Design and Implementation of Intelligent Truck Based on Azure Kinect

In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

Smart Watch to Avoid Face Touching During Pandemic Condition

The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces. © 2023 IEEE.

Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards.

Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Face Recognition Attendance System

The conventional procedure used in all of India's major regions is attendance monitoring on paper with pens. Although the final data is computerized, it takes a long time to get from a classroom to a database. The effectiveness of the classes is directly impacted by the number of absences. The attendance takes up almost half of the lecture's allotted time. The alternative method that is being used involves using fingerprints, but even this approach is ineffective since it takes so long. Due to the illnesses (COVID-19) spreading over the world, however, the situation as it stands right now does not make this the best course of action. Therefore, it will be advisable to develop a contactless and more efficient. © 2023 IEEE.

Masked Face Recognition Model with Explainable AI

Due to the recent COVID-19 pandemic, people tend to wear masks indoors and outdoors. Therefore, systems with face recognition, such as FaceID, showed a tendency of decline in accuracy. Consequently, many studies and research were held to improve the accuracy of the recognition system between masked faces. Most of them targeted to enhance dataset and restrained the models to get reasonable accuracies. However, not much research was held to explain the reasons for the enhancement of the accuracy. Therefore, we focused on finding an explainable reason for the improvement of the model's accuracy. First, we could see that the accuracy has actually increased after training with a masked dataset by 12.86%. Then we applied Explainable AI (XAI) to see whether the model has really focused on the regions of interest. Our approach showed through the generated heatmaps that difference in the data of the training models make difference in range of focus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on Chest X-Ray Image Diagnosis of Covid-19 Based on Improved ResNet

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.

Masked Student Dataset of Expressions

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://github.com/SridharSola/MSD-E. © 2022 ACM.

Glucosaminylmuramyl dipeptide efficacy in post-COVID-19 patient rehabilitation treatment.

The COVID-19 pandemic has altered people's lifestyles around the world. Prevention of recurrent episodes of the disease and mitigation of its consequences are especially associated with effective post-COVID-19 rehabilitation in patients. The aim of the study was to evaluate the effects of the drug Likopid (glucosaminylmuramyl dipeptide, GMDP) for post-COVID-19 rehabilitation in patients. Material and methods. Patients who recovered from mild to moderate COVID-19 (n=60, mean age 54+/- 11.7 years) were randomized into the observation group (n=30, 15 men and 15 women) who received 2 courses of Licopid (1 mg twice a day) and the comparison group (n=30, 15 men and 15 women). Analysis of the phenotypic and functional characteristics of the innate immune cellular factors was carried out before the start of immunomodulatory therapy, immediately after the end of the course, and also after 6 months observations. In order to assess the quality of life of all patients, we used the SF-36 Health Status Survey and the Hospital Anxiety and Depression Scale questionnaires. Results. During assessing the effect of immunomodulatory therapy on the parameters of innate immunity of patients at the stage of rehabilitation after COVID-19, an increase in the protective cytolytic activity of CD16+ and CD8+Gr+ cells, as well as a persistent increase in TLR2, TLR4 and TLR9 expression was found, which indicates the antigen recognition recovery and presentation at the level of the monocytic link of the immune system. The use of GMDP as an immunomodulatory agent resulted in an 8-fold reduction in the frequency and severity of respiratory infections due to an increase in the total monocyte count. As a result of assessing patients' quality of life against the background of the therapy, a positive dynamic in role functioning was revealed in patients. In the general assessment of their health status, an increase in physical and mental well-being was noted during 6 months of observation. The comparison group showed no improvement in the psychoemotional state. Discussion. The study demonstrated the effectiveness of GMDP immunomodulatory therapy in correcting immunological parameters for post-COVID-19 rehabilitation in patients. The data obtained are consistent with the previously discovered ability of GMDP to restore impaired functions of phagocytic cells and induce the expression of their surface activation markers, which in turn contributes to an adequate response to pathogens. Conclusion. The study revealed that the correction of immunological parameters with the use of GMDP in COVID-19 convalescents contributed not only to a decrease in the frequency and severity of respiratory infections, but also to an improvement in the psycho-emotional state of patients, and a decrease in anxiety and depression.Copyright © Eco-Vector, 2023. All rights reserved.

