A Real-Time Analytic Face Thermal Recognition System Integrated with Email Notification

COVID-19 is a contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease has spread worldwide, leading to an ongoing pandemic. The most common symptom of COVID-19 is fever which can be detected using various manual screening techniques that have the risk of exposing the personnel. Since the virus has globally spread, a reliable system to detect COVID-19-infected people, especially before entering any premises and buildings, is in high demand. The most common symptom that can be detected is fever, even though people with fever might not have COVID-19. Thus, a real-time analytic face thermal recognition system integrated with email notification that has the capability to scan the person's temperature and simultaneously analyze the measured temperature with the recorded/stored information/data is presented in this paper. The proposed system is also able to send an email notification to the relevant authorities during the real-time analytical process. Besides that, this information is also recorded in the system database for continuous monitoring of the respective person's health status. The development of the proposed system is integrated with a Thermal Module AMG8833, Pi camera, and Raspberry Pi Zero Wireless. The proposed system has been tested and the captured results successfully accomplished the development objectives.

Twitter as Passive Sensor to Understand How COVID-19 Pandemic Affected Human Mobility

During the COVID-19 pandemic, countries all over the world have tried to prevent the spread of the virus with measures like social distancing, movement limitation, closure of premises and shops, voluntary isolation, lockdown, and curfew. Likely, these limitations have influenced the way people moved within urban spaces. In this study, we use Twitter as a passive sensor to understand how these measures affected human mobility. We focus on the city of Milan, one of the most international and active cities in Italy, but also one of the cities most affected by the spread of the virus. We analyzed more than one million of GPS geo-tagged tweets, posted from 2019 to 2022, and results show that the pandemic has affected human mobility (in 2022, less mobility during work hours and more mobility during the evening hours), and show that social and fashion-related activities are the main reasons people move within the city. This study shows the benefits of using Twitter as a passive sensor to measure human mobility: real-time analysis (not possible with interviews and/or questionnaire) and insights of the reasons behind human mobility (not possible to get with the sole use of telephone operators data). © 2023 IEEE.

Right and yet wrong: a politico-economic perspective on Germany's early COVID-19 policy

Applying a spatio-temporal endemic–epidemic forecasting model, we evaluate different perspectives on the adequacy of COVID-19 containment policies. Using Germany's early containment policy as an example, we show that containment policies judged as rational based on the real-time perspective of policymakers may be deemed unnecessary or ineffective in ex-post evaluations. We also demonstrate that one-size-fits-all policies implemented in Germany early in the pandemic are likely suboptimal. © 2023 Regional Studies Association.

The common interests of health protection and the economy: evidence from scenario calculations of COVID-19 containment policies.

We develop a novel approach integrating epidemiological and economic models that allows data-based simulations during a pandemic. We examine the economically optimal opening strategy that can be reconciled with the containment of a pandemic. The empirical evidence is based on data from Germany during the SARS-CoV-2 pandemic. Our empirical findings reject the view that there is necessarily a conflict between health protection and economic interests and suggest a non-linear U-shape relationship: it is in the interest of public health and the economy to balance non-pharmaceutical interventions in a manner that further reduces the incidence of infections. Our simulations suggest that a prudent strategy that leads to a reproduction number of around 0.75 is economically optimal. Too restrictive policies cause massive economic costs. Conversely, policies that are too loose lead to higher death tolls and higher economic costs in the long run. We suggest this finding as a guide for policy-makers in balancing interests of public health and the economy during a pandemic.

Contactless Temperature Detection of Multiple People and Detection of Possible Corona Virus Affected Persons Using AI Enabled IR Sensor Camera

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.

Optimizing the Sensitivity of Biological Particle Detectors through Atmospheric Particle Analysis According to Climatic Characteristics in South Korea

Biological agents used in biological warfare or bioterrorism are also present in bioaerosols. Prompt identification of a biological weapon and its characteristics is necessary. Herein, we optimized an environmentally adaptive detection algorithm that can better reflect changes in the complex South Korean environment than the current models. The algorithm distinguished between normal and biological particles using a laser-induced fluorescence-based biological particle detector capable of real-time measurements and size classification. We ensured that the algorithm operated with minimal false alarms in any environment by training based on experimental data acquired from an area where rainfall, snow, fog and mist, Asian dust, and water waves on the beach occur. To prevent time and money wastage due to false alarms, the detection performance for each level of sensitivity was examined to enable the selection of multiple sensitivities according to the background, and the appropriate level of sensitivity for the climate was determined. The basic sensitivity was set more conservatively than before, with a 3% alarm rate at 20 agent-containing particles per liter of air (ACPLA) and a 100% alarm rate at 63 ACPLA. The reliability was increased by optimizing five variables. False alarms did not occur in situations where no alarm was unnecessary.

