Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

Reading Chinese anti-COVID-19 pandemic narratives on facemasks as the art of disaster governance: a semiotic and biopolitical survey

In China and around the world, the global spread of COVID-19 has made wearing a facemask more than a pragmatic or aesthetic individual-level issue: it has instilled in people deontic value. In Chinese anti-epidemic narratives, the semiotic ideology of wearing a facemask has been closely related to collectivism, patriotism and, to a certain degree, nationalism. The facemask not only serves as a protective biomedical device but also as a cultural, political and spatial sign of the line of defence against disorders of the natural system, to establish the order of the social system. This paper argues from the perspective of semiotics and life politics that such mask narratives have effectively helped China prevent the large-scale spread of the epidemic across the nation and have served as a means of collective psychotherapy, paradoxically transforming individual separation into collective spiritual cohesion. Previous semiotic studies of disaster have not paid much attention to plagues or disaster governance discourse, between which biomedicine plays an important role. Thus, this paper aims to shed light on how biomedicine works with politics in coding and decoding the relationship between the natural system of the plague and the social system of governance.

An IoT based Smart Robot that Aids in the Prevention of COVID19 Spread

The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.

Social representations of mask wearing in the general population during the COVID-19 pandemic.

Introduction: Although one of the most prominent interventions against COVID-19, face masks seem poorly adopted by the general population. A growing body of literature has found that using face masks has social meaning. This qualitative study assessed the perceptions, representations and practices of mask wearing in the general population. Methods: A qualitative survey by short semi-structured walking interviews was carried out from April to December 2021 in 11 cities in France's Pays de la Loire region. Study locations were selected for their varied geographical, social, and economic characteristics, with urbanized and rural areas. Four domains linked to perceptions of masks and wearing them were explored: (i) evolution in mask wearing, (ii) decision-making methods for wearing and not wearing; (iii) incorporating the mask into way of life; (iv) projecting into the future. Results: A total of 116 people were interviewed. Masks marked a shift from the ordinary world to the pandemic. Overall, interviewees considered masks an obstacle to breathing, communication, and social interactions, leading to establishing strategies circumventing the mask mandate. Poor attention was paid to their medical usefulness as an obligatory clothing accessory. Mask-wearing decisions were driven by social relations, common sense, and vulnerability. The greater the feeling of security (i.e., being with close relatives), the less it was worn or worn properly, with decreased attention to others and their health. Most participants did not remember learning to wear a mask. Some were convinced that mask-wearing could not be learned (experiential knowledge). Institutions (school and work) played a central role by facilitating incorporation of masks into daily life. Conclusions: This study emphasizes the need to reinforce the individual medical values of face masks to prevent COVID-19. Ambitious education and training programmes should be planned to learn how and when to wear masks. Institutions (work and school) may be critical for this purpose.

Minimizing Aerosol Leakage from Facemasks in the COVID-19 Pandemic.

Background: Aerosol therapies with vented facemasks are considered a risk for nosocomial transmission of viruses such as severe acute respiratory syndrome coronavirus 2. The transmission risk can be decreased by minimizing aerosol leakage and filtering the exhaled air. Objective: In this study, we determined which closed facemask designs show the least leakage. Methods: Smoke leakage was quantified during in- and exhalation in a closed system with expiration filter for three infant, six child, and six adult facemasks (three times each mask), using age-appropriate anatomical face models and breathing patterns. To assess leakage, smoke release was recorded and cumulative average pixel intensity (cAPI) was calculated. Results: In the adult group, aircushion edges resulted in less leakage than soft edges (cAPI: 407 ± 250 vs. 774 ± 152) (p = 0.004). The Intersurgical® Economy 5 mask (cAPI: 146 ± 87) also released less smoke than the Intersurgical® Clearlite 5 (cAPI: 748 ± 68) mask with the same size, but different geometry and edge type (p-value <0.05). Moreover, mask size had an effect, as there was a difference between Intersurgical® Economy 4 (cAPI: 708 ± 346) and 5, which have the same geometry but a different size (p-value <0.05). Finally, repositioning masks increased the standard deviations. Mask leakage was not dependent on breathing patterns within the child group. Conclusions: Mask leakage can be minimized by using a closed system with a well-fitting mask that is appropriately positioned. To decrease leakage, and therewith minimize potential viral transmission, selecting a well-fitting mask with an aircushion edge is to be recommended.

