scholarly journals Smart solution for reducing COVID-19 risk using internet of things

2022 ◽  
Vol 25 (1) ◽  
pp. 474
Author(s):  
Akshay Rajeshkumar ◽  
Senthilkumar Mathi
Keyword(s):  
Internet Of Things ◽  
Health Assessment ◽  
Face Mask ◽  
Health Screening ◽  
Smart Device ◽  
Public Places ◽  
Face Images ◽  
Health Authorities ◽  
The Face ◽  
The Government

The article exposes a smart device designed for mitigating the coronavirus disease (COVID-19) risk using the internet of things. A portable smart alerting device is designed for ensuring safety in public places which can alert people when the guidelines given by the government were not followed and alert health authorities when any abnormalities found. By doing so, the spread of this fatal disease can be stopped. The modules of the proposed system include the face mask detection module, social distance alerting module, crowd detection and analysis module, health screening module and health assessment module. The proposed system can be placed in any public entrances to monitor people without human intervention. Firstly, the human face images are captured for face mask check, then the crowd analysis of the particular entrance where the person is entering is performed, thereafter health screening of the person is done and the values were imported to the health assessment module to check for any abnormalities. Finally, after all the conditions were met the door is opened automatically. The smart device can be installed and effectively used in many scenarios such as malls, stores, crowded places and campuses to avoid the risk of spread of the coronavirus.

scholarly journals Smart Door / COVID-19 Face Mask Detection

2021 ◽  
Vol 10 (9) ◽  
pp. 87-92
Author(s):  
Pavan Narayana A ◽  
◽  
Janardhan Guptha S ◽  
Deepak S ◽  
Pujith Sai P ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Open Source ◽  
Face Mask ◽  
Young Lady ◽  
Grayscale Image ◽  
Human Face ◽  
Happy Life ◽  
Public Areas ◽  
The Face ◽  
Human Faces

January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether who are wearing or not wearing mask. By using different technologies such as Open CV, MTCNN, CNN, IFTTT, ThingSpeak we have designed this face mask detection. We use python to program the code. MTCNN using Viola- Jones algorithm detects the human faces present in the screen The Viola-Jones algorithm first detects the face on the grayscale image and then finds the location on the colored image. In this algorithm MTCNN first detects the face in grayscale image locates it and then finds this location on colored image. CNN for detecting masks in the human face is constructed using sample datasets and MobileNetV2 which acts as an object detector in our case the object is mask. ThingSpeak is an open-source Internet of things application used to display the information we get form the smart door. This deployed application can also detect when people are moving. So, with this face mask detection, as a part to stop the spread of the virus, we ensure that with this smart door we can prevent the virus from spreading and can regain our happy life.

scholarly journals Realtime Face-Mask Detection on Raspberry Kit

2021 ◽  
Vol 9 (8) ◽  
pp. 2076-2083
Author(s):  
Kalyan Chakravarthi. M
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Real Life ◽  
Face Mask ◽  
Recognition System ◽  
Primary Concern ◽  
Public Places ◽  
Life Problems ◽  
Promising Area ◽  
The Face

Abstract: Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. Still, difficulties created by masks are usually disregarded. Face recognition is a promising area of applied computer vision . This technique is used to recognize a face or identify a person automatically from given images. In our daily life activates like, in a passport checking, smart door, access control, voter verification, criminal investigation, and many other purposes face recognition is widely used to authenticate a person correctly and automatically. Face recognition has gained much attention as a unique, reliable biometric recognition technology that makes it most popular than any other biometric technique likes password, pin, fingerprint, etc. Many of the governments across the world also interested in the face recognition system to secure public places such as parks, airports, bus stations, and railway stations, etc. Face recognition is one of the well-studied real-life problems. Excellent progress has been done against face recognition technology throughout the last years. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. The occluded face detection problem has been approached using Cascaded Convolutional Neural Network (CNN). Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Finally, a correlative study also made here for a better understanding.

scholarly journals Covid-19 Mask Detection

2021 ◽  
Vol 10 (2) ◽  
pp. 1319-1323
Keyword(s):  
Face Mask ◽  
Classification Model ◽  
World Health ◽  
Live Streaming ◽  
Public Places ◽  
The World ◽  
The Face ◽  
Health Organization ◽  
The Right ◽  
Facial Images

