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1.
Cogn Res Princ Implic ; 7(1):24, 2022.
Article in English | ProQuest Central | ID: covidwho-1833369

ABSTRACT

Face masks have become common protective measures in community and workplace environments to help reduce the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Face masks can make it difficult to hear and understand speech, particularly for people with hearing loss. An aim of our cross-sectional survey was to investigate the extent that face masks as a health and safety protective measure against SARS-CoV-2 have affected understanding speech in the day-to-day lives of adults with deafness or hearing loss, and identify possible strategies to improve communication accessibility. We analyzed closed- and open-ended survey responses of 656 adults who self-identified as D/deaf or hard of hearing. Over 80% of respondents reported difficulty with understanding others who wore face masks. The proportion of those experiencing difficulty increased with increasing hearing loss severity. Recommended practical supports to facilitate communication and social interaction included more widespread use of clear face masks to aid lip-reading;improved clarity in policy guidance on face masks;and greater public awareness and understanding about ways to more clearly communicate with adults with hearing loss while wearing face masks.

2.
BMC Res Notes ; 15(1):162, 2022.
Article in English | PubMed | ID: covidwho-1833342

ABSTRACT

OBJECTIVES: The increasing spread of severe acute respiratory syndrome coronavirus-2 has prompted Canada to take unprecedented measures. The objective of this study was to examine the impact of the implemented public health measures on the incidence of COVID-19 in Manitoba. RESULTS: Using the COVID-19 dataset, we examined the temporal trends of daily reported COVID-19 cases and the coinciding public health measures implemented from March 12, 2020 to February 28, 2022. We calculated the 7-day moving average and crude COVID-19 infection rate/100,000 Manitobans. Due to the restrictions applied, the infection rate decreased from 2.4 (April 1) to 0.07 infections (May 1, 2020). Between May 4 and July 17, 2020, the reported cases stabilized, and some restrictions were lifted. However, in November, the cases peaked with infection rate of 29. Additional restrictions were implemented, and the rate dropped to 3.6 infections on March 31, 2021. As of August 2021, 62.8% of eligible Manitobans received two vaccine doses. The infection rate increased to 128.3 infections on December 31, 2021 and mitigation measures were implemented. This study describes how physical distancing in conjunction with other containment measures can reduce the COVID-19 burden. Future studies into the extent of the implementation of the restrictions are necessary.

3.
Socius ; 8, 2022.
Article in English | Scopus | ID: covidwho-1833249

ABSTRACT

This research shows how face masks took on discursive political significance during the early stages of the coronavirus disease 2019 pandemic in the United States. The authors argue that political divisions over masks cannot be understood by looking to partisan differences in mask-wearing behaviors alone. Instead, they show how the mask became a political symbol enrolled into patterns of affective polarization. This study relies on qualitative and computational analyses of opinion articles (n = 7,970) and supplemental analyses of Twitter data, the transcripts of major news networks, and longitudinal survey data. First, the authors show that antimask discourse was consistently marginal and that backlash against mask refusal came to prominence and did not decline even as masking behaviors normalized and partly depolarized. Second, they show that backlash against mask refusal, rather than mask refusal itself, was the primary way masks were discussed in relation to national electoral, governmental, and partisan themes. © The Author(s) 2022.