Sustainable Management of Facemask Waste Generated From COVID-19 Pandemic in Bhutan

Background: The rise in facemask waste with the uncertainty of deposition data and all its concomitant pressure on management has resulted in new risks and challenges for achieving sustainable development goals (SDGs). The purpose of this mixed methods study was to estimate facemask waste generation and examine management in Bhutan since the COVID-19 pandemic.Method: One thousand five hundred and twenty-seventh (1,527) consenting members of the public from Class A Thromdes (self-governing municipalities) were surveyed online from February to April 2022, to estimate mask waste using descriptive statistics. This was supported by the waste characterization conducted in Memelhakha, the biggest open landfill in Thimphu Thromde. In-depth interviews were conducted with environmental officers, later summarized on emerging themes.Results: The participants discarded on average 3-4 surgical facemasks in a week. Thimphu Thromde generated 144.5 and 186 surgical and N95 waste (tons/year), while those respective figures were (30.7, 54.2), (47.3,65.7), and (65.8, 91.8) for Samdrup Jongkhar, Gelephu, and Phuentsholing. The pandemic has exposed the fragility of the waste system, with an increase in plastic waste that is largely landfilled. This issue can persist and result in big problems, given Thromde negligence and the country's inefficiencies and inadequacies in social and technical aspects.Conclusion: The findings can be used as a tool to re-examine Bhutan's overwhelmed management system to incorporate disaster preparedness and resilience to resist the change and;impact beyond the pandemic.

Acute effects of different facemasks during High Intensity Interval Training in trained women. A randomized crossover pilot study

The aim of the present investigation was to analyze the acute effect of different facemasks on physiological, perceptual and performance parameters in trained young women during a High Intensity Interval Training (HIIT) on cycle ergometer. Fifteen subjects participated in the study. Heart rate variability, muscle oxygen saturation, lactate concentration and comfort parameters were measured under 3 conditions: no facemask, surgical and FFP2 facemask. The use of facemasks had no effect on any variable related to oxygen saturation, heart rate variability and cycling power during the HIIT protocol. Only lactate concentration revealed significantly lower values in the No mask condition compared to FFP2 3 min after HIIT (p =.038). Regarding the overall perception and comfort, participants reported greater discomfort when wearing the FFP2 mask compared to the No mask condition (p<.05). On the contrary, the analysis of heart rate variability, revealed significant differences (p<.001) in the Pre compared to the Post exercise for all conditions. The use of surgical or FFP2 facemask during HIIT training does not affect performance during strenuous exercise while perceived comfort appears to be lower with FFP2 masks in physically trained women.

Masked Face Detection and Recognition System Based on Deep Learning Algorithms

Coronavirus (COVID-19) pandemic and its several variants have developed new habits in our daily lives. For instance, people have begun covering their faces in public areas and tight quarters to restrict the spread of the disease. However, the usage of face masks has hampered the ability of facial recognition systems to determine people's identities for registration authentication and dependability purpose. This study proposes a new deep-learning-based system for detecting and recognizing masked faces and determining the identity and whether the face is properly masked or not using several face image datasets. The proposed system was trained using a Convolutional Neural Network (CNN) with cross-validation and early stopping. First, a binary classification model was trained to discriminate between masked and unmasked faces, with the top model achieving a 99.77% accuracy. Then, a multi-class model was trained to classify the masked face images into three labels, i.e., correctly, incorrectly, and non-masked faces. The proposed model has achieved a high accuracy of 99.5%. Finally, the system recognizes the person's identity with an average accuracy of 97.98%. The visual assessment has proved that the proposed system succeeds in locating and matching faces. © 2023 by the authors.

Covid-19 Control: Face Mask Detection Using Deep Learning for Balanced and Unbalanced Dataset

Facemask wearing is becoming a norm in our daily lives to curb the spread of Covid-19. Ensuring facemasks are worn correctly is a topic of concern worldwide. It could go beyond manual human control and enforcement, leading to the spread of this deadly virus and many cases globally. The main aim of wearing a facemask is to curtail the spread of the covid-19 virus, but the biggest concern of most deep learning research is about who is wearing the mask or not, and not who is incorrectly wearing the facemask while the main objective of mask wearing is to prevent the spread of the covid-19 virus. This paper compares three state-of-the- art object detection approaches: Haarcascade, Multi-task Cascaded Convolutional Networks (MTCNN), and You Only Look Once version 4 (YOLOv4) to classify who is wearing a mask, who is not wearing a mask, and most importantly, who is incorrectly wearing the mask in a real-time video stream using FPS as a benchmark to select the best model. Yolov4 got about 40 Frame Per Seconds (FPS), outperforming Haarcascade with 16 and MTCNN with 1.4. YOLOv4 was later used to compare the two datasets using Intersection over Union (IoU) and mean Average Precision (mAP) as a comparative measure;dataset2 (balanced dataset) performed better than dataset1 (unbalanced dataset). Yolov4 model on dataset2 mapped and detected images of masks worn incorrectly with one correct class label rather than giving them two label classes with uncertainty in dataset1, this work shows the advantage of having a balanced dataset for accuracy. This work would help decrease human interference in enforcing the COVID-19 face mask rules and create awareness for people who do not comply with the facemask policy of wearing it correctly. Hence, significantly reducing the spread of COVID-19.