The corona epidemic poses a global health problem and therefore effective preventive measures are worn in public places,according to the World Health Organization (WHO). The COVID-19 epidemic has forced governments around the world to impose restrictions on the transmission of the virus. Reports show that wearing the right face while in public places and at work clearly reduces the risk of transmission. An effective and economical way to use machine learning is to create a safe environment for device setup. A hybrid model using the depth of the face mask detection machine will be introduced. The face mask detection databasecontains a mask and in addition to the facial images, we will use OpenCV to perform real-time facial detection from live streaming via our webcam. We will use the database to create a COVID-19 face mask detector from a computer view using Python, OpenCV, and Tensor Flow and Cameras. We aim to determine whether the person in the picture/video is wearing a face mask or not with the help of computer vision and in-depth reading and to show the same with caution. Steps to modeling are data collection, pre-processing, data classification, model testing, and modeling

scholarly journals Face Mask Detection System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1400-1402
Author(s):  
Yatharth Khansali
Keyword(s):  
Real Time ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Public Places ◽  
The World ◽  
The Face ◽  
Time Requirements ◽  
Health Organization ◽  
Protective Mask

COVID-19 pandemic has affected the world severely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 176 million people causing over 3.8 million deaths. Wearing a protective mask has become a norm. However, it is seen in most public places that people do not wear masks or don’t wear them properly. In this paper, we propose a high accuracy and efficient face mask detector based on MobileNet architecture. The proposed method detects the face in real-time with OpenCV and then identifies if it has a mask on it or not. As a surveillance task, it supports motion, and is trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context.

scholarly journals Modelo para detectar el uso correcto de mascarillas en tiempo real utilizando redes neuronales convolucionales

2021 ◽  
Vol 9 (17) ◽  
pp. 111-120
Author(s):  
Hugo Andrade Carrera ◽  
Soraya Sinche Maita ◽  
Pablo Hidalgo Lascano
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Face Mask ◽  
Training Process ◽  
Public Places ◽  
The Neural Network ◽  
Great Performance ◽  
The Family ◽  
The Face ◽  
Mandatory Use

Since Covid-19 appeared, the world has entered into a new stage, in which everybody is trying to mitigate the effects of the virus. The mandatory use of face masks in public places and when maintaining contact with people outside the family circle is one of mandatory measures that many countries have implemented, such as Ecuador, thus, the purpose of this article is to develop a convolutional neural network model using TensorFlow based on MobileNetV2, that allows to perform mask detection in real time video with the key feature of determining if the person is using the face mask properly or if it is not wearing a mask, in order to use the model with OpenCV and a pretrained neural network that detects faces. In addition, the performance metrics of the neural network are analyzed, including precision, accuracy, recall and the F1 score. All performance metrics consider the number of epochs for the training process, obtaining as a result a model that classifies between three groups: faces without face mask, faces wearing a face mask improperly and faces wearing a mask properly. with a great performance in all metrics; The results show values greater than 85% for precision, recall and F1 score, and accuracy values between 93% for 5 epochs and 95% for 25 epochs.

scholarly journals Study of Electrostatic Field Responses of Locally Marketed Face Masks in the Philippines

2021 ◽  
Vol 2071 (1) ◽  
pp. 012055
Author(s):  
J Esmeria ◽  
P N Fernandez ◽  
G G Oyong
Keyword(s):  
Transmission Rate ◽  
Public Awareness ◽  
Face Mask ◽  
The Philippines ◽  
Pore Sizes ◽  
Small Pore ◽  
Different Types ◽  
The Face ◽  
Market Opportunities ◽  
The Government

Abstract The face mask is the first line of defense against infectious particulates and droplets that may cause illness. Currently in the Philippines, the wearing of face mask is compulsory whenever citizens leave their residences as mandated by the government to mitigate the spread of COVID-19. The wearing of face masks has become a new normal among Filipinos. This created market opportunities for different types which became commonly and immediately available for purchase. This study aimed to differentiate the effectiveness of locally available face masks in terms of electrostatic filtration capability. Twelve different types of face masks grouped into five categories – surgical, fabric, N95 variants, foam type, and novelty type – were evaluated. Electrostatic fields were measured from each face mask including pore sizes via scanning electron microscopy. Moreover, by utilizing the estimated charge and mass of the SARS-CoV-2 virion, the transmission rate was simulated using COMSOL Multiphysics®. It was observed that face masks with negatively charged materials combined with small pore sizes afforded less particle transmission. The results of this study are of timely significance in potentially laying out public awareness in the selection and utilization of face masks that can provide foremost shielding against viral transmission.

scholarly journals Face Mask Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1520-1523
Keyword(s):  
Deep Learning ◽  
Face Mask ◽  
Video Stream ◽  
Still Images ◽  
Railway Stations ◽  
Health Authorities ◽  
Safety And Health ◽  
Learning Techniques ◽  
The Face ◽  
The Right

