The use of face masks and the impact on heart rate variability during baseline ECG

Sumary We compared electrocardiograms (ECGs) findings with one year difference between each other with and without use of face mask at the moment to be tested. The first ECG was done one year before without face mask, and the second ECG with a mask one year later after 3 months of mandatory use for epidemiological COVID-19 pandemic justifications in healty youth elite athletes. Results Regarding heart rate variability (HRV), an increase in RMSSD was recorded when the test was performed with a mask (M): 108.5 ± 90 ms vs. No mask (NM): 72.9 ± 54.2 ms (p <0.002). And also an increase in SDNN, when the test was done with a M: 86.2 ± 47.2 ms vs. NM: 65.9 ± 43.5 ms (p <0.036). Conclusions The results on ECG are consistent with the increasing predominance of parasympathetic regulation, which is responsible for regulation of the autonomic loop when the subject is using face mask.

Carbon footprint of FFP2 protective facial masks against SARS-CoV-2 used in the food sector: effect of materials and dry sanitisation

Purpose: International outbreak of the SARS-CoV-2 infection has fostered the Italian government to impose the FFP2 protective facial masks in closed environments, including bar, restaurants and, more in general, in the food sector. Protective facial masks are rocketing, both in mass and in costs, in the food sector imposing efforts in fostering reuse strategies and in the achievement of sustainable development goals. The scope of the present paper is to depict possible strategies in manufacturing and reuse strategies that can reduce the carbon footprint (CF) of such devices. Design/methodology/approach: To implement circular economy strategies in the protective facial masks supply chain, it was considered significant to move towards a study of the environmental impact of such devices, and therefore a CF study has been performed on an FFP2 facial mask used in the food sector. Different materials besides the mostly used polypropylene (PP) (polyethylene (PE), polycarbonate (PC), poly (lactic acid) (PLA), cotton, polyurethane (PUR), polystyrene (PS) and nylon 6,6) and different sanitisation alternatives as reuse strategies (both laboratory and homemade static oven, ultraviolet germicidal irradiation) readily implemented have been modelled to calculate the CF of a single use of an FFP2 mask. Findings: The production of textiles in PP, followed by disposal was the main contributor to CF of the single-use FFP2 mask, followed by packaging and transportations. PP and PE were the least impacting, PC, cotton and Nylon 6-6 of the same weight results the worst. PLA has an impact greater than PP and PE obtained from crude oil, followed by PUR and PS. Static laboratory oven obtained an 80.4% reduction of CF with respect to single use PP-made FFP2 mask, whereas homemade oven obtained a similar 82.2% reduction;UV cabinet is the best option, showing an 89.9% reduction. Research limitations/implications: The key strategies to reduce the environmental impacts of the masks (research for new materials and reuse with sanitisation) should ensure both the retention of filtering capacities and the sanitary sterility of the reused ones. Future developments should include evaluations of textile recycling impacts, using new materials and the evaluation of the life cycle costs of the reused masks. Practical implications: This paper intends to provide to stakeholders (producers, consumers and policy makers) the tools to choose the best option for producing and reuse environmentally friendly protective facial masks to be used in the food sector, by using both different materials and easily implemented reuse strategies. Social implications: The reduction of the CF of protective facial masks in the food sector surely will have relevant positive effects on climate change contributing to reach the goals of reducing CO2 emissions. The food sector may promote sustainable practices and attract a niche piece of clients particularly sensible to such themes. Originality/value: The paper has two major novelties. The first one is the assessment of the CF of a single use of an FFP2 mask made with different materials of the non-woven filtering layers;as the major contribution to the CF of FFP2 masks is related to the non-woven textiles manufacturing, the authors test some other different materials, including PLA. The second is the assessment of the CF of one single use of a sanitised FFP2 mask, using different sanitation technologies as those allowed in bars or restaurants. © 2022, Pasquale Giungato, Bianca Moramarco, Roberto Leonardo Rana and Caterina Tricase.

