Effect of face masks on speech understanding: A clinical perspective during speech audiometry

The objective was to measure the effect of various face masks on speech recognition threshold and the word recognition score in the presence of varying background noise levels.20 normal-hearing adult subjects(a total of 40 ears)participated.Pure tone audiometry followed by speech recognition threshold and word recognition score at the most comfortable level in varying signal-to-noise ratios(SNR0,SNR10,and SNR15)using surgical,pleated cloth,and N95 masks.Using surgical,cloth,and N95 masks,speech recognition thresholds increased by 1.8 dB,4.4 dB,and 5.05 dB,respectively.Word recognition scores decreased by 32%without a mask,43.7%in a surgical mask,46.3%in a cloth mask,and 46.7%in N95 mask conditions,between SNR15 and SNR0.The speech recognition threshold was negatively affected with cloth and N95 masks.Surgical masks do not affect the word recognition scores at lower background noise levels.However,as the signal-to-noise ratio decreased,even the surgical,cloth,and N95 masks significantly impacted the word recognition score even in normal-hearing individuals.

Conductive Polymer Composites Fabricated by Disposable Face Masks and Multi-Walled Carbon Nanotubes: Crystalline Structure and Enhancement Effect

Influenced by recent COVID-19,wearing face masks to block the spread of the epidemic has become the simplest and most effective way.However,after the people wear masks,thousands of tons of medical waste by used dis-posable masks will be generated every day in the world,causing great pressure on the environment.Herein,con-ductive polymer composites are fabricated by simple melt blending of mask fragments(mask polypropylene,short for mPP)and multi-walled carbon nanotubes(MWNTs).MWNTs were used as modifiers for composites because of their high strength and high conductivity.The crystalline structure,mechanical,electrical and thermal enhancement effect of the composites were investigated.MWNTs with high thermal stability acted the role of promoting the crystallisation of mPP by expediting the crystalline nucleation,leading to the improvement of amount for crystalline nucleus.MWNTs fibers interpenetrate with each other in mPP matrix to form conducting network.With 2.0 wt% MWNTs loading,the tensile strength and electrical conductivity of the composites were increased by 809% and 7 orders of magnitude.MWNTs fibers interpenetrate with each other in mPP matrix to form conducting network.Thus,more conducting paths were constructed to transport carriers.The findings may open a way for high value utilization of the disposable masks.

Running with Face Masks or Respirators Can Be Detrimental to the Respiratory and Cardiovascular Systems

Since the fi rst cases of coronavirus disease 2019(COVID-19)caused by severe acute respiratory syndrome coro-navirus 2(SARS-CoV-2)were reported at the end of 2019,this infection has spread around the globe,becoming a pandemic.The use of face masks and respirators is an important public health measure to reduce or prevent transmission of SARS-CoV-2.Here we discuss the hypothetical mechanisms by which exercise with face masks or respirators can induce detrimental effects on the cardiovascular system,potentially explaining adverse events such as cardiac arrhythmias and spontaneous pneumothorax.Although sudden death associated with the wearing of a face mask during running is a rare event,the risk is higher especially in those with existing cardiac comorbidities.In such cases,a mask designed specifi cally for runners with no or few side effects of oxygen defi ciency should be considered instead.

Effect of an E-Educational Poster on Improving the Knowledge, Attitude, and Practice on the Proper Use of Face Masks among School Students

Introduction: Two spread methods of Covid-19, namely airborne and respiratory droplets, can be prevented by proper use of face masks. However, it has been reported an inadequate knowledge attitude and practice of proper use of face masks among school students. Therefore, the knowledge, attitude, and practice of school students should be improved. Different approaches are used to improve knowledge, attitude, and practice. However, e-posters are rare, and the effect of e-posters on improving the knowledge, attitude, and practice of school students on the proper use of face masks has not been studied. Objectives: The objective of this study was to determine the effect of an e-educational poster on knowledge, attitude, and practice of the proper use of face masks among school students. Method: This study was conducted as a pre-test and post-test design. The sample was 364 grade 11 students of the Gampaha educational division, Sri Lanka. Data were collected using self-administered questionnaires distributed pre and post to the interventional e-education poster. Data analyses were conducted by using SPSS Software. Results: Results show no significant demographic difference (p = 0.446) between the pre and post-test groups. A significant increase was observed between the pre and post-test mean scores of knowledge (p ≤ 0.05), attitude (p ≤ 0.05), and practice (p ≤ 0.05) on the proper use of face masks. In pre-group knowledge (p = 0.155), attitude (p = 0.258) and practice (p = 0.211) shows no significant difference due to gender. Also post group knowledge (p = 0.079), attitude (p = 0.835) and practice (p = 0.435) shows no significant difference due to gender. Conclusions: The results suggest that e-educational posters may be useful to improve the knowledge, attitude, and practice on the proper use of face masks among school students. The improvement of knowledge, attitude, and practice on the proper use of face masks by e-educational posters among school students occurs irrespective of gender.

