Interpersonal Distance Tracking with mmWave Radar and IMUs

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. © 2023 Owner/Author.

COVID-19 vaccine stance classification from tweets

Since the discovery and betterment of vaccines for human diseases, Anti-Vaccine rhetoric and resistance have been prevalent in social circles. These sentiments adversely affect the effectiveness of preventing the contraction of deadly contagious diseases, such as COVID-19. With the advent of social media platforms, the expression of anti-vaccine stances has a far greater reach in society. In this paper, we tackle the task of COVID-19 vaccine stance detection to gauge people's receptiveness towards vaccines and subsequently understand the effectiveness of the vaccination drives. © 2021 Copyright for this paper by its authors.

COVID-19 SOP Compliance and Monitoring Electronic System for Business and Public Places Using Arduino Uno

COVID-19 Corona virus disease is a rapidly spreading contagious disease that is causing a global public health crisis. In December 2019, the coronavirus was identified in Wuhan, China. COVID-19 is causing severe disease issues and many people are losing their lives daily. SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) is a severe infectious disease that is spreading very fast and is currently inflicting a healthcare crisis across the globe. The lethal coronavirus was founded in Wuhan, China in December 2019. The symptoms of this disease are fever, cough, fatigue, no taste or smell, stinging throat, headache, and difficulty in breathing. This deadly disease, COVID-19, is difficult to identify and spread. The vaccination process is still going on around the world. There are some existing strategies to minimize the spread of the COVID-19 virus by monitoring the temperature rise using sensors, wearing masks, and sanitizing their hands frequently. The proposed system comprises of an RFID reader, an IR sensor, a temperature sensor, a buzzer, a laptop or a personal computer with a web cam. A person on entry gets detected for their body temperature, wearing a face mask and then sanitizing their hands. If the temperature of the person is below 37.6 degrees, i.e., below the acceptance limit, then mask detection takes place by using MATLAB followed by spraying the sanitizer. Now the door will open automatically. Otherwise, the door will not open and the buzzer will sound. With these precautionary steps, people can survive this pandemic situation. © 2023 IEEE.

Impacts of COVID-19-Related Non-Pharmaceutical Interventions on Mobility and Accidents in Bangladesh

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown—including on the transport sector—to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra-and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making. © National Academy of Sciences: Transportation Research Board 2021.

TVET Technology Development and Transfer Effort for Combating COVID-19 in Addis Ababa City, Ethiopia

Purpose --The main purpose of this research work is to evaluate the efforts of TVET efforts' in developing and transferring useful technologies in Addis-Ababa, Ethiopia. TVET Institutions are engaged in developing and transferring technologies like hand washing devices, sanitizer sprayers, ventilators, beds, disinfection devices, sanitizers, antiviral finish fabrics, masks and hand-free devices that is used to prevent contamination of the human body while working with materials of different kinds. These equipment's posse's characteristic simple operational and quality properties like usability, functionality, efficiency, etc. The evaluation of these developed and transferred technologies help to prevent transmission of COVID-19 at community level. Design/methodology/approach -- The methodology used in this research was descriptive and purposive sampling type. The sample pool consisted of 5 TVET institutions. 40 respondents participated in this questionnaire study. The response was recorded through interview questionnaires based on the 5-point Likert Scale. Data analysis and Cronbach's alpha reliability tests were computed using SPSS and Minitab software. Practical Implications: The coronavirus is the deadly pandemic and highly contagious disease the human mankind has witnessed since 1918 flue pandemic. However, the coronavirus spread in Ethiopia at the community level has prompted many TVET (Technical Vocational and Education Training) institutions and universities to ramp up their efforts to develop and transfer technologies. Hence TVET organizations need therefore to develop and transfer technologies that are useful to prevent the spread of COVID-19. This research will help to gather the technical information pertaining to design, quality and performance of Hand washing equipment's, face masks, Hand sanitizers and other equipment's. Findings - The results prove TVET Institutions' efforts were successful in developing and transferring the technologies required to combating COVID-19. Cronbach's alpha (reliability test) value is 0.77 for the TVET colleges, indicating the data is excellent, unique and consistent. The position of the TVET institution is excellent as regards to the efforts put in developing and transferring technologies used for combating COVID-19 in and around Addis Ababa. The responses received were unique in nature. Originality/value -- COVID-19 has posed many challenges to public and has resulted in many deaths in Addis Ababa and entire Ethiopia. This work is unique and would make valuable contribution and gather information on the design and technical aspects of developed and transferred technologies by TVET Institutions used for combating COVID-19. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Mental Health Amidst COVID-19: A Review Article.

