CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.

Design and Autonomous Navigation of a New Indoor Disinfection Robot Based on Disinfection Modeling

The COVID-19 pandemic shows growing demand of robots to replace humans for conducting multiple tasks including logistics, patient care, and disinfection in contaminated areas. In this paper, a new autonomous disinfection robot is proposed based on aerosolized hydrogen peroxide disinfection method. Its unique feature lies in that the autonomous navigation is planned by developing an atomization disinfection model and a target detection algorithm, which enables cost-effective, point-of-care, and full-coverage disinfection of the air and surface in indoor environment. A prototype robot has been fabricated for experimental study. The effectiveness of the proposed concept design for automated indoor environmental disinfection has been verified with air and surface quality monitoring provided by a qualified third-party testing agency. Note to Practitioners - Robots are desirable to reduce the risk of human infection of highly contagious virus. For such purpose, a novel autonomous disinfection robot is designed herein for automated disinfection of air and surface in indoor environment. The robot structure consists of a mobile carrier platform and an atomizer disinfection module. The disinfection modeling is conducted by using the measurement data provided by a custom-built PM sensor array. To achieve cost-effective and qualified disinfection, a full-coverage path planning scheme is proposed based on the established disinfection model. Moreover, for specifically disinfecting the frequently contacted objects (e.g., tables and chairs in offices and hospitals), a target perception algorithm is proposed to mark the localization of these objects in the map, which are disinfected by the robot more carefully in these marked areas. Experimental results indicate that the developed disinfection robot offers great effectiveness to fight against the COVID-19 pandemic. © 2004-2012 IEEE.

Passive and Privacy-Preserving Human Localization: A Social Distancing Approach Using Commercial Millimeter-Wave Access Points

The pandemic outbreak has profoundly changed our life, especially our social habits and communication behaviors. While this dramatic shock has heavily impacted human interaction rules, novel localization techniques are emerging to help society in complying with new policies, such as social distancing. Wireless sensing and machine learning are well suited to alleviate virus propagation in a privacy-preserving manner. However, their wide deployment requires cost-effective installation and operational solutions.

Rumor Localization, Detection and Prediction in Social Network

With the global epidemic of the COVID-19, various rumors spread wantonly on social networks, which has seriously affected the stability and harmony of the entire society. To purify the network environment, some researchers have proposed to fight rumors from the perspectives of tracing the source of rumors, detecting the authenticity of information, and predicting explosive fake news. But their works are fragmented, and their performance are not significant. So we need strong antirumor methods to fight rumors. To this end, this article proposes a more comprehensive antirumor mechanism, which can realize rumors source location, rumor detection, and popularity prediction (RLDP). In particular, in the task of localization, we propose graph neural network-based method, which does not need to specify the underlying propagation mode and the number of rumor sources;in the task of detection, utilizing lightGBM, we construct a rumor detection model;in the task of popularity prediction, we construct a model based on contrastive learning while considering user engagements and information propagation, and the text of rumor. Finally, we verify the performance of the proposed RLDP by conducting extensive experiments. IEEE

A Decentralized Solution for Epidemiological Surveillance in Campus Scenarios

Throughout the various containment phases of a pandemic, such as Covid-19, digital tools and services have proven to be essential measures to counteract the ensuing disrupting effects in social and working interactions. In such scenarios, Nausica@DApp, the comprehensive solution proposed in this paper, eases compatibility of the in-presence activities of a campus-based corporation with the organizational constraints posed by the virus during the pandemic, or at a later endemic stage. This is accomplished throughout several intervention areas, such as personnel contact tracing, crowd gathering surveillance, and epidemiological monitoring. These operational requirements, in particular indirect contact tracing and overcrowd monitoring, call for the adoption of an absolute device localization paradigm, which, in the proposed solution, has been devised on top of the campus WiFi infrastructure, proving to be encouragingly accurate in most cases. Absolute localization, on the other hand, entails a certain amount of server-based centralized operations, which might affect the preservation of user data privacy. The novelty of the proposed solution consists in maximizing confidentiality and integrity in the handling of sensitive personal information, in spite of the centralized aspects of the localization system. This is accomplished by decentralizing contact tracing matching operations, which are entirely carried out locally, by apps running on the users' mobile devices. Contact data are pseudonymized and their authenticity is guaranteed by a blockchain. Furthermore, the proposed novel solution improves privacy preservation by eschewing recourse to the Bluetooth app-to-app channel for user data exchange, in fact a typical choice of most current contract tracing solutions. Thanks to a sensible use of the blockchain features, integrated into Nausica@DApp's microservice-based back-end, a higher degree of operation transparency can be relied upon, thus boosting the user's level of trust and enhancing the availability and reliability of data about people gathering within the campus premises. Moreover, contact tracing only requires the mobile device WiFi interface to be on, so that users are neither forced to adopt new habits, nor to grant additional device access permissions to contact tracing apps (potentially undermining their own privacy). The overall system has been analysed in terms of performance and costs, and the experiments have shown that its adoption is viable and effective.

