"I Won't Go Speechless": Design Exploration on a Real-Time Text-To-Speech Speaking Tool for Videoconferencing

The COVID-19 pandemic has shifted many business activities to non-face-to-face activities, and videoconferencing has become a new paradigm. However, conference spaces isolated from surrounding interferences are not always readily available. People frequently participate in public places with unexpected crowds or acquaintances, such as cafés, living rooms, and shared offices. These environments have surrounding limitations that potentially cause challenges in speaking up during videoconferencing. To alleviate these issues and support the users in speaking-restrained spatial contexts, we propose a text-to-speech (TTS) speaking tool as a new speaking method to support active videoconferencing participation. We derived the possibility of a TTS speaking tool and investigated the empirical challenges and user expectations of a TTS speaking tool using a technology probe and participatory design methodology. Based on our findings, we discuss the need for a TTS speaking tool and suggest design considerations for its application in videoconferencing. © 2023 ACM.

Demo Abstract: Implementing a COVID-19 Risk Assessment System for User-Informed Travel Planning in Harris County

The COVID-19 pandemic has forced people worldwide to modify their daily activities, including travel plans. To help individuals make informed decisions about visiting public places, Cheng [2] first proposed a real-time COVID-19 risk assessment system called RT-CIRAM and implemented prototypes for two U.S. metropolitan locations. The system calculates a COVID-19 risk score and categorizes the risk levels into high, medium, and low, recommends the safe travel destination using the users' location and the specified distance the user is willing to travel, thereby helping users make informed decisions about their travel plans. © 2023 ACM.

Facemask detection using Deep Learning

COVID-19 was raised in the year 2020 which became more dangerous to society. According to the medical results, 100 million confirmed cases and 6 million deaths. This virus became an obstacle to gathering people in public places. This virus has spread all over the world. So, the Government has implemented a facemask policy to prevent the hazardous virus. It is a very difficult task to observe manually in crowded places. Most people are not wearing facemasks properly in public a place which causes the increase of the virus. So, the proposed model will detect the face mask whether the people are wearing it or not. By using, the HAAR-CASCADE technique we can able to detect whether the people are wearing the mask or not. By using this algorithm, we can able to prevent affecting of the virus to the person. This algorithm works effectively for detecting facemasks. The system compares faces with masks and faces without the mask. If people are not wearing a mask, the system detects through the camera and alerts by the alarm sound. The experiment results show the proposed technique achieves a 95% accuracy rate. © 2023 IEEE.

Performance of COVID Face Mask Detection Based on SVM Algorithm Using OpenCV DNN as Preprocessing Model

As COVID-19 became a pandemic in the world, wearing a mask has become one of the best measures to prevent the spread of the epidemic, so face mask recognition in public places has become a very important part of controlling the epidemic. This paper mainly tests the performance of the OpenCV DNN preprocessing model (OpenCV DNN + SVM) based on the SVM algorithm model in the face mask recognition dataset. The dataset I use is from Kaggle called COVID Face Mask Detection Dataset. This dataset contains 503 face images with masks and 503 face images without masks. I test the performance of using OpenCV DNN + SVM and using only the SVM algorithm to evaluate this study by setting a control experimental group. In this study, it was found that using OpenCV DNN + SVM, the accuracy of ROI parameters and SVM parameters can reach 93.06% and F1score can also reach 93.06% without a lot of adjustment. The accuracy rate can only reach 68.31%, and the F1score reaches 68.31%. Findings suggest that the method using OpenCV DNN + SVM can achieve slightly better results in the COVID Face Mask Detection Dataset, and can perform better than only using the SVM algorithm. In addition, using OpenCV DNN preprocessing model based on the SVM algorithm plays an important role in feature extraction in face mask recognition. If the developer does enough parameters tuning, the accuracy will also increase. © 2022 SPIE.

Design of People Count System Based on Object Detection Algorithm

At present, the Covid-19 epidemic is still spreading globally. Although the domestic epidemic has been well controlled, the prevention and control of the epidemic must not be taken lightly. Being able to count the number of people in public places in real time has played a vital role in the prevention and control of the epidemic. Deep learning networks usually cannot be directly deployed on embedded devices with low computing power due to the huge amount of parameters of convolutional neural networks. This article is based on the YOLOv5 object detection algorithm and Jetson Nano embedded platform with TensorRT and C++ accelerating, it can realize the function of counting the number of people in the classroom, on the elevator entrance, and other scenes. © 2022 SPIE.