A Review on Challenges in Telerehabilitation and Human Activity Recognition Approaches during Covid-19 Pandemic

Impact of COVID-19 pandemic is widespread imposing limitations on the healthcare services all over the world. Due to this pandemic, governments around the world have imposed restrictions that limit individual freedom and have enforced social distance to prevent the collapse of national health care systems. In such situation, to offer medical care and rehabilitation to the patients, Telerehabilitation (TR) is a promising way of delivering healthcare facilities remotely using telecommunication and internet. Technological advancement has played the vital role to establish this TR technology to remotely assess patient's physical condition and act accordingly during this pandemic. Likewise, Human Activity Recognition (HAR) is a key part of the recovery process for a wide variety of conditions, such as stroke, arthritis, brain injury, musculoskeletal injuries, Parkinson's disease, and others. Different approaches of human activity recognition can be utilized to monitor the health and activity levels of such a patient effectively and TR allows to do this remotely. Therefore, in situations where conventional care is inadequate, combination of telerehabilitation and HAR approaches can be an effective means of providing treatment and these opportunities have become patently apparent during the COVID-19 outbreak. However, this new era of technical progress has significant limitations, and in this paper, our main focus is on the challenges of telerehabilitation and the various human activity recognition approaches. This study will help researchers identify a good activity detection platform for a TR system during and after COVID-19, considering TR and HAR challenges.

Model Construction and Empirical Study on Unsustainable Usage Behavior of Paid Knowledge Users

Knowledge payment is a new method of electronic learning that has developed in the era of social media. With the impact of the COVID-19 pandemic, the market for knowledge payment is rapidly expanding. Exploring the factors that influence users' sustained willingness is beneficial for better communication between knowledge payment platforms and users, and for achieving a healthier and more sustainable development of the knowledge payment industry. The model of unsustainable usage behavior of knowledge payment users was constructed on the basis of expectation inconsistency theory, price equilibrium theory, and perceived value theory, using the 'cognitive-emotional-behavioral' model framework of cognitive emotion theory. The data were collected from 348 users through a web-based questionnaire and analyzed using structural equation modeling. Findings show that expectation inconsistency, price equilibrium, and quality value, emotional value, and social value have significant effects on discontinuous use intentions. Discontinuous use intentions also significantly affect discontinuous use behavior. © 2013 IEEE.

An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. © 2023 IEEE.

Face Mask detection using Mask R-CNN to control the spread of Covid-19

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.

Masked Face Recognition System using Extended VGG-19

The scientific community has observed several issues as a result of COVID-19, both directly and indirectly. The use of face mask for health protection is crucial in the current COVID-19 scenario. Besides, ensuring the security of all people, from individuals to the state system, financial resources, diverse establishments, government, and non-government entities, is an essential component of contemporary life. Face recognition system is one of the most widely used security technology in modern life. In the presence of face masks, the performance of the current face recognition systems is not satisfactory. In this paper, we investigate a flexible solution that could be employed to recognize masked faces effectively. To do this, we develop a unique dataset to recognize the masked face, consisting of a frontal and lateral face with a mask. We propose an extended VGG19 deep model to improve the accuracy of the masked face recognition system. Then, we compare the accuracy of the proposed framework to that of well-known deep learning techniques, such as the standard Convolutional Neural Network (CNN) and the original VGG19. The experimental results demonstrate that the proposed extended VGG19 outperforms the investigated approaches. Quantitatively, the proposed model recognizes the frontal face with the mask with high accuracy of 96%. © 2022 IEEE.

Urban Mobility in the Postpandemic Stage: A Comprehensive Investigation of a Variety of Cities in China

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public's responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels;whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public's vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal. © 2023 American Society of Civil Engineers.

FaST-Net: Face Style Self-Transfer Network for Masked Face Inpainting

During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.

A Survey of Human-Computer Interaction Technology in Intelligent Home for Children's Learning

The development of the COVID-19 epidemic has increased the home learning time of children. More researchers began to pay attention to children's learning in home. This survey reviewed the frontier and classic cases in the field of interactive design of children's home learning in the past five years, analyzed tangible user interface, augmented reality, and multimodal interaction in human-computer interaction of children's home learning. This paper reviewed the application of interactive system in children's learning and points out its positive side in development of ability, process of learning, habits of learning, and environment of learning of children. Through analysis, we advise that it is necessary to create home learning applications, link smart home systems, and build an interactive learning environment for smart home learning environment design. Finally, we point out the technical and ethical problems existing in the current research, proposes that intelligent perception, emotion recognition, and expression technologies should be introduced in the future, and looks forward to the development of this field. © 2023 Institute of Computing Technology. All rights reserved.