An Algorithm for Facial Mask Area Repair Based Upon Deep Learning

Due to the impact of Corona Virus Disease 2019 (COVID-19), facial mask has become a necessary protective measure for people going out in the last two years. One's mouth and nose are covered to suppress the spread of the virus, which brings a huge challenge for face verification. Whereas some existing image inpainting methods cannot repair the covered area well, which reduces the accuracy of face verification. In this paper, an algorithm is proposed to repair the area covered by facial mask to restore the identity information for face authentication. The proposed algorithm consists of an image inpainting network and a face verification network. Among them, in image inpainting network, to begin with, two discriminators, namely global discriminator and local discriminator. Then Resnet blocks are employed in two discriminators, which is used to retain more feature information. Experimental results show that the proposed method generates fewer artifacts and receives the higher Rank-1 accuracy than other methods in discussion. © 2022 SPIE.

ULN: An efficient face recognition method for person wearing a mask.

Although the face recognition has advanced by leaps and bounds in recent years, recognizing faces with large occlusion, e.g., masks, is still a challenging problem. In the context of the COVID-19 outbreak, wearing masks becomes mandatory, which fails numerous face attendance and surveillance systems. Therefore, a robust face recognition algorithm that can deal with facial masks is urgently needed. To build a mask-robust face recognition algorithm, we first generate numerous facial images with masks based on public face datasets, which obviously alleviates the problem of the training data shortage. Second, we propose a novel network architecture called Upper-Lower Network (ULN) to recognize the faces with masks efficiently. The upper branch of ULN with the mask-free images as input is pretrained that provides supervisory information for the training of the lower branch. Considering that the occlusion areas of masks usually appear in the lower parts of faces, we further divide the high-order semantic features into upper and lower parts. The designed loss function force the learned features of the lower branch similar to those of the upper branch with the same mask-free image inputs, but only the upper part of features similar to the mask counterparts. Extensive experiments demonstrate that the proposed method is effective for recognizing persons with masks and outperforms other state-of-the-art face recognition methods.

Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces.

Owing to the availability of a wide range of emotion recognition applications in our lives, such as for mental status calculation, the demand for high-performance emotion recognition approaches remains uncertain. Nevertheless, the wearing of facial masks has been indispensable during the COVID-19 pandemic. In this study, we propose a graph-based emotion recognition method that adopts landmarks on the upper part of the face. Based on the proposed approach, several pre-processing steps were applied. After pre-processing, facial expression features need to be extracted from facial key points. The main steps of emotion recognition on masked faces include face detection by using Haar-Cascade, landmark implementation through a media-pipe face mesh model, and model training on seven emotional classes. The FER-2013 dataset was used for model training. An emotion detection model was developed for non-masked faces. Thereafter, landmarks were applied to the upper part of the face. After the detection of faces and landmark locations were extracted, we captured coordinates of emotional class landmarks and exported to a comma-separated values (csv) file. After that, model weights were transferred to the emotional classes. Finally, a landmark-based emotion recognition model for the upper facial parts was tested both on images and in real time using a web camera application. The results showed that the proposed model achieved an overall accuracy of 91.2% for seven emotional classes in the case of an image application. Image based emotion detection of the proposed model accuracy showed relatively higher results than the real-time emotion detection.

Perceptions of persons who wear face coverings are modulated by the perceivers' attitude.

We examined if the effect of facial coverings on person perception is influenced by the perceiver's attitudes. We used two online experiments in which participants saw the same human target persons repeatedly appearing with and without a specific piece of clothing and had to judge the target persons' character. In Experiment 1 (N = 101), we investigated how the wearing of a facial mask influences a person's perception depending on the perceiver's attitude toward measures against the COVID-19 pandemic. In Experiment 2 (N = 114), we examined the effect of wearing a head cover associated with Arabic culture on a person's perception depending on the perceiver's attitude toward Islam. Both studies were preregistered; both found evidence that a person's perception is a process shaped by the personal attitudes of the perceiver as well as merely the target person's outward appearance. Integrating previous findings, we demonstrate that facial covers, as well as head covers, operate as cues which are used by the perceivers to infer the target persons' underlying attitudes. The judgment of the target person is shaped by the perceived attitude toward what the facial covering stereotypically symbolizes.

Comparison of the Pre-Lens Tear Film Stability and Visual Performance of a Novel and Two Other Daily Disposable Contact Lenses in Healthcare Professionals Wearing Facial Masks for Prolonged Time.