Technology-Driven Implementation of Smart Entrances in Public Places during the COVID-19

This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 2023 IEEE.

COVID-19 SOP Compliance and Monitoring Electronic System for Business and Public Places Using Arduino Uno

COVID-19 Corona virus disease is a rapidly spreading contagious disease that is causing a global public health crisis. In December 2019, the coronavirus was identified in Wuhan, China. COVID-19 is causing severe disease issues and many people are losing their lives daily. SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) is a severe infectious disease that is spreading very fast and is currently inflicting a healthcare crisis across the globe. The lethal coronavirus was founded in Wuhan, China in December 2019. The symptoms of this disease are fever, cough, fatigue, no taste or smell, stinging throat, headache, and difficulty in breathing. This deadly disease, COVID-19, is difficult to identify and spread. The vaccination process is still going on around the world. There are some existing strategies to minimize the spread of the COVID-19 virus by monitoring the temperature rise using sensors, wearing masks, and sanitizing their hands frequently. The proposed system comprises of an RFID reader, an IR sensor, a temperature sensor, a buzzer, a laptop or a personal computer with a web cam. A person on entry gets detected for their body temperature, wearing a face mask and then sanitizing their hands. If the temperature of the person is below 37.6 degrees, i.e., below the acceptance limit, then mask detection takes place by using MATLAB followed by spraying the sanitizer. Now the door will open automatically. Otherwise, the door will not open and the buzzer will sound. With these precautionary steps, people can survive this pandemic situation. © 2023 IEEE.

COVID-19 Face Mask Classification Using Deep Learning

The COVID-19 pandemic has triggered a global health disaster because its virus is spread mainly through minute respiratory droplets from coughing, sneezing, or prolonged close contact between individuals. Consequently, World Health Organization (WHO) urged wearing face masks in public places such as schools, train stations, hospitals, etc., as a precaution against COVID-19. However, it takes work to monitor people in these places manually. Therefore, an automated facial mask detection system is essential for such enforcement. Nevertheless, face detection systems confront issues, such as the use of accessories that obscure the face region, for example, face masks. Even existing detection systems that depend on facial features struggle to obtain good accuracy. Recent advancements in object detection, based on deep learning (DL) models, have shown good performance in identifying objects in images. This work proposed a DL-based approach to develop a face mask detector model to categorize masked and unmasked faces in images and real-time streaming video. The model is trained and evaluated on two different datasets, which are synthetic and real masked face datasets. Experiments on these two datasets showed that the performance accuracy rate of this model is 99% and 89%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Crowd Size Estimation and Detecting Social Distancing using Raspberry Pi and OpenCV

In this covid19 pandemic the number of people gathering at public places and festivals are restricted and maintaining social distancing is practiced throughout the world. Managing the crowd is always a challenging task. It requires monitoring technology. In this paper, we develop a device that detects and provide human count and detects people who are not maintaining social distancing. The work depicted above was finished using a Raspberry Pi 3 board with OpenCV-Python. This method can effectively manage crowds. © The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/), which permits use, distribution, and reproduction in any medium, provided that the Article is properly cited.

Covid-19 crowd detection and alert system using image processing

In this paper, we aim to help in identifying the people that are violating social distancing norms set by the government (necessary during the COVID-19 pandemic in public places), by providing an efficient real-time deep learning-based framework to automate the process of monitoring the social distancing via object detection and tracking approaches. Our system is divided into two subsystems: one that deals with crowd detection and control, and the other that sends information to the police authorities. Our system technologies, including as IoT, image processing, web cams, BLE, OpenCV, and Cloud, are being considered for inclusion in the proposed framework. The image processing is divided into two sections, the first of which is the extraction of frames from real-time movies, and the second of which is the processing of the frame to determine the number of individuals in the crowd. Even in a crowd, dissemination may be restricted if people adhere to social distancing standards. As a result, the image processing model primarily targets the number of people who do not adhere to social distancing norms and stand too close together. © 2023 IEEE.