Outbreaks of infectious diseases confined to a particular locality are not unusual. Respiratory infections such as tuberculosis or community-acquired pneumonia are known in developing and underdeveloped countries. However, COVID-19 infection had globally created havoc due to its high rate of transmission and serious consequences on physical and mental health paralyzing the healthcare facilities of not only developing but also developed nations. This created a sense of uncertainty and insecurity in the public globally, adversely affecting the mental health of almost every individual. It is genuinely obtrusive that the COVID-19 pandemic brought about a global lockdown, adversely affecting the psychological health of the public. Some pandemic-related stressors affect nearly everyone. This review aims to study the effect of the COVID-19 pandemic in terms of psychological well-being and its overall effect on society, thereby making it essential to lend them a helping hand.

Contagious Health Risk and Precautionary Social Distancing.

BACKGROUND: Since humans are social animals, social relations are incredibly important. However, in cases of contagious diseases such as the flu, social contacts also pose a health risk. According to prominent health behavior change theories, perceiving a risk for one's health motivates precautionary behaviors. The "behavioral immune system" approach suggests that social distancing might be triggered as a precautionary, evolutionarily learned behavior to prevent transmitting contagious diseases through social contact. This study examines the link between personal risk perception for an infectious disease and precautionary behavior for disease-prevention in the context of social relationships. METHODS: At 2-week intervals during the first semester, 100 Psychology freshmen indicated their flu risk perception, whether they had been ill during the previous week, and their friendships within their freshmen network for eight time points. RESULTS: Social network analysis revealed that participants who reported a high flu risk perception listed fewer friends (B = -0.10, OR = 0.91, p = 0.026), and were more likely to be ill at the next measuring point (B = 0.26, OR = 1.30, p = 0.005). Incoming friendship nominations increased the likelihood of illness (B = 0.14, OR = 1.15, p = 0.008), while the reduced number of friendship nominations only marginally decreased this likelihood (B = -0.07, OR = 0.93, p = 0.052). CONCLUSION: In accordance with the concept of a "behavioral immune system," participants with high flu risk perception displayed a social precautionary distancing even when in an environment, in which the behavior was ineffective to prevent an illness.

COVID-19 Data Clustering Using K-means and Fuzzy c-means Algorithm

Corona Virus Disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory symptoms. It has been declared a global pandemic since 2019 by the World Health Organization. Countries are in an authoritarian state of preventing and controlling this pandemic, and the USA is the central hub. The COVID-19 virus has also shown variance. As an outcome of the genetic recombination of genes that arise from coronavirus, their short life span results in mutations that promote new strains. However, the number of individuals who passed their lives is still counted. Additionally, it is crucial to analyze the spread of the virus before it is deferred in the lungs. In this research, the effort has been taken to predict the proliferation of the virus through various chest radiography images by data clustering. In this study, two clustering algorithms, i.e., the K-means algorithm and the Fuzzy c-means algorithm, have been used better to analyze the spread of the virus in the lungs. These algorithms are further being compared and evaluated for the precise result of both models. This study helps to recognize the most suitable clustering model for the COVID-19 prediction and spread of the virus in the lung. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Deep Learning based Lung Segmentation Prior for Robust COVID-19 Classification