Objective: To compare a novel daily disposable contact lens (DDCL) verofilcon A with other DDCL materials in terms of pre-lens tear film (PLTF) stabilization and visual performance for prolonged use in healthcare professionals with the use of masks. Methods: Subjects aged 20-40 years old were prospectively randomized into three study groups. Group 1: verofilcon A, group 2: nesofilcon A and group 3: senofilcon A. The subjects were evaluated at baseline with best corrected visual acuity (BCVA), non-invasive tear break up time (NIBUT) of pre-lens tear film, and high order aberrations (HoAs). After 28 days of CL use, NIBUT at 1, 4, 8, and 12 h, HoAs, contrast sensitivity (CS) with CVS100-E and contact lens dry eye questionnaire-8 (CLDEQ-8) were evaluated. Results: Between August and September 2021, 147 eyes of 77 subjects were included in the three study groups. At day 28, the CS scores at 18 cycles per degree, spatial frequencies, and the mean NIBUT scores at 4, 8, and 12 h were higher in the verofilcon A group compared to the nesofilcon A and at 12 h were higher compared to the senofilcon A (p < 0.05). The mean HoAs and CLDEQ-8 test scores were higher in the nesofilcon A group (p < 0.001). Conclusion: The results of this study suggest the superiority of the PLTF stabilization ability of verofilcon A in healthcare professionals with prolonged use of mask. The improved CS and NIBUT scores of this lens could be explained by a new and unique surface technology with greater than 80% water content.

Emotion Recognition for Partial Faces Using a Feature Vector Technique.

Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask images. First, a synthetic mask is used to cover the facial input image. With only the upper part of the image showing, and including only the eyes, eyebrows, a portion of the bridge of the nose, and the forehead, the boundary and regional representation technique is applied. Second, a feature extraction technique based on our proposed rapid landmark detection method employing the infinity shape is utilized to flexibly extract a set of feature vectors that can effectively indicate the characteristics of the partially occluded masked face. Finally, those features, including the location of the detected landmarks and the Histograms of the Oriented Gradients, are brought into the classification process by adopting CNN and LSTM; the experimental results are then evaluated using images from the CK+ and RAF-DB data sets. As the result, our proposed method outperforms existing cutting-edge approaches and demonstrates better performance, achieving 99.30% and 95.58% accuracy on CK+ and RAF-DB, respectively.

Season- and facial site-specific skin changes due to long-term mask wearing during the COVID-19 pandemic.

BACKGROUND: As people have regularly worn facial masks due to the coronavirus disease 2019 (COVID-19) pandemic, mask-wear-related adverse effects on the skin have been recognized. The aim of this study was to explore skin changes, their seasonal variations in the general population caused by commonly used masks and a possible mechanism underlying negative effects of mask-wearing. MATERIALS AND METHODS: Eighteen Japanese females participated in the study during summer and winter in Japan. Skin characteristics were measured in the non-mask-wearing preauricular area and the mask-wearing cheek and perioral areas. RESULTS: Trans-epidermal water loss (TEWL) on the cheek area tended to be increased in winter, which was positively correlated with skin scaliness on the same area. Ceramide (CER) content and composition in the mask-covered stratum corneum (SC) were slightly changed between summer and winter, and CER [NP]/[NS] ratio was negatively correlated with the TEWL on the perioral skin in winter. Skin hydration and sebum secretion were higher on the cheek compared to the perioral area in summer. Skin redness was particularly high on the cheek in winter. CONCLUSION: Mask-wear-related skin changes were season- and facial site-specific, and alterations in SC CER may play a role in barrier-related skin problems caused by mask use.