Real-time Face Mask Detection and Contactless Body Temperature Measurement on Edge Device Using Deep Learning

The world is severely affected by COVID-19 disease and it has became a threat to everyone. One of the effective methods to prevent infection of this disease is to wear a face mask in public places. The body temperature of a person is an important indicator of COVID-19 infection. Many public places or services give entry to the people only if they wear a mask and have body temperature in a normal range. In areas like college labs, internet cafes and malls, they keep a daily log of visiting persons with details such as name, date, body temperature etc. In this work, a system is proposed that can be utilized to remind people to wear a face mask and monitor them. It can also measure body temperature using an IR temperature sensor and alert respective authorities if it is high. In the proposed system, convolutional neural network MobileNetV2 is used for face mask detection deployed on NVIDIA Jetson Nano. © 2022 IEEE.

A more accurate mask detection algorithm based on Nao robot platform and YOLOv7

With the global outbreak of Corona Virus Disease 2019(COVID-19), many countries had made it mandatory for people to wear masks in public places. This paper proposed a novel mask detection algorithm RMPC (Restructing the Maxpool layer and the Convolution layer)-YOLOv7 based on YOLOv7 for detecting whether people wear masks in public places. The RMPC-YOLOv7 algorithm reconstructed the downsampling structure in the original YOLOv7 algorithm. We changed the stacking of the maxpooling layer and the convolutional layer. This enabled the feature information to be fully integrated to achieve the accuracy improvement of the new model. Through comparison experiments, our proposed RMPC-YOLOv7 had was improved 0.9% and 1.2% for mAP0.5 and mAP0.5:0.95, respectively. The experimental results demonstrated the feasibility of RMPC-YOLOv7. © 2023 IEEE.

Face Mask Detection using Single Shot Multibox Detector and Mobile Net

During the pandemic time government took many safety measures to protect the public at common gathering places. People are insisted on wearing a face mask to protect themselves from COVID. Even then many people were roaming without a mask in public places. The proposed technique to detect the face mask is to identify the person's face with mask and person's face without mask and reporting to the safety officers about the persons without mask for further action. The proposed Face mask detection is developed using the ML technique which can be used to classify the people wearing masks and not wearing masks with the input given to the model. The proposed face mask detector is a one-stage detector that focuses on detecting the face mask alone. This work is implemented using the Tensor flow and Computer vision libraries. NumPy is used for image processing. The data set used in MAFA dataset. The model is trained using this data set to get the accurate results. To enable multiple detection here the single shot with multi box detector is used. The base model used for this process is Mobile Net V2. The proposed model is simple and it can be integrated with several other technologies to provide high accuracy percentage of output in the minimum possible time. © 2022 IEEE.

Contactless Fog based Handwash Kit for COVID Safety

Recent days have changed tremendously, and rules are strictly being deployed to maintain social distancing, avoid crowding and frequent hand washing. Frequent washing of hands using our domestic water by a mass crowd result in water wastage which is a huge loss for our society. A better solution to sanitize the hands with reduced water wastage is attempted in this study. With technological advancements in engineering, several solutions and cope-up methods are being given to combat the spread of COVID-19 in this pandemic era. As an attempt, this study develops a Fog based Contactless Handwash kit which uses the Mis Spray method to sanitize the hands. The mist consists of water vapour and herbal sanitizer which is skin-friendly to humans. This kit is suggested to be deployed in public places to avoid the spreading of the virus since it is in a complete contactless manner. It is developed with an Atmega based microcontroller, NodeMCu,ultrasonic sensor and mist spray module economically. The outcomes of the developed handwash kit serve to optimally favour the preventing behaviour in this pandemic time. This study gives way for further research studies on the automatic sanitizing methods to combat the spread of the virus and its variants. © 2022 IEEE.

Implementation of Social Distancing Device

As the continued COVID-19 outbreak has brought about an international catastrophe with spreading. Due to the absence of powerful remedial sellers and the lack of immunizations in opposition to the virus, populace vulnerability increases. Therefore, social distancing is a concept to be a good enough precaution in opposition to the pandemic virus. As discussed, this paper present and discuss the ideology of how to provide automated social distancing by measuring the distance between the user and the opposite person and monitoring the temperature of the user, alarming them when social distancing is violated and also if the temperature is raised more than a normal body temperature by using a microcontroller named Arduino with ultrasonic sensor and PIR sensor as a detecting medium. To achieve this objective, we have developed a small wrist device that can help users to be safe in public places, crowded areas, etc., and also easier to wear and use as it is like a smartwatch. Detailed processes and work are shown in this paper. © 2022 IEEE.

