be toxic. The experimental setup is inexpensive, portable, quick, and contactless. We present results for ethanol solutions ranging from 25% to 100% in a small tube container. © 2022 IEEE.
ABSTRACT
An automated temperature scanner with contact-tracing capability had previously been developed to screen temperature related diseases such as COVID-19, Ebola or Lassa fever and trace possible infected persons. The device uses a non-contact temperature sensor (MLX-90614) to acquire human temperature while the user's identity is obtained by means of Radio Frequency Identification card. This information is sent for storage in remote database and made available for possible contact-tracing via a secured web interface. Due to the fact that several studies contest the validity of non-contact temperature sensors as replacement for contact ones, the present study therefore compares performance of its non-contact temperature sensor with that of the mercury-in-glass thermometer considered as a standard in this study. This is in an attempt to validate performance of the developed automated temperature scanner and to optimize its usage. Investigations reveal that the developed device performs best when user is within a 16 cm distance from the temperature sensor. Any measurement done outside this 16 cm critical distance might not be valid. Other investigations reveal that the developed device with non-contact temperature sensor is faster than the contact thermometer with an average response time of 0.004 second compared with mercury-in-glass of 179.2 seconds. So non-contact sensor would be very useful when speed is of essence but it was found to exhibit a lower precision compared to the contact thermometer. The critical temperature obtained in this study will guide users in the usage and researchers in further studies on the developed automated temperature scanner with contact-tracing capability. © 2022 IEEE.
ABSTRACT
Managing employee attendance is a key factor for a large organization since the staff is the main resource for them. In Sri Lanka, currently, there are various methods used to track the attendance of employees, and the traditional manually assigning paper-based signature mechanism, fingerprint method, card strap Radio Frequency Identification (RFID), and biometric-based attendance recording systems such as fingerprint recognition methods are generally used. Most of the systems offer either one-way or two-way authentication for recording the attendance.In this project, we aim to upgrade the customary used individual authentication systems into a single, integrated contactless and biometric-based identity system. To achieve this, a facial recognition mechanism along with the card strap of RFID and fingerprint authentication mechanism(optional) are combined into a singular attendance managing system which can provide the flexibility to use multi-optional biometric identification methods to verify one's identity and mark their attendance. To address the Covid 19 pandemic situation, the system is also equipped with a contactless InfraRed (IR) thermal sensor to detect the temperature of each employee before they mark attendance. As a result, it automates the manual temperature monitoring method of employees without the involvement of another person. The resultant attendance is recorded data in a cloud service platform (Amazon Web Services(AWS)) in real-Time and later analyze the stored attendance data using a Database platform. Due to the high secureness of the system, unethical activities conducted by employees when marking attendance can be avoided thus increasing the performance of the organization in the long-Term run. © 2022 IEEE.
ABSTRACT
The objective of the paper is to monitor the temperature of students at the entrance and take the necessary steps quickly to overcome the spread of COVID-19. The proposed system consists of a Contactless IR Infrared Temperature Sensor Mlx90614, a NodeMcu, RFID Receiver, RFID Tags and a Buzzer. The Contactless IR Infrared Mlx90614 Temperature Sensor helps in predicting the body temperature. If the temperature range exceeds the normal limit, it stops allowing the person to enter the academic premises. This is done by Machine Learning algorithms. The Temperature is predicted by the ML algorithms and a notification is sent to the Incharge through IFTTT. This implementation helps to stop the spread of COVID-19 and acts as a prevention measure. A unique RFID receiver placed near the entrance aids in the identification of the corresponding student by scanning a unique RFID tag. The ID matches with the database and predicts the contact details, which are sent to the in-charge via SMS. For intelligence decision-making, machine learning is applied to analyze the temperature history for corresponding students from the database, which helps to enable us to accurately detect the affected range. The NodeMCU acts as the brain of the circuit, It controls and processes the data collected from the RFID tag and the wireless temperature sensor. Also, it connects with the IoT cloud using a Wi-Fi module. The buzzer is used to alert the student when the temperature measured is higher than the threshold temperature level. The servo motor is used to control the opening and closing of the door. Thus, the system helps to identify the high-temperature person and stop allowing them into the college premises. © 2022 IEEE.
ABSTRACT
Shopping is considered as an essential activity for many households and is negatively impacted ever since the COVID-19 pandemic had begun. People are panic buying and crowding without social distancing and practice hygiene after exchanging physical touch with objects or cashiers at retail stores. A Smart Shopping System that utilizes RFID and integrated with mobile website technology can improve efficiency and effectiveness for customers to shop even during a pandemic in a more controlled manner. It can be achieved by integrating closely both hardware and software over an active internet connection to send and receive data for real-time updates for each respective customer. The research aims to facilitate users for an improved shopping experience that highlights the accessibility of easy, contactless purchasing, engaging marketing and simultaneously reduces direct contact with public possessions during the coronavirus (COVID-19) pandemic. An extensive discussion of the existing similar systems reviews, literature review and the current developments are performed in depth. The research gathered public insights through a quantitative research approach and applied the gather data for meaningful information in order to recommend an efficient solution. With the creation and visualization of a prototyping model, the researcher demonstrated how a Smart Shopping System could achieve efficiency and effectiveness in a retail store on a small scale. © 2022 IEEE.
ABSTRACT
To control the COVID-19 outbreak, the Malaysia government has to tighten the rules and add on some standard operating procedures (SOP) for all premises. There will be an entrance registration for people that enter any shops, malls, schools, or offices. This entrance registration will take their identities, such as name, contact number, and current temperature. Thus, the government can easily track down and notify the person if the virus transmission occurs. This paper is mainly about improving the daily registration system to monitor the movement of Malaysians during the Covid-19 outbreak. With that needs in mind, a Radio-frequency Identification (RFID) based identity authentication system is developed and presented in this paper. Users do not need to fill in the manual form or scan the Quick Response (QR) code repeatedly, and instead, they are required to just key in the personal data once at the entrance. The RFID tag is applicable to be used as a self-registration at all premises. It can also keep track of the user identity, and the data will be recorded automatically through a monitoring application every time the users enter or leave the premises. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.