4.
Inquiry ; 59:469580221096285, 2022.
Article in English | MEDLINE | ID: covidwho-1832929

ABSTRACT

Background. Coronavirus disease (COVID-19) is a highly communicable virus that continues to interrupt livelihoods, predominantly those of low-income segments of society. For the prevention of respiratory infections like the current COVID-19 outbreak, face masks are considered an effective approach. Objective. This study intended to assess the knowledge, attitude, and practice of public transport drivers towards face mask use amid the COVID-19 pandemic in Gondar, Ethiopia. Methods. A cross-sectional study was conducted among 412 public transport drivers in Gondar town from July to September 2021. The study subjects were recruited using a simple random sampling technique after proportionally allocating the sample size from the total number of public transport drivers, and finally, study subjects were selected using the convenience sampling method to select the participant drivers working in Gondar Town. The data were collected by face-to-face interview administered questioners and an on-the-spot observational checklist. Results. A total of 412 public transport drivers have participated in the study. The mean age of the respondents was 32.75 years (+/-8.75 years). The majority of the participants were Bajaj drivers 193 (46.84%). Among the responders, 114 (27.67%) of them use radio to gather information about the pandemic and 50 (12.14%) of them had reported being caught by COVID-19. Meanwhile, only 32 (7.77%) were vaccinated against COVID-19. Drivers that had a diploma level of education were found to be 87.7% less knowledgeable than degree holders (AOR .123, 95% CI = .026, .573). Respondents that had good knowledge about COVID-19 and face masks were found to have 1.7 times more positive attitudes than those that had poor knowledge (AOR = 1.728, 95% CI = 1.150, 2.596). drivers whose family members have ever been caught by COVID-19 were found 2 times more likely to use face masks whenever they are working/driving (AOR = 2.173, 95% CI = 1.015, 4.652) than their counterparts. Conclusion. This study revealed a very low Knowledge, attitude, and practice of face mask use among public transport drivers in Gondar town. Periodic reinforcement and training programs are needed for all public transport drivers in each level for proper understanding and adherence to COVID-19 prevention protocols and the use of face masks.

5.
2022 Augmented Humans Conference, AHs 2022 ; : 243-253, 2022.
Article in English | Scopus | ID: covidwho-1832601

ABSTRACT

Wearing masks and social distancing have become the norm during the COVID-19 pandemic. However, these are increasingly seen as a source of frustration in face-to-face communications. While efforts have been made to overcome these impediments to communication, they typically focus on recovering lost communication quality. Herein, we envision a future where everyone augments their vision using face masks with Augmented Reality capabilities, such that people can conduct safe and expressive face-to-face communication in public. To speculate on this vision, we developed an AR mask prototype which can overlay dynamic virtual "masks"on other users. The virtual mask is dynamic in that it accelerates towards any observer who approaches the wearer. Using this system, we conducted an explorative study to further our speculations on the impact of ubiquitous AR technologies. © 2022 ACM.

6.
2022 Augmented Humans Conference, AHs 2022 ; : 26-34, 2022.
Article in English | Scopus | ID: covidwho-1832600

ABSTRACT

We present E-MASK, a mask-shaped interface for silent speech interaction. As face masks have become daily accessories since the COVID-19 pandemic, it is reasonable to utilize a mask as a wearable interface. Unlike conventional speech recognition, we envision that silent speech interaction allows users to access digital services even in crowded public spaces. With flexible and highly sensitive strain sensors, E-MASK presents a new measurement principle for silent speech interactions. We built a dataset of sensor patterns corresponding to 21 fundamental commands of Alexa's operation. All commands were silently spoken by five non-native English speakers. The dataset was used to estimate the silently spoken commands. Estimation accuracies of 84.4% while sitting on a chair and 79.1% while walking on a treadmill were archived. This result suggests that our system provides seamless interaction with digital devices in various situations in daily life, such as walking in a crowd. © 2022 Owner/Author.

7.
4th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2021 ; : 72-76, 2021.
Article in English | Scopus | ID: covidwho-1832586

ABSTRACT

The technological evolution and recent advances in machine learning have transformed how ordinary tasks are performed. Due to many technological, cultural and health related changes (such as Covid 19 pandemic), the means for managing attendance has been transformed with Internet of Things (IoT) based technologies. Attendance management system (AMS) is a system that documents and keeps track of employee and student hours and stores them on local repository or in the cloud. Manual approach to recording and keeping track of attendance is prone to human errors and time consuming. Although many studies have proposed new IoT biometric based solutions to enhance this process, achieving accuracy, efficiency and expense affordability can be a challenging task. The most used biometric approach recently is face recognition IoT solutions. Face recognition can be challenging during the Covid 19 pandemic because of face masks. Taking these issues into consideration, we propose a GPS-enabled Iris-based biometric approach for the attendance management system with smartwatches' compatibility feature. The system performs two main tasks: identification and real time localization. The identification is achieved with iris-based identification while localization is using GPS technology and smart watches. The proposed system addresses many fundamental issues such as the expense factors of manufacturing dedicated tracking wearable devices. It also provides an efficient means of identification using iris-based biometric identification which provides many advantages such as accuracy and enhanced friendly experience without relying on face recognition. The proposed IoT Attendance management systems will be designed to provide better automation for managing attendance and reduce many human errors resulting from manual approaches. © 2021 ACM.