A Novel Vision of Reinforcing Nanofibrous Masks with Metal Nanoparticles: Antiviral Mechanisms Investigation.

Prevention of spreading viral respiratory disease, especially in case of a pandemic such as coronavirus disease of 2019 (COVID-19), has been proved impossible without considering obligatory face mask-wearing protocols for both healthy and contaminated populations. The widespread application of face masks for long hours and almost everywhere increases the risks of bacterial growth in the warm and humid environment inside the mask. On the other hand, in the absence of antiviral agents on the surface of the mask, the virus may have a chance to stay alive and be carried to different places or even put the wearers at risk of contamination when touching or disposing the masks. In this article, the antiviral activity and mechanism of action of some of the potent metal and metal oxide nanoparticles in the role of promising virucidal agents have been reviewed, and incorporation of them in an electrospun nanofibrous structure has been considered an applicable method for the fabrication of innovative respiratory protecting materials with upgraded safety levels.

Shape Memory Respirator Mask for Airborne Viruses.

The emergence of COVID-19 has spurred demand for facemasks and prompted many studies aiming to develop masks that provide maximum protection. Filtration capacity and fit define the level of protection a mask can provide, and the fit is in large part determined by face shape and size. Due to differences in face dimensions and shapes, a mask of one size will not be likely to fit all faces. In this work, we examined shape memory polymers (SMPs) for producing facemasks that are able to alter their shape and size to fit every face. Polymer blends with and without additives or compatibilizers were melt-extruded, and their morphology, melting and crystallization behavior, mechanical properties, and shape memory (SM) behavior were characterized. All the blends had phase-separated morphology. The mechanical properties of the SMPs were modified by altering the content of polymers and compatibilizers or additives in the blends. The reversible and fixing phases are determined by the melting transitions. SM behavior is caused by physical interaction at the interface between the two phases in the blend and the crystallization of the reversible phase. The optimal SM blend and printing material for the mask was determined to be a polylactic acid (PLA)/polycaprolactone (PCL) blend with 30% PCL. A 3D-printed respirator mask was manufactured and fitted to several faces after being thermally activated at 65°C. The mask had excellent SM and could be molded and remolded to fit a variety of facial shapes and sizes. The mask also exhibited self-healing and healed from surface scratches.

Masking morality in the making: how China's anti-epidemic promotional videos present facemask as a techno-moral mediator

The spread of Covid-19 has made facemask a critical artifact for people all over the world. Mediating between (non-)wearers and their environment, facemask makes people aware of a set of morally relevant distinctions and sometimes forces decisions on them. In fact, the semiotics of "facemask in use” largely shapes the parameters for human practice and experience in the epidemic. Drawing on the techno-moral mediation concept [Verbeek, 2008. "Obstetric Ultrasound and the Technological Mediation of Morality: A Postphenomenological Analysis.” Human Studies 31 (1): 11–26], we explore how anti-epidemic promotional videos released by Chinese authorities build facemask-related narratives in the Covid-19 epidemic. Findings reveal that "facemask in use” links people's "smaller love” for family to a "bigger love” for Chinese people in general;transforms an individual to member of a large group of commoner-turned-protectors;or marks the military's loyalty and obedience to the Party-State, which makes possible the "Chinese speed” in saving lives. We add to extant literature by unraveling an entanglement between the moralization of facemask-wearing and China's traditional values, social institutions and media newsroom culture about disaster coverage. This scrutiny into the "face-masking morality in the making” implies that the power to frame the cultural significance of facemask induces an alternative mode of techno-moral change, which may outlast the epidemic itself.

Covid-19 Indoorsafety Monitoring System Using Machine Learning

The covid-19 epidemic is causing a world pandemic crisis. The powerful device in these situations is to wear a mask in public entry, schools, and super markets to reduce the Covid-19 spread. There are many convolutions face recognition technologies to distinguish effective images for monitoring the discovery of a face mask. Therefore, it is very important to improve the effectiveness of the acquisition methods available in the existing system. The data set value increases in the proposed input to improve the maximum accuracy. The proposed method is used to determine body temperature, face mask, and social retention using advanced machine learning methods. Using the EM8RFID scanner personal data such as temperature value, face mask identification and public distance detection are collected. It is used to indicate the state of human health in a cloud platform. A wireless heat sensor issued to determine a person's body temperature using MLX90614 without anyone. The Raspberry integrated with the pi camera is used in detecting a face mask and a social distance. Raspberrypi captures the image and detects with the convolution neural network algorithm verifying a person is wearing a face mask, following social distance. Therefore, authorities should monitor the human condition in the cloud platform area. By applying this concept, the spread of Covid-19 can be greatly reduced and it is easier to identify peoplewith Covid-19symptoms. © 2022 IEEE.