Fabrication of Modified Fibrous Filters by Electrospinning and Investigating Their Application as Improved Face Masks

Owing to the significant increase in air pollutants and the spread of infectious diseases,it seems that the use of face masks will become an essential item in human societies.Therefore,there is a need to conduct more research to develop novel types of respirators utilizing upto-date science such as nanotechnology.In this study,we fabricated a nanocomposite fibrous filter containing modified graphene oxide(GO)and zinc oxide(ZnO)nanoparticles.This layer was used as an active filter for absorbing and removing air pollutants,such as suspended submicron particles(below 2.5 microns)and CO_(2),NO_(2),and SO_(2)gases.The synthesized nanostructures and fibrous filters were characterized by different analysis(FTIR,XRD,TGA,and FESEM),and the performance of the filters was surveyed by tests such as pressure drop,CO_(2),NO_(2),SO_(2)gas rejection,and particulate removal.The results showed that the stabilization of the modified GO and ZnO nanostructures on the fibrous filter improved the effectiveness of this filter as a mask for removing toxic particles and gases,and the filter containing nanoparticles had the best performance.

The effect of activity and face masks on exhaled particles in children

Importance:Despite the high burden of respiratory infections among children,the production of exhaled particles during common activities and the efficacy of face masks in children have not been sufficiently studied.Objective:To determine the effect of type of activity and mask usage on exhaled particle production in children.Methods:Healthy children were asked to perform activities that ranged in intensity(breathing quietly,speaking,singing,coughing,and sneezing)while wearing no mask,a cloth mask,or a surgical mask.The concentration and size of exhaled particles were assessed during each activity.Results:Twenty-three children were enrolled in the study.Average exhaled particle concentration increased by intensity of activity,with the lowest particle concentration during tidal breathing(1.285 particles/cm^(3)[95%CI 0.943,1.627])and highest particle concentration during sneezing(5.183 particles/cm^(3)[95%CI 1.911,8.455]).High-intensity activities were associated with an increase primarily in the respirable size(≤5μm)particle fraction.Surgical and cloth masks were associated with lower average particle concentration compared to no mask(P=0.026 for sneezing).Surgical masks outperformed cloth masks across all activities,especially within the respirable size fraction.In a multivariable linear regression model,we observed significant effect modification of activity by age and by mask type.Interpretation:Similar to adults,children produce exhaled particles that vary in size and concentration across a range of activities.Production of respirable size fraction particles(≤5μm),the dominant mode of transmission of many respiratory viruses,increases significantly with coughing and sneezing and is most effectively reduced by wearing surgical face masks.

Determinants of COVID-19 Face Mask Litter in Coastal Urban Parking Lots: Implications for Source Modelling of Potential Microplastic Pollution

Despite cities being recognized as being potential sources of microplastic pollution to the wider environment, most surveys of COVID-19 plastic-based litter have been undertaken through linear transects of marine beaches. For the far fewer number of studies conducted on inland and urban locations, the site-specific focus has primarily been surveys along the length of streets. The present study is the first to specifically assess the standing stock (i.e., moment-in-time) of littered face masks for the entire surface area of urban parking lots. The density of face masks in 50 parking lots in a Canadian coastal town (0.00054 m2 ± 0.00051 m2) was found to be significantly greater than the background level of littering of town streets. Face mask density was significantly related to visitation “usage” of parking lots as gauged by the areal size of the lots and of their onsite buildings, as well as the number of vehicles present. Neither parking lot typology nor estimates of inferred export (various measures of wind exposure) and entrapment (various metrics of obstruction) of face masks had a significant influence on the extent of whole-lot littering. In consequence, modelling of the potential input of mask-derived microplastics to the marine environment from coastal communities can use the areal density of face masks found here in association with the total surface area of lots for individual municipalities as determined through GIS analysis.