8.
3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022 ; : 507-511, 2022.
Article in English | Scopus | ID: covidwho-1831841

ABSTRACT

The use of computer vision to realize non-contact face mask wearing testing and identify the standardization of wearing can improve the efficiency of mask wearing inspection, which is of great significance to reduce the spread of COVID-19. Based on the YOLO v3 framework in deep learning, this project conducts an in-depth study on the accuracy and efficiency of face mask detection, introduces its structure and principle, and explores and experiments its application based on TensorFlow. The evaluation and identification rate of the experiment was 78∗, which verified the feasibility and practicability of the method. © 2022 IEEE.

9.
21st International Symposium INFOTEH-JAHORINA, INFOTEH 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1831827

ABSTRACT

This paper describes research effort aimed at the use of machine learning, Internet of Things, and edge computing for a use case in health, mainly the prevention of the spread of infectious diseases. The main motivation for the research was the Covid-19 pandemic and the need to improve control of the prevention measures implementation. In the study, the experimentation was focused on the use of machine learning to create and utilize prediction models for face mask detection. The prediction model is then evaluated on the various platforms with a focus on the use on various edge devices equipped with a video camera sensor. Different platforms have been tested and evaluated such as standard laptop PC, Raspberry Pi3, and Jetson Nano AI edge platform. Finally, the paper discusses a possible approach to implement a solution that would utilize the face mask detection function and lays out the path for the future research steps. © 2022 IEEE.

10.
2022 International Conference on Electronics and Renewable Systems, ICEARS 2022 ; : 340-344, 2022.
Article in English | Scopus | ID: covidwho-1831812

ABSTRACT

For the prevention and spread of COVID-19, people are currently used to screen temperature and identify masks in public spaces. All scanning entries include temperature testing equipment;however, manual temperature scanning has some limitations. Temperature scanners aren't well-known among personnel. Despite higher temperature readings or the lack of masks, people are frequently given admission. Manual scanning equipment is useless in huge groups. As a result, an automated system that monitors temperature and mask is required. To address this problem, a completely automated temperature scanner and entry provider system has been presented. A contactless temperature scanning and a camera are used in the system to take images. The scanner is attached to a gate-like mechanism that blocks admission if a feverishness or no mask is detected. The gadget employs a temperature sensor and camera attached to a Raspberry Pi system to monitor the entire operation. The major goal of this article is to automate the entire COVID scanning procedure to reduce the possibility of COVID-19 spreading in densely populated areas like malls, schools, and universities © 2022 IEEE.

11.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 815-819, 2021.
Article in English | Scopus | ID: covidwho-1831747

ABSTRACT

The coronavirus pandemic (COVID-19) has unfolded hastily throughout the entire world. This pandemic disease can spread through droplets and can be airborne. Hence, the use of face masks in public places is crucial to stop its spread. The present study aims to develop a system that can identify masked or non-masked faces;whether it is a normal mask, transparent mask, or a face alike mask. The face mask detection system is developed with the help of Convolutional Neural Networks (CNN). The model compression technique of Knowledge Distillation has been used to make the machine lesser computation and memory intensive so that it is simple to install the model on a few embedded gadgets and cell computing platforms. Using the model compression technique and GPU systems will help boom the calculation velocity of the model and drop the storage space required for calculations. The experimental outcomes show that the developed detector is capable to classify diverse types of masks. Also, it can classify video images in real-time. Using the Knowledge Distillation on the baseline model can improve the testing accuracy from 88.79% to 90.13%. The proposed unique system can be implemented to assist in the prevention of COVID-19 spread and detect various mask types. © 2021 IEEE.