Theory and Simulation of Multipurpose Antenna for Detection and Degrading of Viruses in Times of Pandemic

This paper present a theoretical model that aims to minimize the capabilities of viruses in public places through engineered electromagnetic fields. Thus, the modeling of antenna based at the infinitesimal dipole is used. In addition fields and directivity at the far field region are calculated. This proposal empathizes the fact that the radiated energy will affect the spike protein of viruses. In this manner the functionality of virus as to produce infection would be minimized. Simulations of the radiate electric field are presented. © 2022 IEEE.

Use of E-Wallet as a Substitute for Physical Money in Transactions at Malls

Several challenges have emerged as a result of the rapid spread of Covid-19 in Indonesia. To combat the pandemic, the government has also issued general guidelines for avoiding/limiting direct contact with common people who do not live with them. However, in some unavoidable cases, such as cash transactions, the social distancing has not yet been followed properly. These type of challenges can be avoided by the introduction of e-wallets and e-money. This research study examines the transactions made using e-wallets and e-money and how these methods are increasing sales in public places. The proposed research study has attempted to utilize both qualitative and a quantitative method to observe the customer behavior while using e-wallet as a substitute for physical money when making a transaction in a shopping mall. The result of the proposed research work will be compared with the existing literature to provide a conclusion based on existing research data and literature. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Research on Mask Wearing Recognition Based on Deep Learning

Since the outbreak of COVID-19 epidemic, research results have shown that the COVID-19 transmitted by droplets, and the most effective means of epidemic prevention is to wear masks. In public places where crowds gather, it is particularly important to use technical means to detect the situation of wearing masks, and remind people to wear masks in time to prevent cross-infection. This paper mainly starts with the target detection and tracking technology in the field of computer vision, and takes the recognition of whether to wear a mask as the entry point. Using python as the development tool, based on the convolutional neural network, the YOLOv2 algorithm is used as the core algorithm, and the ResNet50 network structure is built. Compared with other existing system test experiments, we can see that the system we built has better detection performance. © 2022 Association for Computing Machinery.

Social Distancing Monitoring and Alerting System using YOLO Deep Learning Algorithm

The Covid-19 disease is caused by the severe acute respiratory (SAR) syndrome coronavirus-2 and becomes the reason for the Global Pandemic since 2019. Until July 2022, the total reported cases were 572 million and reported deaths were 6.38 million around the world. In many countries the infections caused severe damages. It not only took the precious lives but also caused few other national damages like economic crisis. The only solution to stop this pandemic is to increase the vaccination and reducing the spreads. The covid 19 virus is an airborne disease and spread when people breathe virus contaminated air. The WHO and all the nations were insisting to maintain social distance to control the virus spreading. But maintaining the social distance in public places is very hard. In this project we developed a method for detecting social distance. The system uses Raspberry Pi processor to detect the distance between two people from the live video stream. The YOLOv3 technique is used to detect the object from single frame of the video. © 2022 IEEE

Population Index and Analysis Based on Different Geographies, Using Distance Measurement, Social Distancing, and Deep Learning

Nowadays, every individual is familiar with the COVID-19 pandemic which has caused great turmoil in everyone's life. Also, they are aware that there is no medicine or drug to cure COVID immediately, and people are at the risk of losing their lives. Lack of vaccines or delay in vaccine production for mass results social distancing being the only measure to tackle this pandemic. As a result, social distancing has proven to be a very reliable and efficient way to diminish the growth of this disease;the reason why lockdowns are imposed, and people are asked to keep some distance from each other, for their safety as there will be minimal physical contact. Machine learning and artificial intelligence come into the picture in every solution to a generic problem the community faces nowadays like in medical, supply chain management, face detection, etc. Using the power of AI algorithms, the paper aims to develop a robust system to monitor and analyze social distance measurement protocols at public places during the COVID-19 pandemic with the help of CCTV feed and check whether they abide by the safety protocols or not by measuring the distance between them. The proposed approach is implemented to enumerate the number of violations at a popular public place to prevent massive crowds at particular periods. The proposed method is suitable to construct a scrutiny system at a public place to alert people and eschew mass gatherings that can be concluded using achieved results. The paper also has an analysis of the performance of different models of R-CNN, Fast R-CNN, and YOLO. YOLO architectures are validated based on object detection and object tracking rate in real time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.