Toward High-quality Face-Mask Occluded Restoration

Face-mask occluded restoration aims at restoring the masked region of a human face, which has attracted increasing attention in the context of the COVID-19 pandemic. One major challenge of this task is the large visual variance of masks in the real world. To solve it we first construct a large-scale Face-mask Occluded Restoration (FMOR) dataset, which contains 5,500 unmasked images and 5,500 face-mask occluded images with various illuminations, and involves 1,100 subjects of different races, face orientations, and mask types. Moreover, we propose a Face-Mask Occluded Detection and Restoration (FMODR) framework, which can detect face-mask regions with large visual variations and restore them to realistic human faces. In particular, our FMODR contains a self-adaptive contextual attention module specifically designed for this task, which is able to exploit the contextual information and correlations of adjacent pixels for achieving high realism of the restored faces, which are however often neglected in existing contextual attention models. Our framework achieves state-of-the-art results of face restoration on three datasets, including CelebA, AR, and our FMOR datasets. Moreover, experimental results on AR and FMOR datasets demonstrate that our framework can significantly improve masked face recognition and verification performance. © 2023 Association for Computing Machinery.

Allergenic Effect of Facemasks Used Due To Covid-19 Pandemic

We aim to discuss the prevalence of adverse skin reactions to facemask among the community admitted to our hospital during the SARS outbreak. Between 2019 and 2020, 97 patients who developed an allergic reaction on their face in the SARS-CoV-2 pandemic were discussed. The average age of the patients was 37.7 (range 18-78), while the average age was 35.2 for males and 40.04 for females. While 13 (27.7%) of the male patients were using cloth masks, 34 (72.3%) of them were using surgical masks, 11 (22%) of the female patients were using cloth masks and 39 (78%) of them were using surgical masks.The combination of the female gender, additional disease, and surgical mask contributes to the development of allergic reactions on the face.Copyright © 2022 International Journal of Academic Medicine and Pharmacy. All rights reserved.

Using the health action process approach to predict facemask use and hand washing in the early stages of the COVID-19 pandemic in China.

Personal hygiene including wearing facemask and washing hands are instrumental to reduce transmission of COVID-19. The present study applied the health action process approach (HAPA) to examine the process from intention to protective behaviors in the early stages of the COVID-19 pandemic. A longitudinal online survey study was conducted among 229 individuals (61.6% females; M age = 25.37 years, SD age = 8.34 years) living in Hubei province, China. Action self-efficacy, outcome expectancy, risk perception, intention, planning and action control regarding facemask wearing and hand washing were assessed at baseline (Time 1), and behaviors were assessed a week later (Time 2). Data were collected from 30 January to 16 February 2020. Two structural equation models were specified to test the theory-driven determinants of the facemask wearing and hand washing respectively. The results showed that action self-efficacy predicted intentions to wear facemasks and wash hands. Intention and action control predicted both behaviors at Time 2. Associations between planning and behaviors were mixed. Mediation analyses revealed that action control significantly mediated the relationship between intention and both behaviors (facemask wearing: 90% CI [0.01, 0.12]; hand washing: 95% CI [0.01, 0.21]). Planning did not mediate the relationship between intention and the two behaviors. The findings illustrate that action self-efficacy is positively associated with intention to facemask wearing and hand washing, and action control contributes to bridging intention to behaviors. Both motivational and volitional factors warrant consideration in interventions to improve adherence to facemask wearing and hand washing in COVID-19.