Mobilenet Based CNN Architecture For Detection of Face Masks

It has remained to be the cause of misery for millions of businesses and lives throughout 2020 and into 2021 after the outbreak of Coronavirus Disease 2019 (COVID-19). Almost everyone, especially those planning to resume in-person activity, is feeling anxious while the world is recovering from the pandemic and prepares to return to a normal condition. Face masks are proven to be the only prominent way of reducing the risk of transfusion of viral agents, as well as provide a sense of protection. But, due to the negligence and casual attitude of people, strict policies must be enacted. Manual tracking of this policy, while possible, is ineffective and time-consuming. This is where technology plays a critical role and that's why in this paper, we propose a Deep Learning-based system that uses Convolutional Neural Network (CNN) architecture to detect unmasked as well as masked faces and can interface with security cameras installed. This architecture is trained by using 1923 images. It was found that a high rate of accuracy (99.13%) and validation was achieved with the proposed model, more accurate than other models. As a result, safety violations can be tracked, face masks can be encouraged, and safe working conditions can be ensured. © 2021 IEEE.

An Automated System for Facial Mask Detection and Face Recognition During COVID-19 Pandemic

The coronavirus (COVID-19) pandemic is an ongoing pandemic of coronavirus disease-2019. It is still spreading continuously across the globe, causing huge economic and social disruption. There are many measures that are suggested by the World Health Organization (WHO) to reduce the spread of this disease. In this paper, we are proposing a system in which people wear masks or not in public and recognize faces who do not wear masks. We detect the people who are monitored by using Webcam and those who are not wearing masks, and the corresponding authority is informed about the same by using convolutional neural network (CNN) with a mobile net and Haar cascade algorithm. The proposed model will help to reduce the spread of the virus and check the safety of surrounding people. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Novel Facial Mask Detection Using Fast-YOLO Algorithm

The extremely high transmission rate of the COVID-19 has made the supply of medical resources in countries around the world in short supply. The implementation of quarantine in order to avoid group infections has a serious impact on the economy, transportation, education and other aspects. Epidemic prevention will be a routine task that needs to be carried out for a long time and cannot be neglected. In view of the fact that wearing masks is currently an effective method of epidemic prevention, and the current face detection models are not effective for masked faces, and pedestrians who have not worn masks in the correct way. It may spread the epidemic. This research will establish a face data set with three kinds of annotations, and combine a variety of deep learning convolutional neural network architectures and methods to design a face detection model that can quickly train and detect wearing a mask, not wearing a mask, and wearing a mask incorrectly faces. In the hope of contributing to the epidemic prevention, we use an adaptive algorithm to adjust the image size to reduce unnecessary operations, and modify the CIOU_LOSS error function to speed up the operation. Experiments have confirmed that our algorithm saves 70% of the time compared to YOLO v5m with the same accuracy. © 2022 IEEE.

Facial Mask Region Completion Using StyleGAN2 with a Substitute Face of the Same Person

In recent years, there has been a worldwide outbreak of coronaviruses, and people are wearing facial masks more and more often. In many cases, people wear masks even when taking photos of themselves, and when photos with the lower half of the face hidden are uploaded to web pages or social networking sites, it is difficult to convey the attractiveness of the photographed persons. In this study, we propose a method to complete the masked region in a face using StyleGAN2, a kind of Generative Adversarial Networks (GAN). In the proposed method, we prepare an image of the same person who is not wearing a mask, and change the orientation and contour of the face of the person in the image to match those of the target image using StyleGAN2. Then, the image with the changed orientation is combined with the target image in which the person is wearing the mask to produce an image in which the mask region is completed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Automated System for Facial Mask Detection and Social Distancing during COVID-19 Pandemic

Since December 2019, the world has started getting affected by a widely spreading virus which we all call the coronavirus. This virus is spread all across the globe, causing many severe health problems and deaths too. COVID-19 is spread when a healthy person comes in contact with the droplets generated when an infected person coughs or sneezes. So, the WHO has suggested some precautionary measures against the spread of this disease. These measures include wearing a mask in public, maintaining social distancing, avoiding mass gatherings. To help reduce the virus’ spread, in this paper, we are proposing a system that detects unmasked people, identifies them, checks if social distancing is followed or not, and also provides a feature of contact tracing. The proposed system consists of mainly two modules: face mask detection and social distancing. There are two more modules which include face recognition and contact tracing. We used two datasets for training our models. First one to detect masks on faces. For this purpose, we collected the image dataset from GitHub and Kaggle. And, the second dataset was for face recognition in which we took our own images for training purposes. It is hoped that our model contributes toward reducing the spread of this disease. Along with COVID-19, this model can also help reduce the spread of similar communicable disease scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automated Facemask Detection and Monitoring of Body Temperature using IoT Enabled Smart Door

