Cough Detection for Prevention Against the COVID-19 Pandemic

This paper proposes a novel and robust technique for remote cough recognition for COVID-19 detection. This technique is based on sound and image analysis. The objective is to create a real-time system combining artificial intelligence (AI) algorithms, embedded systems, and network of sensors to detect COVID-19-specific cough and identify the person who coughed. Remote acquisition and analysis of sounds and images allow the system to perform both detection and classification of the detected cough using AI algorithms and image processing to identify the coughing person. This will give the ability to distinguish between a normal person and a person carrying the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Building a Real-Time System to Monitor Students Electronically Based on Digital Images of Face Movement

The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.

Design of an Intelligent Educational Evaluation System Using Deep Learning

Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario. © 2013 IEEE.

A study of Real-Time Non-invasive Detection Techniques to Identify COVID-19 contagious person

The whole world has witnessed the global pandemic situation caused and hampered very badly due to COVID-19. We had seen the adverse effect globally, in terms of health, economy, social lifestyle. So, it's an urgent need to find a rapid detection technique/test to avoid the spread of the virus. The most effective and world-wide accepted detection method of COVID-19 is the RT-PCR. But due to its slow detection time and False-negative rates, researchers and scientists are trying different detection methods such as use of GC-MS, E-nose, Electrochemical method, use of nanomaterial-based sensor arrays. But all these have limitations in terms of real time sensing, detection time, sample preparation, etc. In order to overcome said drawbacks and to get real-time analysis, we are proposing a concept for COVID-19 detection based on the reported literature. As per recent advancement researchers have evident the presence of VOCs in COVID-19 infected person's breath by GC-MS method. A real time system is very much necessary to detect the VOCs in the Exhaled breath of the COVID-19 infected person to minimize the burden of healthcare system. In this article we will discuss and propose the probable detection techniques for real time sensing of the VOCs presence in the Exhaled breath of the COVID-19 infected person. © Published under licence by IOP Publishing Ltd.

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. © 2005-2012 IEEE.

Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. IEEE

Convolution Neural Network Based Covid -19 Face Mask Detection and Recognition

The coronavirus is devastating global health. Ac- cording to WHO guidelines, wearing a mask and keeping a 6-foot distance between people can help to prevent the spread of COVID 19. As a condition of the international COVID-19 outbreak, protective equipment, the most vital of which is a face mask, is required. Wearing a face mask in public is a good way to be safe. This project seeks to develop a real-time, GUI- based face detection and identification system using machine learning. Tensor Flow, Keras, Scikit-learn, and Open CV are used to develop a Convolutional Neural Network (CNN) model to make the technique as accurate as possible. Principal Component Analysis (PCA) and the HAAR Cascade Algorithm are two components of the proposed methodology. If the person in front of the camera is wearing a mask, the classification algorithm's result will be displayed by a green rectangle overlaid around the region of the face;otherwise, it will be represented by a red rectangle superimposed around the area of the face. © 2022 IEEE.

Socially Distanced Games: Exploring the Future Opportunities of Remote Play

Playing games with friends and family provided a way to stay connected and deal with isolation during the COVID-19 pandemic. However, restrictions introduced to co-located events affected how both regular and casual players scheduled, organised, participated and engaged with various games. Through an online survey, we aimed to gain preliminary insights into how the swift switch from physical to remote play - forced by the circumstances - impacted the gameplay experiences and how different players potentially changed their playing habits. Our preliminary results suggest that computer-mediated communication systems successfully allowed the translation of co-located game sessions, but also highlight the emergence of different points of player friction during remote game experiences, e.g., the tediousness of scheduling and setup, miscommunication or playmates' wellbeing. We discuss future research and design opportunities that explore the potential to augment social game experiences at a distance and debate the future of remote or hybrid play. © 2022 Owner/Author.

Automatic Monitoring and Control System for Different Water Levels in Interconnected Multiple Twin Tanks with Dry Run Protection Using Microprocessor Orange Pi

The proposed system effectively controlled and monitored the water level of the dual tank system with efficient dry-run protection to prevent the motor from burning out and avoid wastage of electricity in case of no water. An Orange Pi was used to test its working and ability to control and monitor a real-Time system as no prior research is done on a dual water tank control system using Orangepi SBC. Due to COVID-19, millions of people's financial condition has worsened, which is why an initiative is taken to use the cheap board for making prototypes and moving towards PLCs after the desired outcomes. This research aims to provide a system at cheaper rates to handle large water tanks. This system is very efficient and valuable in dams, tanks, purifiers, and water containers. The proposed system also has dry run protection to avoid wastage of electricity in case of no water. The research is an advancement in automation in real-Time virtual monitoring and different water level control systems. This research makes life more comfortable because real-Time monitoring with a water control system reduces water wastage, leading to the complete modern solution of the problems. © 2022 IEEE.