12.
2021 International Conference on Computational Performance Evaluation, ComPE 2021 ; : 803-809, 2021.
Article in English | Scopus | ID: covidwho-1831737

ABSTRACT

Covid-19 has brought various complications in our day-to-day life leading to a disruption in overall movements across the world. Although still researchers and scientists are working on finding more effective ways to deal with it, wearing a face is one of the most simplistic yet efficient ways to overcome this. Wearing a face mask all the time in public places has become a new normal. Therefore, face mask detection for monitoring of people in public places has become a crucial task. Deep learning has been used to make recent advances in the field of object detection. To accomplish this objective, this research employs three state-of-the-art object identification models, notably YOLOv4 and YOLOv4-tiny. The models were trained using a dataset that included photos of persons wearing and not wearing masks. Considering it for surveillance purposes, it can also be used for detection of face and mask in motion. The models employ an approach that involves drawing bounding boxes (red or green) around people's faces and determining whether or not they are wearing a face mask. Further, the performance of these models was compared using mAP, recall F1-score and FPS © 2021 IEEE.

13.
Journal of the Royal Society Interface ; 19(190):20210781, 2022.
Article in English | MEDLINE | ID: covidwho-1831584

ABSTRACT

Face masks do not completely prevent transmission of respiratory infections, but masked individuals are likely to inhale fewer infectious particles. If smaller infectious doses tend to yield milder infections, yet ultimately induce similar levels of immunity, then masking could reduce the prevalence of severe disease even if the total number of infections is unaffected. It has been suggested that this effect of masking is analogous to the pre-vaccination practice of variolation for smallpox, whereby susceptible individuals were intentionally infected with small doses of live virus (and often acquired immunity without severe disease). We present a simple epidemiological model in which mask-induced variolation causes milder infections, potentially with lower transmission rate and/or different duration. We derive relationships between the effectiveness of mask-induced variolation and important epidemiological metrics (the basic reproduction number and initial epidemic growth rate, and the peak prevalence, attack rate and equilibrium prevalence of severe infections). We illustrate our results using parameter estimates for the original SARS-CoV-2 wild-type virus, as well as the Alpha, Delta and Omicron variants. Our results suggest that if variolation is a genuine side-effect of masking, then the importance of face masks as a tool for reducing healthcare burdens from COVID-19 may be under-appreciated.

14.
Advances in Nursing Science ; 45(2):E94, 2022.
Article in English | MEDLINE | ID: covidwho-1831378
15.
Construction Research Congress 2022: Health and Safety, Workforce, and Education, CRC 2022 ; 4-D:541-551, 2022.
Article in English | Scopus | ID: covidwho-1830307

ABSTRACT

Pandemics, such as Covid-19 virus spread fast with significant impact on people and the economy. The construction industry with productivity stagnation of over two decades is not excluded from this significant impact or restrictions that determine the present way of life. These restrictions (e.g., government shutdowns, social distancing, and face mask requirement) impede several construction processes resulting in scheduling restrictions, increased work-related hazards, and developing challenges helping to sabotage existing labor force shortage issues. Consequently, researchers and practitioners have focused on low-risk activities, staggered schedules, etc. However, there is a need to appraise the impact of Covid-19 on construction labor force while making a case for construction automation. In this study, the authors utilized a state of practice review of Covid-19-related developments (i.e., disruptions, standards, and regulatory practices) within construction, along with qualitative and quantitative approach among twelve professionals. The study identified productivity, safety, and quality concerns affecting the construction workforce before proposing a workflow for increased automation within the industry to deal with the present and future pandemics. The findings demonstrate the need and emphasize the importance of embracing automation for construction processes in phases that can improve labor force issues and performance metrics to change the path for lingering concerns in construction. © 2022 ASCE.

16.
Environmental and Health Management of Novel Coronavirus Disease (COVID-19) ; : 419-441, 2021.
Article in English | Scopus | ID: covidwho-1827722

ABSTRACT

The current COVID-19 pandemic has presented unprecedented challenges for health care facilities worldwide. Global production and shipping routes were disrupted, and health care institutions, even in high resource areas, found themselves lacking the basic supplies for effective infection prevention and control. One major hurdle was the global access to supplies, particularly N95/FFP2 masks and alcohol-based hand rub (ABHR) for performing hand hygiene. © 2021 Elsevier Inc. All rights reserved.