CD4+ T cells from preeclamptic patients cause hypertension, autoantibodies and cognitive dysfunction in a pregnant rat model

CD4+ T Cells from Preeclamptic patients with or without a history of COVID-19 during pregnancy cause hypertension, autoantibodies and cognitive dysfunction in a pregnant rat model Objective: Preeclampsia (PE) new onset hypertension (HTN) during pregnancy, is associated with increased autoantibodies, cerebral blood flow (CBF) impaired cognitive function and memory loss. We have shown adoptive transfer of placentalCD4+T cells from PE women into athymic nude pregnant rats causesHTNand autoantibodies associated with PE.COVID-19 (CV) during pregnancy is associated with increased diagnosis of PE. However, we do not know the role of CD4+ T cells stimulated in response to CV in contributing to the PE phenotype seen patients with a Hx of CV during pregnancy. Therefore, we hypothesize that adoptive transfer of placental CD4+ T cells from patients with a CV History (Hx) during pregnancy with PE causes HTN, increased CBF and cognitive dysfunction in pregnant athymic nude recipient rats. Study Design: Placental CD4+ T cells isolated from normotensive (NP), PE, Hx of CV normotensive (CV Hx NT), and Hx of CV with PE (CV Hx+PE) at delivery. One million CD4+ T cells were injected i.p. into nude athymic rats on gestational day (GD) 12. The Barnes maze and the novel object recognition behavioral assays were used to assess cognitive function on GDs 15-19. Blood pressure (MAP) and CBF were measured by carotid catheter and laser Doppler flowmetry on GD19, respectively. A two-way ANOVA was used for statistical analysis. Result(s):MAPincreased inCVHx+PE (111 +/- 4, n = 4) and PE recipient rats (115 +/- 2 mmHg, n = 5) compared to CV Hx NT (100 +/- 4, n = 5) and NP (99 +/- 3 mmHg, n = 4, P < .05). CV Hx+PE and PE exhibited latency with errors navigating in the Barnes maze compared to CV Hx NT and NP groups. Locomotor activity was decreased in CV Hx+PE (P < .05) compared to PE, CV Hx NT, and NP groups. CV Hx+PE and PE spent more time exploring identical objects compared to CV Hx NT and NP groups. PE and CV Hx+ PE had increased CBF compared to CV Hx NT and NP rats. Conclusion(s): Our findings indicate that pregnant recipients of CD4+ T cells from PE with or without a Hx CV during pregnancy cause HTN, increased CBF and cognitive dysfunction compared to recipients of NP or NT Hx COVID-19 CD4+ T cells.

An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python

With the extraordinary growth in images and video data sets, there is a mind-boggling want for programmed understanding and evaluation of data with the assistance of smart frameworks, since physically it is a long way off. Individuals, unlike robots, have a limited capacity to distinguish unexpected expressions. As a result, the programmed face proximity frame- work is important in face identification, appearance recognition, head-present evaluation, human-PC cooperation, and other applications. Software that uses facial recognition for face detection and identification is regarded as biometric. This study converts the mathematical aspects of a person's face into a face print, which is then stored in a database to verify an individual's identification. A deep learning system compares a digital image or an image taken quickly to a previously stored image(which is saved in the database). The face has a significant function in interpersonal communication for identifying oneself. Face recognition technology determines the size and placement of a human face in a digital picture. Facial recognition software has a wide range of uses in the consumer market and in the security and surveillance sectors. The COVID pandemic has brought facial recognition into greater focus lately than ever before. Face detection and recognition play a vital part in security systems that people need to interact with without making physical contact. The pattern of online exam proctoring is employing face detection and recognition. Facial recognition is used in the airline sector to enable rapid, accurate identification and verification at every stage of the passenger trip. In this research, we focused on image quality because it is the major drawback in existing algorithms and used OPEN CV, Face Recognition, and designed algorithms using libraries in python. This study discusses a method for facial recognition along with its implementation and applications. © 2023 IEEE.