A Cross-Sectional Study on the Assessment of Knowledge and Methods Practiced in Disinfecting and Disposal of Facemasks among the General Public

Objectives: The vast amount of facemasks generated as waste should be disposed of appropriately and responsibly to prevent adverse effects on health and the environment. This study aims to assess the knowledge and methods commonly practiced by the public to disinfect and dispose of used facemasks. Method(s): A cross-sectional study was conducted from June 2021 to December 2021 in the South Part of Karnataka using a face-validated questionnaire. The questionnaire included subject demographic details and methods to disinfect and dispose of masks. The data were recorded and analyzed using Microsoft Excel. Result(s): A total of (n=501) participants took part in the study. This study observed that the most commonly practiced method was to wash the masks with specific cleaners (n=259, 53%). We also assessed knowledge regarding the methods of disposal of a mask. The majority of the participants in our study are aware of the correct method for disposal of used masks(n=342, 68.26%), while (n=159, 31.73%) of subjects are not aware of the right facemask disposal method. Subjects who are educated (p=0.008) and regular mask users (p=0.011) are aware of the right method for mask disposal. Conclusion(s): Many people are unaware of the correct disposal methods. Hence, Manufacturers and Scientists should identify an effective way to disinfect and dispose of used masks and educate the public to practice the proper method of facemask disposal.Copyright © 2023 The Authors. Published by Innovare Academic Sciences Pvt Ltd.

Measured Air Flow Leakage in Facemask Usage.

The importance of wearing a facemask during a pandemic has been widely discussed, and a number of studies have been undertaken to provide evidence of a reduced infectious virus dose because of wearing facemasks. Here, one aspect that has received little attention is the fraction of breathing flow that is not filtered because it passes as leak flow between the mask and face. Its reduction would be beneficial in reducing the dose response. The results of the present study include the filter material pressure loss parameters, pressure distributions under masks, and the fraction of breathing flow leaked versus steady breathing flow in the range of 5 to 30 L min-1, for two commonly used facemasks mounted on mannequins, in the usual 'casual' way and in a 'tight' way by means of three different fitters placed over the mask to improve the seals. For the 'casual' mount, leaks were high: 83% to 99% for both masks at both exhalation and inhalation flows. For the 'tight' mount with different fitters, the masks showed different lower levels in the range of 18 to 66% of leakage, which, for exhalation, were nearly independent of flow rate, while for inhalation, were decreasing with increasing rates of respiration flows, probably because suction improved the sealing. In practice, masks are worn in a 'casual' mount, which would imply that nearly all contagious viruses found in aerosols small enough to follow air streams would be exhaled to and inhaled from the ambient air.

Development of a reusable low-cost facemask with a recycled hydrophobic layer for preventive health care.

The current work focuses on designing a low-cost, reusable, and highly efficient facemask for protection from respiratory droplets that cause COVID-19, other infection-causing organisms, and dust allergies. Several masks available in the market are single-use that would choke the environment through plastic pollution or are expensive for the commoner to afford. In the present study, the facemask incorporates a waste-derived polyethylene terephthalate (PET) layer and a non-woven polypropylene (PP) layer sandwiched between two tightly woven cotton layers. Combining these layers provides comfort and breathability, besides high bacterial and particulate filtration efficiency. Moreover, the unique PET layer provides mechanical strength and a 3D shape that enables hindrance-free speaking and prevents spectacle fogging. Compared to commercial N95 masks, the developed mask can be reused up to 30 washes and recycled with zero waste discharge ensuing green technology. Moreover, the mask was produced at an affordable cost of Rs. 17 (0.22 USD), including labor charges, and sold at a 100% profit margin @ Rs.35 (0.45 USD) per unit. Further, the mask was certified by neutral testing agencies and provided to a population of more than 6 lakhs, thus significantly contributing to the mitigation of COVID-19.

Determining the filtration effectiveness of non-standard respiratory protective devices by an ad-hoc laboratory methodology.

The recent pandemic caused by COVID-19 profoundly changed people's habits. Wearing a face mask has become usual in everyday life to reduce the risk of infection from airborne diseases. At the beginning of the pandemic, the massive request of surgical or filtering face piece (FFP) masks resulted in a global shortage of these devices for the most exposed people, such as healthcare workers. Due to this high demand for respiratory protective devices, many industrial plants have partly converted to the production of face masks using adapted materials and not complying with any specific regulation (non-standard respiratory protective devices or community masks). In this work, an ad-hoc laboratory methodology has been developed to evaluate the filtration efficiency of the materials that compose the community masks using specific instrumentation. The instrumentation consists of three main tools: an aerosol generator, a specifically designed measuring chamber, and an optical particle sizer (OPS) for the measurement of aerosol concentration. The generated aerosol was sent into the measuring chamber, divided into two separate sections by the respiratory mask. The OPS measured the aerosol mass concentration upstream and downstream of the respiratory mask, and from the concentration difference the filtration efficiency was evaluated. The proposed methodology has been validated by evaluating the particle filtration efficiency (PFE) of certified respiratory masks and was then applied for the evaluation of the filtration efficiency of different types of non-standard or community masks to analyze their effectiveness in protecting from the risk of infection of airborne diseases.