Living with the novel Coronavirus is becoming the new normal as nations around the globe resume. However, in order to stop the virus from spreading, we must isolate Covid-infected persons from the rest of the population.Fever is the most common symptom of coronavirus infection, according to the CDC [1], with up to 83 percent of symptomatic patients presenting indications of fever. Early symptom detection and good hygiene standards are therefore critical, particularly in situations where people come into random contact with one another. As a result, temperature checks and masks are now required in schools, colleges, offices, and other public spaces. However, manually monitoring each individual and measuring their respective body temperatures is a cumbersome task. Currently, most of the temperature checkups are done manually which can be inefficient, impractical, and riskybecause sometimes people checking manually may be reluctant to check every person’s temperature or sometimes allow people even if they violate the guidelines. Moreover, the person assigned to manually check will be at high risk as he is exposed to a lot of people. To solve these issues, we propose a project that reduces the growth of COVID-19 by monitoring the presence of a facial mask and measuring their temperature. The Face Mask Detection can be done using the TensorFlow software library, Mobilenet V2 architecture and OpenCV.A non-contact IR temperature sensor is used to monitor the individual's body temperature. To avoid false positives, the system will be strengthened by training it with a variety of cases. Once the system detects a mask, it measures the body temperature of the person. If the temperature is within the normal range, sanitization is done,and the person is permitted entry through an IOT enabled smart door. However, if the system fails to detect a mask or the person's temperature falls out of the predefined range, a buzzer rings and the door remains closed. Our model is intended to be effective in preventing the spread of this infectious disease. © 2022 IEEE.

Masked Face Recognition from Synthesis to Reality

As we have been seriously hit by the COVID-19 pandemic, wearing a facial mask is a crucial action that we can take for our protection. This paper reports a comprehensive study on the recognition of masked faces. By using facial landmarks, we synthesize the facial mask for each face in several benchmark databases with different challenging factors. The IJB-B and IJB-C databases are selected for evaluating the performance against the variation across pose, illumination and expression (PIE). The FG-Net database is selected for evaluating the performance across age. The SCface is chosen for evaluating the performance on low-resolution images. The MS-1MV2 is exploited as the base training set. We use the ResNet-100 as the feature embedding network connected to state-of-the-art loss functions designed for tackling face recognition. The loss functions considered include the Center Loss, the Marginal Loss, the Angular Softmax Loss, the Large Margin Cosine Loss and the Additive Angular Margin Loss. Both verification and identification are conducted in our evaluation. The performances for recognizing faces with and without the synthetic masks are all evaluated for a complete comparison. The network with the best loss function for recognizing synthetic masked faces is then assessed on a real masked face database, the cleaned RMFRD (c-RMFRD) dataset. Compared with a human user test on the c-RMFRD, the network trained on the synthetic masked faces outperforms human vision for a large gap. Our contributions are fourfold. The first is a comprehensive study for tackling masked face recognition by using state-of-the-art loss functions against various compounding factors. For comparison purpose, the second is another comprehensive study on the recognition of faces without masks by using the same loss functions against the same challenging factors. The third is the verification of the network trained on synthetic masked faces for tackling the real masked face recognition with performance better than human inspectors. The fourth is the highlight on the challenges of masked face recognition and the directions for future research. Our code, trained models and dataset are available via the project GitHub site. Author

Reusability of P3 Facial Filter in a Pandemic Emergency: A 3D Analysis of Filter Microstructure with X-ray Microtomography Images after Dry Heat and UV Sterilization Procedures.

