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
Measuring human temperature is a crucial step in preventing the spread of diseases such as COVID-19. For the proper operation of an automatic body temperature measurement system throughout the year, it is necessary to consider outdoor conditions. In this paper, the effect of atmospheric factors on facial temperature readings using infrared thermography is investigated. A thorough analysis of the variation of facial temperature with the prevailing atmospheric conditions was carried out using recordings collected over two years and compared with air temperature values at 1 hour accuracy. A method that takes account of outdoor conditions on temperature readings was proposed. We developed a correction curve with coefficients values based on an analysis of the recordings of people entering the building. Such a method will allow an effective real-time fever screening in public places. © 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
The COVID-19 pandemic also known as the Corona Virus worldwide epidemic is contemplate as the transcendent critical global health disaster in the world. Pneumonia, acute respiratory syndrome, and even death are the severity of this virus. We are living in a situation where Covid infection cases can be increased unexpectedly anytime if we do not follow the advisory of World Health organization (WHO). The majority of people who are infected with the virus has experienced mild to moderate fever. This virus spread rapidly in public places such as hospitals, metro station, railway station, malls etc. In such crowded areas, the chances of virus spread is high and we can prevent this by social distancing and measuring the temperature of the every individual without using human interference. In our idea we have introduced a fully automatic temperature detection system which would energized by piezoelectric generator. We have also implemented an automatic door opening system in which the door of a particular place will remain closed if temperature is above the preset value. The opening and closing of door is done through the piezoelectric generated power. © 2022 IEEE.
ABSTRACT
The current situation of pandemic demands utmost care of health for every individual, rapid spread of SARS-CoV-2 needs regular temperature check-ups as one of the means of identifying the disease. The design of a low cost and efficient system to automate the human temperature sensing using IoT is presented in this paper. This system can be used in most of the places where temperature check ups are to be done and still using the manual check-up tools hence easing the way of checking the temperature. The system mainly consists of two subsystems. One subsystem that determines the temperature value (TS) and the other that triggers the temperature sensor (PS). The PS uses a proximity sensor that senses whether the person is near the temperature sensor and triggers the TS to sense the temperature of individual standing in front of it. The system can be mounted on any simple mirror that enables the individuals to align their head correctly with the temperature sensor and hence it can be used anywhere. To govern the behavior of the sensors we use a microcontroller (Node MCU). In this proposed method we are developing a cost-effective solution to detect the temperature of the individual without human resources installed in the place. The system can be used in various places such as Schools and colleges, public places, hospitals and many more. © 2021 IEEE.
ABSTRACT
In this paper, an efficient embedded machine learning system is proposed to automatically detect face masks and measure human temperature in a real-time application. In particular, our system uses a Raspberry-Pi camera to collect realtime video and detect face masks by implementing a classification model on Raspberry Pi 3 in public places. The face mask detector is built based on MobileNetV2, with ImageNet pre-trained weights, to detect three cases of correctly wearing, incorrectly wearing and not wearing a mask. We also design a human temperature measurement framework by deploying a temperature sensor on the Raspberry Pi 3. The numerical results prove the practicality and effectiveness of our embedded systems compared to some state-of-the-art researches. The results of accuracy rate in detecting three cases of wearing a face mask are 98.61% based on the training results and 97.63% for validation results. Meanwhile, our proposed system needs a short time of 6 seconds for each person to be tested through the whole process of face mask detection and human forehead temperature measurement. © 2021 IEEE.
ABSTRACT
Due to the COVID-19 pandemic, much computer science research has been dedicated to utilizing sensor readings for medical purposes. Throughout this period, the need for virus symptom tracking has become a promising area for remotely deployed sensor networks and platforms. Our research goal is to prove that the temperature readings from these sensor network platforms can be statistically linked to public record, medical case study data. The expected outcome of our project is to prove the correlation between sensor network tracking of remote human temperature data and medical records for COVID cases. The results of this study will prove that tracking human temperature can assist in tracking disease outbreaks in various populations. Our framework platform is comprised of four main modules: (1) Temperature Collection, (2) Internal Data Validation (3) Internal-External data merger, (4) Data Analytics. The temperature data are collected from internal databases, mobile sensing devices and medical health professionals. After collection, the internal data are validated by our software, TAU-FIVE, a multi-tier data quality validation system, then merged with external data sources into a data analytic based data warehouse. The data mart queries are designed to compare the location and date of temperature sensor data with known data sets from government officials. Once blended into a fully operational data warehouse, these data marts produce high quality data analysis linking remotely sensed human temperature readings to sources of disease outbreaks. © 2021 IEEE.