Businessesare constantly overhauling their existing infrastructure and processes to be more efficient, safe, and usable for employees, customers, and the community. With the ongoing pandemic, it's even more important to have advanced applications and services in place to mitigate risk. For public safety and health, authorities are recommending the use of face masks and coverings to control the spread of Coronovirus. The COVID-19 pandemic is devastation to themankind irrespective of caste, creed, gender, and religion. Using a face mask can undoubtedly help in managing the spread of the virus. COVID-19 face mask detector uses deep learning techniques to successfully test whether a person is wearing a face mask or not. Using a deep learning method called Convolutional Neural Network, got an accuracy of 98.6 %. It can work with still images and also works with a live video stream. Cases in which the mask is improperly worn are when the nose and mouth are partially covered is considered as the mask is not worn. Our face mask identifier is the least complex in structure and gives quick results and hence can be used in CCTV footage to detect whether a person is wearing a mask perfectly so that he does not pose any danger to others. Mass screening is possible and hence can be used in crowded places like railway stations, bus stops, markets, streets, mall entrances, schools, colleges, etc. By monitoring the placement of the face mask on the face, we can make sure that an individual wears it the right way and helps to curb the scope of the virus

scholarly journals Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 107-121
Author(s):  
R Dhaya
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Face Mask ◽  
World Health ◽  
Survey Study ◽  
Identification System ◽  
Two Stage ◽  
Public Places ◽  
The World ◽  
The Face

The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's "ReLU" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.

scholarly journals Automatic Approach for Mask Detection Effective: For COVID-19

2021 ◽  
Author(s):  
Debajyoty Banik ◽  
Saksham Rawat Rawat ◽  
Aayush Thakur ◽  
Pritee Parwekar ◽  
Suresh Chandra Satapathy
Keyword(s):  
Region Of Interest ◽  
Face Mask ◽  
Working Environment ◽  
Public Places ◽  
Detection Model ◽  
Face Region ◽  
Sense Of Security ◽  
Improper Use ◽  
The Face ◽  
Dual Stage

Abstract The outbreak of Coronavirus Disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a Deep Learning-based system that can detect instances of improper use of face masks. A dual-stage Convolutional Neural Network (CNN)architecture is used in our system to recognie masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. This paper will automate the tasks of mask detection in public places when incorporated with CCTV cameras and will alert the system manager when a person without mask or wearing incorrect mask tries to enter. This paper includes multi face detection model which has the potential to target and identify a group of people whether they are wearing masks or not. We tried to collect various facial pictures and tried to identify the face Region of Interest (ROI), and then we separated it. Applying facial milestones, to permit the restriction the eyes, nose, mouth, and so. face was then completed and we tried to detect the presence of mask. To prepare a custom face cover locator, breaking our venture into two unmistakable stages was required, each with its own separate sub-steps. 1. Preparing: Here, stacking our face veil discovery dataset from plate, preparing a model on this dataset, and afterward serializing the face cover locator to circle was the focus. 2. Sending: Once the face veil identifier is prepared, the accompanying advance of stacking the cover finder, performing face recognition, and afterward characterizing each face as with veil or without veil, can be executed.

scholarly journals Covid-19: The Burden of a Pandemic and Effectiveness of Face Masks in Curtailing Its Spread

2021 ◽  
Vol 7 (1) ◽  
pp. 1-4
Author(s):  
Muyiwa Seun OLUWAFEMI ◽  
◽  
Ese Freeman OGHAGHARE ◽  
Ese Freeman OGHAGHARE ◽  
Bassey UDOFIA ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Health System ◽  
World Economy ◽  
Face Mask ◽  
The United States ◽  
Public Places ◽  
The World ◽  
The Government

The outbreak of Covid-19 presents an unprecedented threat to public health with a devastating effect on the world economy and health system. In March 2020, the government of the United States responded by adopting the use of face masks as one of the measures to prevent the spread of coronavirus in public places. The increase in the spread of coronavirus necessitated the need for researchers to evaluate the effectiveness of face masks as a measure to control the spread of coronavirus. It became of more concern when alternatives to face masks were observed in public places. This article reviewed factors that affect the effectiveness of face masks and the choice of an effective face mask as reported by several studies. The use of appropriate face masks and other measures to prevent the spread of coronavirus should be encouraged at all levels. Keywords: COVID-19, SARS-CoV-2, FACE-MASKS.

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