OBJECTIVE: Our goal is to evaluate the effects of heat and ultraviolet (UV) irradiation on P3 facial respirator microstructure. INTERVENTION: P3 facial filters were exposed to dry heat and UV sterilization procedures. METHODS: P3 facial filter samples underwent a standardized sterilization process based on dry heat and UV irradiation techniques. We analyzed critical parameters of internal microstructure, such as fiber thickness and porosity, before and after sterilization, using 3D data obtained with synchrotron radiation-based X-ray computed microtomography (micro-CT). The analyzed filter has two inner layers called the "finer" and "coarser" layers. The "finer" layer consists of a dense fiber network, while the "coarser" layer has a less compact fiber network. RESULTS: Analysis of 3D images showed no statistically significant differences between the P3 filter of the controls and the dry heat/UV sterilized samples. In particular, averages fiber thickness in the finer layer of the control and the 60° dry heated and UV-irradiated sample groups was almost identical. Average fiber thickness for the coarser layer of the control and the 60° dry heated and UV-irradiated sample groups was very similar, measuring 19.33 µm (±0.47), 18.33 µm (±0.47), and 18.66 µm (±0.47), respectively. There was no substantial difference in maximum fiber thickness in the finer layers and coarser layers. For the control group samples, maximum thickness was on average 11.43 µm (±1.24) in the finer layer and 59.33 µm (±6.79) in the coarser layer. Similarly, the 60° dry heated group samples were thickened 12.2 µm (±0.21) in the finer layer and 57.33 µm (±1.24) in the coarser layer, while for the UV-irradiated group, the mean max thickness was 12.23 µm (±0.90) in the finer layer and 58.00 µm (±6.68) in the coarser layer. Theoretical porosity analysis resulted in 74% and 88% for the finer and coarser layers. The finer layers' theoretical porosity tended to decrease in dry heat and UV-irradiated samples compared with the respective control samples. CONCLUSIONS: Dry heat and UV sterilization processes do not substantially alter the morphometry of the P3 filter samples' internal microstructure, as studied with micro-CT. The current study suggests that safe P3 filter facepiece reusability is theoretically feasible and should be further investigated.

I9MASKS: FROM A MULTIDISCIPLINARY SUMMER PROJECT TO A NON-ACCREDITED SHORT COURSE

This study presents a research experience with engineering students at undergraduate and graduate levels, during the summer of 2020 at the School of Engineering, University of Minho, Portugal. Following the first pandemic event in Portugal, from March to May 2020, the Foundation for promoting Science and Technology (FCT) has opened a call for research projects among students and researchers at different Universities. The main aim of these projects was to motivate students to return physically to the campus during a summer course, and to promote a research environment among them. i9Masks was one of the projects approved by the University of Minho and its main objective was the development of innovative masks in a silicone elastomer for the protection of COVID-19 with the use of state-of-the-art technologies. The development of masks was at the time a very hot topic as well as a fashionable subject for research. Considering the results obtained, from the final works presented by students, a very positive balance of the experience was achieved. The i9Masks project was a useful learning experience for engineering education, particularly in Portugal, where the opportunity to participate in this type of "learning by doing" experience is very small. Copyright © 2021 by ASME

The Influence of the COVID-19 Pandemic on Orthodontic Treatments: A Survey Analysis.

Coronavirus disease 2019 represents the pandemic of the 21st century that has negatively affected the lives of the whole of humanity. For many months, the only weapons to fight against this infection were protective masks and social isolation. During recent months, fear of the virus has led people to avoid crowded environments and events, and to reduce medical checks, limiting them only to emergencies. Outpatient clinics, doctors' offices, and all closed-in environments were required to limit the patients' access. Nowadays, the presence of specific protocols around the world, and the extended vaccination campaign, have allowed a reduction of many restrictions. Unfortunately, the virus is still widespread, and dental practice and dental treatments suffer the consequences. Dental therapies in general, and in particular orthodontics, are not considered lifesaving. Due to this, orthodontists, in this historical time, must find solutions for motivating patients to start or continue therapies, while providing a safe way for them to do so. There are orthodontists who have developed, during this period, different ways to help them in treating and communicating with patients. Aim: The aim of this study is to assess the influence of the pandemic on the choice to start orthodontic treatment, oral health care, and the importance placed on the appearance of dental occlusions. Materials and Methods: This study is a survey analysis of 159 people, which was posted in Facebook groups of adult orthodontic patients. The timestamps and answers of responses were analyzed to avoid duplicated or interrupted questionnaires. Conclusions: This study found that the current health emergency has not reduced the demand for orthodontic care, while some patients' behaviors are changing in relation to oral hygiene and the importance that they attribute to dental health. It seems that dentists' availability plays a key role in this period of sanitary emergency.