17.
International Conference on Electrical and Electronics Engineering, ICEEE 2022 ; 894 LNEE:327-344, 2022.
Article in English | Scopus | ID: covidwho-1826337

ABSTRACT

The COVID-19 pandemic has reshaped everyone’s life as we know it. Many measures and proposals have been suggested to prevent the virus’s rapid spread since scientists detected it. In the midst of many of these, one major piece of advice was to “wear a mask!”. The mask or face coverings can protect the wearer from contracting the virus or spreading it to others. Hence, to ensure that the public is not wearing a mask, one of the applications we have developed is face mask detection, which uses biometrics to map the features of a human face, analyse the data, and determine whether the detected face is a positive image or a non-face or negative image by placing a box around it. The main ideology of studying and researching face mask detection is to use computer technology to directly mimic the action and manner of human facial features and develop a system that reduces the amount of effort required to identify whether or not the person is wearing a mask and notifying them with the assistance of the proposed model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

18.
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 ; 286:317-325, 2022.
Article in English | Scopus | ID: covidwho-1826297

ABSTRACT

The spread of coronavirus can be prevented among the people in a crowded place by making face mask mandatory so that the droplets from the mouth and nose would not reach the masses nearby. The negligence of some people, i.e., by not wearing the mask, causes the spread of this pandemic. Therefore, persons who do not wear masks should be tracked at the entrance to public venues such as malls, institutions, and banks. The mechanism proposed warns if the individual is wearing or not wearing the mask. The proposed system is built in a small CNN model to integrate any low-end devices with minimal cost. The small CNN model like ShuffleNet and Mobilenetv2 are evaluated in Transfer Learning and Deep Learning but the Deep Learning model has better performance than the Transfer Learning. Again, the Deep Learning approach, i.e., mobilenetv2 plus Support Vector Machine achieved 99.5% accuracy, 99.01% sensitivity, 100% precision, 100% FPR, 99.51% F1 score, 99.01% MCC, and 99.01% kappa coefficient. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

19.
2nd International Conference on Intelligent and Cloud Computing, ICICC 2021 ; 286:295-303, 2022.
Article in English | Scopus | ID: covidwho-1826296

ABSTRACT

The whole world is passing through a very difficult time since the outbreak of Covid-19. Wave after wave of this pandemic hitting people very hard across the globe. We have lost around 3.8 million lives so far to this pandemic. Moreover, the impact of this pandemic and the pandemic-induced lockdown on the lives and livelihoods of the people in the developing world is very significant. Till now there is no one-shot remedy available to stop this pandemic. However, spread can be controlled by social distancing, frequent hand sanitization, and using a face mask in public places. So, in this paper, we proposed a model to detect face mask of people in public places. The proposed model uses OpenCv module to pre-process the input images, it then uses a deep learning classifier MobileNetV3 for face mask detection. The accuracy of the proposed model is almost 97%. The proposed model is very light and can be installed on any mobile or embedded system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

20.
International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020 ; 837:367-379, 2022.
Article in English | Scopus | ID: covidwho-1826273

ABSTRACT

The deadliest COVID-19 (SARS-CoV-2) is expanding steadily and internationally due to which the nation economy almost come to a complete halt;citizens are locked up;activity is stagnant and this turn toward fear of government for the health predicament. Public healthcare organizations are mostly in despair need of decision-making emerging technologies to confront this virus and enable individuals to get quick and efficient feedback in real-time to prevent it from spreading. Therefore, it becomes necessary to establish auto-mechanisms as a preventative measure to protect humanity from SARS-CoV-2. Intelligence automation tools as well as techniques could indeed encourage educators and the medical community to understand dangerous COVID-19 and speed up treatment investigations by assessing huge amounts of research data quickly. The outcome of preventing approach has been used to help evaluate, measure, predict, and track current infected patients and potentially upcoming patients. In this work, we proposed two deep learning models to integrate and introduce the preventive sensible measures like face mask detection and image-based X-rays scanning for COVID-19 detection. Initially, face mask detection classifier is implemented using VGG19 which identifies those who did not wear a face mask in the whole crowd and obtained 99.26% accuracy with log loss score 0.04. Furthermore, COVID-19 detection technique is applied onto the X-ray images that used a Xception deep learning model which classifies whether such an individual is an ordinary patient or infected from COVID-19 and accomplished overall 91.83% accuracy with 0.00 log loss score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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