Face Mask Detection using MobileNetV2

To stem the COVID19 pandemic, great attention is needed to mitigate public health and the global economy which are negatively impacting. To overcome this, a technique is required to urge people put on the face mask. To contribute to the health of communities, this article aims to design a very precise and real-time analysis that can efficiently detect non-mask faces in public and thus, enforcing to wear mask. According to the World Health organization, the most effective way to fight the transmission of the corona virus is to wear medical masks. The detection of face mask in is done with the machine learning by using the series of stages involved through classification of images: MobileNetV2. The steps and stages used for developing the model square measure grouping the information, and pre-processing the data to remove noisy data, splitting the data, testing the model for the accuracy, and implementation of the model. The engineered model will sight those that square measure sporting a mask associated not sporting it at an accuracy of 95.85 percent. © 2022 IEEE.

Object Detection for COVID Rules Response and Crowd Analysis

Understanding the hotspots attracting massive crowds is a huge necessity during this pandemic times. The knowledge of analyzing crowds will help to plan and avoid the spread of the virus to a large extent by identifying exact hotspots. Understanding where the crowds flock and whether they are following the guidelines or not will help in taking appropriate actions, allotting concerned personnel in advance, and closing of areas which are at higher risks can be advantageous. In order to realize the situation, real-time analysis of the pandemic rules like social distancing, wearing masks is necessary. This paper proposes the use of video surveillance and provides a combined application to check the factors necessary during crowd situations as per rules set by the Government. This work uses python as a coding language, and YOLOv4 algorithm along with various libraries like darknet to improve video and image analysis for the identification of exact requirements. This work also uses Cuda software and Cudnn library for the acceleration of processing. The paper proposes importantly, counting people passing through a particular area, detecting whether people are following social distancing, detecting if the participants are wearing a mask, and counting the number of vehicles passing through an area. The knowledge of analyzing crowds will help to plan and avoid the spread of the virus to a large extent by identifying exact hotspots. All the applications are connected to the graphical user interface (GUI) and depending on the input each application proposed considers different analysis. The types of input are image, video, image directory and live feed are considered to obtain better results. © 2021 IEEE.

Containment of procedure-associated aerosols by an extractor tent: effect on nebulized drug particle dispersal.

BACKGROUND: Several medical procedures involving the respiratory tract are considered as 'aerosol-generating procedures'. Aerosols from these procedures may be inhaled by bystanders, and there are consequent concerns regarding the transmission of infection or, specific to nebulized therapy, secondary drug exposure. AIM: To assess the efficacy of a proprietary high-efficiency-particulate-air-filtering extractor tent on reducing the aerosol dispersal of nebulized bronchodilator drugs. METHODS: The study was conducted in an unoccupied outpatient room at St. James's Hospital, Dublin, Ireland. A novel real-time, fluorescent particle counter, the Wideband Integrated Bioaerosol Sensor (WIBS), monitored room air continuously for 3 h. Baseline airborne particle count and count during nebulization of bronchodilator drug solutions were recorded. FINDINGS: Nebulization within the tent prevented any increase over background level. Nebulization directly into room air resulted in mean fluorescent particle counts of 4.75 x 105/m3 and 4.21 x 105/m3 for Ventolin and Ipramol, respectively, representing more than 400-fold increases over mean background level. More than 99.3% of drug particles were <2 µm in diameter and therefore small enough to enter the lower respiratory tract. CONCLUSION: The extractor tent was completely effective for the prevention of airborne spread of drug particles of respirable size from nebulized therapy. This suggests that extractor tents of this type would be efficacious for the prevention of airborne infection from aerosol-generating procedures during the COVID-19 pandemic.

Onsite real-time detection of covid-like-virus transmission through air using spark-induced plasma spectroscopy.

In March 2020, COVID-19 was officially classified as a pandemic and as a consequence people have adopted strenuous measures to prevent infection, such as the wearing of PPE and self-quarantining, with no knowledge of when the measures will no longer be necessary. Coronavirus has long been known to be non-infectious when airborne; however, studies are starting to show that the virus can infect through airborne transmission and can remain airborne for a significant period of time. In the present study, a spark-induced plasma spectroscopy was devised to characterize the air propagation of the virus in real-time. The risk of air propagation was evaluated in terms of changes in virus concentration with respect to distance traveled and measurement time. Thus, our study provides a benchmark for performing real-time detection of virus propagation and instantaneous monitoring of coronavirus in the air.