Independent of a person's race, COVID-19 is one of the most contagious diseases in the world. The World Health Organization classified the COVID-19 outbreak as a pandemic after noting its global distribution. By using (i) sample-supported analysis and (ii) image-assisted diagnosis, COVID-19 is examined and verified. Our goal is to use CT scan images to identify the COVID-19 infiltrates. The followings steps are used to carry out the suggested work: (i) Automated segmentation with CNN;(ii) Feature mining;(iii) Principal feature selection with Bat-Algorithm;(iv) Classifier implementation using mobile framework and (v) Performance evaluation. We used a variety of automatic segmentation algorithms in our experiment, and the VGG-16 produced better results. This study is evaluated using benchmark datasets gathered, and SVM based RBF kernal classifier system resulted in superior COVID-19 abnormality identification. © 2023 IEEE.

An Intelligent IoT-based COVID-19 Contact Tracing System for Ashesi University

The outbreak of contagious diseases demand isolation and quarantining of infected persons and people they have been in close proximity with. This can be easily achieved if technology-based systems are designed to facilitate contact tracing. The aim of this research project is to develop a privacy focused IoT-based COVID-19 contact tracing system that leverages mobile devices and artificial intelligence for the Ashesi University community. To achieve this, we divided the project into two main parts: The software sub-system and the hardware subsystem. The software sub-system comprises of a cross-platform mobile application that tracks users, and an admin portal to monitor user activities. The hardware sub-system is an IoT-based system that uses a Raspberry Pi to capture indoor images with the aid of a Raspberry Pi camera module. It processes the images to determine whether the occupants of the room have been in close proximity with one another or not while relaying feedback to them via its actuators and at the same time updates the admin portal. Through system testing, it was identified that 32% our system users considered privacy during the pandemic as critical even though 95% confirmed that the system assures very high level of privacy. © 2022 IEEE.

ALow Cost QuArm Band for Controlling Contagious Diseases

In the recent times it is found that there is a growing interest in the field of controlling the contagious diseases, especially after the outbreak of the novel COVID-19 (coronavirus). It still remains to be one of the biggest threats to humanity and people are dying and getting infected on a daily basis. Governments across the globe are trying their level best to contain the virus. They are also taking the necessary steps (e.g., travel bans, suspension of recreational and outdoor activities concerning mass audiences or public, isolation and contact tracing, social distancing, etc.). There are many patients who are undocumented just because they have coronavirus in their systems but they show no symptoms. Around 79% patients come under this category. It is to be noted that the total count of the number of cases at present in several countries differ from the actual people who are infected at present. This is because in the maj ority of cases, the symptoms show after a certain period of days and not just instantly. Also testing the whole population of a country in such a limited time is simply not possible. The World Health Organization recommended COVID-19 patients to isolate themselves from the healthy individuals in order to stop the spread of the disease. In order to ensure that this happens more efficiently and smoothly, in this paper an IoT based wearable band called QuArm band (i.e) Quarantine Arm band, which the patient can wear on his/her arm for tracking the real-time location of the patient to ensure that the quarantine rules are being followed is designed. This band is made keeping in mind the requirements of the public and the cost is set accordingly. Web interface alongside the band is made to retrieve the information. Notification on band tampering is also enabled. © 2022 IEEE.

Community-aware group testing

Group testing is a technique that can reduce the number of tests needed to identify infected members in a population, by pooling together multiple diagnostic samples. Despite the variety and importance of prior results, traditional work on group testing has typically assumed independent infections. However, contagious diseases among humans, like SARS-CoV-2, have an important characteristic: infections are governed by community spread, and are therefore correlated. In this paper, we explore this observation and we argue that taking into account the community structure when testing can lead to significant savings in terms of the number of tests required to guarantee a given identification accuracy. To show that, we start with a simplistic (yet practical) infection model, where the entire population is organized in (possibly over-lapping) communities and the infection probability of an individual depends on the communities (s)he participates in. Given this model, we compute new lower bounds on the number of tests for zero-error identification and design community-aware group testing algorithms that can be optimal under assumptions. Finally, we demonstrate significant benefits over traditional, community-agnostic group testing via simulations using both noiseless and noisy tests1. IEEE