Social Distance Monitoring to Enhance the Safety from COVID-19 Infection

The best precaution in the COVID-19 pandemic period is social distancing. Despite being aware of these, people often violate social distance and take down their masks in public places, knowingly or unknowingly. This unawareness is the primary reason for public places like retail shops, bus stops, public transport, educational institutions, hospitals, and clinics to become increasingly inaccessible and COVID-19 hotspots. It would be practically impossible for anyone to make sure everyone is following social distancing in a public place;a human will not be able to see beyond his field of view. An automated CCTV-based real-time system could help in monitoring the crowd. The proposed model takes a video stream as input, segments people using a CNN-object detection algorithm (YOLO), anchors objects' locations, and finds the distance between them. The system then marks bounding boxes around people who do not follow social distancing. Using pixel coordinates of violating people can accurately mark and alert security personnel or display wherever social distancing is violated. © 2022 IEEE.

Facial mask detection system based on YOLOv4 algorithm

This paper mainly addresses the detection of facial mask wear under the new COVID-19. To meet this demand, this paper performs facial mask wear detection on specific targets through a model trained based on the YOLOv4 algorithm. It has the characteristics of fast detection and light weight, and the application of this system to daily mask wear detection requires high real-time system performance. YOLOv4 meets this requirement, so the system designed based on this model has practical significance. This paper further demonstrates that the facial mask detection system designed based on the YOLOv4 algorithm is capable of working in multiple scenes of daily life, successfully detecting whether the target is wearing a mask in many scenes such as routine, multi-person and occlusion environment. © 2022 IEEE.

Game Theory-inspired Novel Mechanism for Assessing Healthcare Quality

Healthcare is the most pivotal domain of every nation. With the sudden upraise of the COVID-19 pandemic, there has been a major concern for the healthcare industry to provide quality medical services to the common people. Vulnerable healthcare conditions have proven to be fatal for the patients. Conspicuously, it has become indispensable to assess the quality of healthcare services provided by the hospitals. The current paper focuses on analyzing healthcare service quality delivered by the hospitals and healthcare centers. Specifically, the presented framework utilizes Internet of Things (IoT) technology to acquire real-time ambient data inside smart hospitals. The quantification of the healthcare service is performed using Probability of Health Grade (PoHG) to classify data segments using the Probabilistic Bayesian Belief Model. Furthermore, the temporal data ion is performed for the numerical analysis of healthcare service quality in terms of the Health Quality Index (HQI). Finally, a 2-player game theory-inspired decision modeling is performed to analyze healthcare quality in a time-sensitive manner. The proposed framework is assessed using a simulated environment where 225,325 data segments are analyzed. Results are compared with state-of-the-art techniques in which enhanced performance measures are registered in terms of Classification Efficacy (93.74%), Decision-Making Efficiency (Coefficient of Determination (95%), Accuracy (97.53%), Mean Square Error(2.01%)), Root Mean Square Error(1.95%)), Temporal Delay (96.62s), and Reliability(91.58%). IEEE

Use of Interactive Multimedia to Support Meaningful Learning for Vocational High School Students in the Pandemic Era

The covid-19 pandemic has changed the entire life order of government and society. Including also having an impact on vocational high schools, learning that was originally carried out in schools now has to take place with distance learning. This study examines the effect of interactive multimedia to enhance meaningful learning in the context of technical and vocational education and training schools. This research method uses a literature review, namely by finding sources that are relevant to the object of research. Even though learning is currently being carried out using distance learning, of course, teachers are still required to provide quality learning to achieve meaningful learning. The limitations of distance learning are a challenge for vocational high schools because the implementation of learning in vocational high schools is mostly carried out by work practices both in schools, places of business, and industry. Thus, the development and use of educational technology become important. One of the existing technologies is interactive multimedia. Interactive multimedia is a learning media that combines text, animated video, images, sound, and evaluation in learning media. © 2021 ACM.