Functional Fiber Membranes with Antibacterial Properties for Face Masks

Reusable face masks are an important alternative for minimizing costs of disposable and surgical face masks during pan-demics.Often complementary to washing,a prolonged lifetime of face masks relies on the incorporation of self-cleaning materials.The development of self-cleaning face mask materials requires the presence of a durable catalyst to deactivate contaminants and microbes after long-term use without reducing filtration efficiency.Herein,we generate self-cleaning fibers by functionalizing silicone-based(polydimethylsiloxane,PDMS)fibrous membranes with a photocatalyst.Coaxial electro-spinning is performed to fabricate fibers with a non-crosslinked silicone core within a supporting shell scaffold,followed by thermal crosslinking and removal of the water-soluble shell.Photocatalytic zinc oxide nanoparticles(ZnO NPs)are immo-bilized on the PDMS fibers by colloid-electrospinning or post-functionalization procedures.The fibers functionalized with ZnO NPs can degrade a photo-sensitive dye and display antibacterial properties against Gram-positive and Gram-negative bacteria(Escherichia coli and Staphylococcus aureus)due to the generation of reactive oxygen species upon irradiation with UV light.Furthermore,a single layer of functionalized fibrous membrane shows an air permeability in the range of 80-180 L/m^(2)s and 65%filtration efficiency against fine particulate matter with a diameter less than 1.0µm(PM_(1.0)).

Electrospun Nanofibers-Based Face Masks

Textiles have proved to be very important materials to human beings since the time immemorial.And,fibers are the basic building units of these materials.In this perspective we substantiate the uniqueness and capability of nanofibers as active layers in face masks,to protect people against the novel coronavirus disease(COVID-19).This time-sensitive letter introduces the mechanisms based on which their active filters function,the uniqueness of electrospun nanofibers in face masks and do-it-yourself(DIY)steps to realize a fully functional face mask at home.

Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method

A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person’s physical and mental state at any given time.Accordingly,many studies extract physiological signals from the human body with non-contact methods,and most of them require facial feature points.However,under COVID-19,wearing a mask has become a must in many places,so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research.In this study,RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate,blood pressure,respiratory rate,and forehead temperature for peoplewearing masks due to the pandemic.Using the green(G)minus red(R)signal in the RGB image,the region of interest(ROI)is established in the forehead and nose bridge regions.The photoplethysmography(PPG)waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method,baseline drift calibration,normalization,and bandpass filtering.The relevant parameters in Deep Neural Networks(DNN)for the regression model can correctly predict the heartbeat and blood pressure.In addition,the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths.Meanwhile,the thermal image can be used to read the temperature average of the ROI of the forehead,and the forehead temperature can be obtained smoothly.The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3%and the error of forehead temperature within±0.5°C.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

Notwithstanding the religious intention of billions of devotees,the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).Most attendees ignored preventive measures,namely maintaining physical distance,practising hand hygiene,and wearing facemasks.Wearing a face mask in public areas protects people from spreading COVID-19.Artificial intelligence(AI)based on deep learning(DL)and machine learning(ML)could assist in fighting covid-19 in several ways.This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering(DLFMD-RMG)technique during the COVID-19 pandemic.The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering.To accomplish this,the presented DLFMD-RMG technique undergoes two pre-processing levels:Bilateral Filtering(BF)and Contrast Enhancement.For face detection,the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector.In addition,the face detection performance can be improved by the seeker optimization algorithm(SOA)for tuning the hyperparameter of the ResNet-50 module,showing the novelty of the work.At last,the faces with and without masks are classified using the Fuzzy Neural Network(FNN)model.The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset.The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.