Design and modeling of a waiter robot

The science of robotics is considered one of the most practical sciences in all fields. The application of this science is visible in all kinds of work fields and related fields, from construction activities to activities in the fields of medicine or even social services. One of the social services that are very widely used, is delivering items and orders to customers. This work is the duty of people who are called waiters. This job has very few benefits for people working in this field. Also, things like illness can cause some delay in the employer's work or not complete his work in some cases, also in situations such as when contagious diseases have spread, the direct communication of people within a short distance can cause more spread of the disease. The devices ordered by the customers could increase the speed of work and have a low-risk connection, the costs of the employer could be reduced, perfect service could be given to the customers, and the workforce could be employed for more useful work. This robot is specifically designed to use for reception in the conference hall of the growth center of Kharazmi University to receive the people present in this conference hall, but as mentioned above, these robots can be used in other places such as hospitals for delivering medicines to patients, also can be used in restaurants to deliver customer's orders to them. With this replacement, the speed of catering increases, at the same time, there is no lack of accuracy, and the issue that becomes more important with the spread of the contagious disease Covid-19 is hygiene, which can achieve several important goals in this field with this replacement. Specifically, during the reception, the distance between the host and the guest is less than one meter and is unsafe. Also, there is a possibility that each of the parties is a carrier of contagious diseases, and these problems are solved by this replacement. © 2022 IEEE.

Face mask detection using transfer learning and OpenCV in live videos

In 2019, we have seen the biggest epidemic of the century, which claimed many lives worldwide. The epidemic has in fact changed our life in many ways. It changed the way we interact with people. Wearing a mask is now the new normal. Though now the vaccine for the disease is available, still wearing a mask can save us from Covid19, its variants, and other contagious diseases.Especially at places where the large gathering is expected wearing a mask can be made mandatory and our proposed framework can do its monitoring through CCTV cameras.So in this research, we build a deep learning-based framework to detect whether some person is wearing a mask or not through the live video stream. We used a total of three state-of-The-Art transfer learning methods to train our system and used OpenCV to detect faces in the live video stream. We found that efficientnetB1 achieved the highest accuracy of 97.75%. © 2022 IEEE.

Using Dynamic Models to Showcase Pandemic Prevention Empirical Covid-19

Coronavirus disease (COVID-19) is a viral contagious disease caused by a newly discovered coronavirus. The COVID-19 virus primarily spreads from an infected person through droplets of saliva or nasal discharge when the person coughs or sneezes, and most people who have been infected with the virus usually experience mild to severe respiratory illness, and they recover with minimal or no treatment. COVID-19 causes mild illness in the majority of patients although it can be fatal in rare cases. Our project focuses on using an SPO2 level monitor and thermal scanning to monitor patient health and take precautions to avoid constant transmission, as well as providing support to patients by assisting them with basic needs with the help of food delivery agencies and non-governmental organizations (NGOs) and assisting with prevention. We use an enhanced version of the SIR epidemic model, which is further explained in this work as an IoT-based system which is being used for automated health monitoring and surveillance, this work aims to reveal certain facts about the current situation that are not presented by data, as well as predict and forecast future situations. AI-assisted sensors can be of major help to foresee whether or not someone is tested positive for the virus supported on indicators like body temperature, coughing patterns, and blood oxygen levels. The ability to track people's locations is another helpful function. All these problems collectively checked will make an efficient model to curb the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Quarantined-subject monitoring and indoor positioning with anti-Absconding artificial intelligence (Project BANTAY)