Needs Analysis of Mathematics Digital Media Development to Improve Critical Thinking Skills in Elementary School Students

The purposes of this study are 1) to obtain a description of the difficulties experienced in learning elementary school mathematics, 2) to obtain a description of the use of learning media in the mathematics learning process in the field, and 3) to formulate learning media that need to be developed in learning mathematics. The type of research used is qualitative research. Information and data collection techniques in the form of distributing questionnaires, interviews, observations, and documentation. Data analysis through data reduction, data display, and conclusion drawing. The results of collecting information concluded that 1) the problems faced in elementary school mathematics learning include the delivery of material according to student development, some math material such as multiplication and students' critical thinking skills;2) the use of learning media in the mathematics learning process in the field during the COVID-19 pandemic is still not optimal and digital media needs to be developed;3) The media formulation needed to be developed is in the form of interactive multimedia according to the development of elementary school students regarding multiplication material that has a nurturant affect the formation of students' positive characters such as spiritual and social attitudes. © 2021 ACM.

Students' View of Using Mobile Based Interactive Multimedia with Contextual Teaching and Learning Approach in Chemistry Learning

The COVID-19 pandemic has had an impact on the education sector, especially in the learning process, which was originally face-to-face, now must be carried out online. Thus, teachers need to develop appropriate learning strategies that are supported by appropriate learning media as well. The aim of this study is to determine the perceptions of senior high school students about the use of mobile based interactive multimedia with contextual teaching and learning approach in chemistry learning. This study used a descriptive research design with a survey method to the students of tenth grade science students of Pradita Dirgantara high school that were randomly selected as research sample. The questionnaire was distributed to the sample as a data collection technique, and then the results were analyzed quantitatively with a percentage. The results of this study showed that teachers have used learning media in Chemistry learning activities that are in accordance with the topics being taught with often frequency. The types of learning media that are often used in learning are web-based media, sometimes with conventional media, and sometimes with mobile-based media. In addition, students' perceptions regarding the use of mobile-based learning media include the use of learning media have a positive influence and impact on students, because students can more easily understand lessons, students feel not bored and are not afraid to learn, are more motivated to learn, and become more diligent in studying Chemistry. © 2021 ACM.

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as a sensing devices can be useful in detecting overcrowding events. Accordingly, in this paper, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on Deep Transfer Learning (DTL). According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. IEEE

Care Workers Making Use of Robots: Results of a Three-Month Study on Human-Robot Interaction within a Care Home

Research on social robots in care has often focused on either the care recipients or the technology itself, neglecting the care workers who, in and through their collaborative and coordinative practices, will need to work with the robots. To better understand these interactions with a social robot (Pepper), we undertook a 3 month long-term study within a care home to gain empirical insights into the way the robot was used. We observed how care workers learned to use the device, applied it to their daily work life, and encountered obstacles. Our findings show that the care workers used the robot regularly (1:07 hours/day) mostly in one-to-one interactions with residents. While the robot had a limited effect on reducing the workload of care workers, it had other positive effects, demonstrating the potential to enhance the quality of care. © 2022 Owner/Author.

Tangible Interaction for SupportingWell-being

Our workshop aims to bring together researchers and practitioners across disciplines in HCI who share an interest in promoting well-being through tangible interaction. The workshop forms an impassioned response to the worldwide push towards more digital and remote interaction in nearly all domains of our lives in the context of the COVID-19 pandemic. One question we raise is: to what extent will measures like remote interaction remain in place post-pandemic, and to what extent these changes may influence future agendas for the design of interactive products and services to support living well? We aim to ensure that the workshop serves as a space for diverse participants to share ideas and engage in cooperative discussions through hands-on activities resulting in the co-creation of a Manifesto to demonstrate the importance of embodied and sensory interaction for supporting well-being in a post-pandemic context. All the workshop materials will be published online on the workshop website and disseminated through ongoing collaboration. © 2022 Owner/Author.

Application of Deep Learning in Detection of Covid-19 Face Mask

With the increase in the number of Covid-19 cases throughout the globe wearing face masks has proved to be effective in the prevention of the virus. In this work, we have originated a method that can detect if people are violating the rule of wearing a mask outdoors using a two-stage deep learning system. The first stage of the system detects different faces present in the input image using YOLO (You Only Look Once) model trained for the face detection and returns face ROIs. In the second stage extracted face ROI is passed through face mask detector model trained using MobileNetV2 which in turn classifies it as Mask or No mask. The dataset used for training the mask detector model is Real-World Masked Face Dataset (RMFD) and for Face Detection model is the WIDER dataset. The proposed method gives 98% accuracy for mask detection. The promising results derived from the proposed model demonstrate that the deployment of the model can be done in real-time systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.