Impact of COVID-19 pandemic on the ocular surface

BACKGROUND There have been increased reports of dry eyes in the coronavirus disease 2019(COVID-19) pandemic era.AIM To analyze the differences in tear film properties from pre-and post-pandemic of the COVID-19 era.METHODS It was a retrospective comparative study.Patients were divided into three groups according to the data of multimodal ocular surface evaluation:(1) Group 1 if it was before Portugal lockdown decision(from August 2019 to March 2020);(2) Group 2 if it was after Portugal lockdown decision but without mask mandate(from April 2020 to October 2020);and(3) Group 3 if it was after Portugal lockdown but with mask mandate in health public highway(from November 2020 to April 2021).The following variables were analyzed:Lipid layer thickness,blink rate,Schirmer test,tear meniscus height,tear osmolarity,non-invasive break-up time,and loss area of the meibomian glands.RESULTS The study included 548 eyes of 274 patients,aged 18 years to 89 years,with a mean age of 66.15 ± 13.40 years at the time of multimodal ocular surface evaluation.Compared to group 1:(1) Mean lipid layer thickness was better in group 2(P = 0.001) and group 3(P < 0.001);(2) Schirmer test was similar in group 2(P = 0.576) and better in group 3(P = 0.002);(3) Tear osmolarity and loss area of the meibomian glands were worse in group 2(P = 0.031 and P < 0.001,respectively) and in group 3(both with P < 0.001);(4) Blink rate and tear meniscus height were similar in group 2(P = 0.821 and P = 0.370,respectively) and worse in group 3(P < 0.001 and P = 0.038,respectively);and(5) Non-invasive break-up time was worse in group 2(P = 0.030) and similar in group 3(P = 0.263).CONCLUSION Our study demonstrated that differences existed in tear film properties comparing data from the pre-and post-pandemic of the COVID-19 era.

The Impact of Face Mask Inks and Dyes on the Environment and Human Health

To reduce the impact of the novel SARS-CoV-2 virus, popularly known as the Coronavirus, many public health-related rules have been established around the world. Along with social distancing and lockdowns, most countries have mandatory wearing of face masks in public areas to limit the spread of the virus during the COVID-19 pandemic. However, because people are free to choose any method to make their masks, some are being fabricated from materials that can be toxic to the environment and human health. This paper discusses how inks and dyes used in face masks are causing major environmental degradation and health issues in industry workers and the general mask-wearing public. The goal fixed for the present study is to raise the alarm with authorities and decision-makers regarding the toxic nature of some colors (dyes and inks) and fabrics in the masks being worn every day.

Face mask detection algorithm based on HSV+HOG features and SVM

To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.

A Real-Time Integrated Face Mask Detector to Curtail Spread of Coronavirus

Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral and limited medical resources,many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources.Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals.Regardless of discourse on medical resources and diversities in masks,all countries are mandating coverings over nose and mouth in public areas.Towards contribution of public health,the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask.The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy.We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps.In addition,we also propose a bounding box transformation to improve localization performance during mask detection.The experiments are conducted with three popular baseline models namely ResNet50,AlexNet and MobileNet.We explored the possibility of these models to plug-in with the proposed model,so that highly accurate results can be achieved in less inference time.It is observed that the proposed technique can achieve high accuracy(98.2%)when implemented with ResNet50.Besides,the proposed model can generate 11.07%and 6.44%higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.

An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

Today,due to the pandemic of COVID-19 the entire world is facing a serious health crisis.According to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of COVID-19.The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places.Therefore,it is very difficult to manually monitor people in overcrowded areas.This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19.This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked.The proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces.Moreover,this work applies to real-time and can be implemented in any public service area.

Face Mask Recognition for Covid-19 Prevention

In recent years,the COVID-19 pandemic has negatively impacted all aspects of social life.Due to ease in the infected method,i.e.,through small liquid particles from the mouth or the nose when people cough,sneeze,speak,sing,or breathe,the virus can quickly spread and create severe problems for people’s health.According to some research as well as World Health Organization(WHO)recommendation,one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space.A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection.This simple method can reduce up to 95%of the spread of the particles.However,this solution depends heavily on social consciousness,which is sometimes unstable.In order to improve the effectiveness of wearing face masks in public spaces,this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask.The approach uses the deep learning technique that relies on GoogleNet,AlexNet,and VGG16 models.The results are synthesized by an ensemble method,i.e.,the bagging technique.From the experimental results,the approach represents a more than 95%accuracy of face mask recognition.