Quarantine is the process of restricting and separating the movement of people who have been exposed to a contagious disease to avoid proliferation. Quarantined subjects were monitored manually, and patients tended to abscond or 'run away.' In the Philippines, there was a lack of research and the absence of similar technology commercially available related to this matter. Project Bantay integrated Artificial Intelligence of Things (AIoT), indoor positioning systems, and wearable technology to alleviate the shortage of personnel, long-Term savings on workforce utilization in the government, and predict absconding or 'run-Away' potentially infectious individuals in our current and future quarantine facilities. Also, it included information system development for monitoring quarantined subjects' heart rates and temperatures that would eventually help the government combat and prepare for a similar unexpected pandemic. The prototype would start by turning on the device after being placed on the quarantined subject. The device must be linked to the router, and the sensors must then be calibrated. The web interface should receive and be able to see data readings, including temperature, heart rate, the location of the subject being quarantined, and the removal of the device via an IR reading. The temperature, heart rate sensor, and indoor positioning all measured above a p-value of 0.05, which accepted the null hypothesis, confirming that the actual and commercial product versus Project Bantay's temperature, heart rate, and indoor positioning accuracy was statistically the same. The anti-Absconding tendencies of the hypothetical dataset using machine learning data analytics showed that among four inherently multioutput machine learning regression algorithms, the Decision Tree Regression Algorithm could output a much better result in determining the tendencies of a subject to abscond from the quarantine facility. © 2022 IEEE.

Detrimental impact of contagious disease cues on consumer preference for anthropomorphic products.

Despite the extensive use of anthropomorphism strategy in marketing practices, little research attention has been given to the environmental factors that influence consumer preference for anthropomorphic products. This research examines when and why contagious disease cues can influence consumer preference for anthropomorphic products. The results from four empirical experiments consistently show that when exposed to contagious disease cues, consumers exhibit a lower preference for anthropomorphic products (Study 1), which is mediated by social withdrawal (Study 2). Furthermore, our findings demonstrate that this detrimental effect would be attenuated for products in digital (vs. physical) format (Study 3), or in regions with low (vs. high) local severity of the contagious disease (Study 4). These findings contribute to the literature on contagious diseases and anthropomorphism and offer important managerial implications. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11002-022-09614-x.

A Framework of a User Interface Covid-19 Diagnosis Model

The world is facing the global challenge of COVID-19 pandemics, which is a topic of great concern.It is a contagious disease and infects others very fast.Artificial intelligence (AI) can assist healthcare professionals in assessing disease risks, assisting in diagnosis, prescribing medication, forecasting future well, and may be helpful in the current situation.Designing, a user-friendly Web application-based diagnosis model framework, is more useful in health care.The study focuses on a Web-based model for diagnosing the COVID-19 patients without direct contact with the patient.Chest CT scans have been important for the testing and diagnosing of COVID-19 disease.The Web-based model would take inputs, CT scan images, and users' symptoms and display classification results: NON-COVID-19 or COVID-19 infected. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well. Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world. However, with the advancement of technology, the Internet of Things (IoT) and social IoT (SIoT), the versatile data produced by smart devices helped a lot in overcoming this lethal disease. Data mining is a technique that could be used for extracting useful information from massive data. In this study, we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset” COVID-19 Symptoms and Presence.” RapidMiner Studio ML software was used to apply the Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (K-NNs) and Naive Bayes (NB), Integrated Decision Tree (ID3) algorithms. To develop the model, the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures, Cohan's kappa statistics, properly or mistakenly categorized cases and root means square error. The results demonstrate that DT outperforms other methods, with an accuracy of 98.42% and a root mean square error of 0.11. In the future, a devised model will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets. © 2023 Tech Science Press. All rights reserved.

Touchless Secure QR-Based ATM System

We live in an era, where ATMs can be used hundreds of times a day. With rising outbreaks like COVID-19 and many others, it can be one threatening source of spreading contagious diseases more rapidly. This paper proposes a new touchless ATM system, in which authentication can be done by scanning a generated QR code shown on the ATM screen through the bank's mobile app instead of inserting one's ATM card into the ATM. The ATM user gets verified by validating the device location and entering an OTP sent to their mobile device on scanning a QR code, after which they can withdraw or deposit cash, performing secure touchless transactions through the bank's mobile app. This proposed system has been implemented, tested, and proven to fulfill the desired purpose, taking various security countermeasures into consideration. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.