IoT-Based Epidemic Monitoring via Improved Gated Recurrent Unit Model

During the Coronavirus Disease 2019 (COVID-19) pandemic, non-contact health monitoring and human activity detection by various sensors have attracted tremendous attention. Robot monitoring will result in minimizing the life threat to health providers during the COVID-19 pandemic period. How to improve the performance and generalization of the monitoring model is a critical but challenging task. This paper constructs an epidemic monitoring architecture based on multi-sensor information fusion and applies it in medical robots’ services, such as patient-care, disinfection, garbage disposal, etc. We propose a gated recurrent unit model based on a genetic algorithm (GA-GRU)to realize the effective feature selection and improve the effectiveness and accuracy of the localization, navigation, and activity monitoring for indoor wireless sensor networks (WSNs). By using two GRU layers in the GA-GRU, we improve the generalization capability in multiple WSNs. All these advantages of GA-GRU make it outperform other representative algorithms in a variety of evaluation metrics. The experiments on the WSNs verify that the proposed GA-GRU leads to successful runs and provides optimal performances. These results suggest the GA-GRU method may be preferable for epidemic monitoring in medicine and allied areas with particular relation to the control of the epidemic or pandemic such as COVID-19 pandemic.

CSI-based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world’s healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a non-wearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the Channel State Information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional Convolutional Neural Networks (1D-CNN) and Bi-directional long-short term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds. First, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The HAR results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. IEEE

Vaccinated, What Next? An Efficient Contact and Social Distance Tracing Based on Heterogeneous Telco Data

The demand for safety-boosting systems is always increasing, especially to limit the rapid spread of COVID-19. Real-time social distance preserving is an essential application towards containing the pandemic outbreak. Few systems have been proposed which require infrastructure setup and high-end phones. Therefore, they have limited ubiquitous adoption. Cellular technology enjoys widespread availability and their support by commodity cellphones which suggest leveraging it for social distance tracking. However, users sharing the same environment may be connected to different teleco providers of different network configurations. Traditional cellular-based localization systems usually build a separate model for each provider, leading to a drop in social distance performance. In this paper, we propose CellTrace, a deep learning-based social distance preserving system. Specifically, CellTrace finds a cross-provider representation using a deep learning version of Canonical Correlation Analysis. Different providers’data are highly correlated in this representation and used to train a localization model for estimating the social distances. Additionally, CellTrace incorporates different modules that improve the deep model’s generalization against overtraining and noise. We have implemented and evaluated CellTrace in two different environments with a side-by-side comparison with the state-of-the-art cellular localization and contact tracing techniques. The results show that CellTrace can accurately localize users and estimate the contact occurrence, regardless of the connected providers, with a sub-meter median error and 97% accuracy, respectively. In addition, we show that CellTrace has robust performance in various challenging scenarios. Author

Locating Multi-Sources in Social Networks With a Low Infection Rate

With the development of modern technology, numerous economic losses are incurred by various spreading phenomena. Thus, it is of great significance to identify the initial sources triggering such phenomena. The investigation of source localization in social networks has gained substantial attention and become a popular topic of study. For practical spreading phenomena on social networks, the infection rates are relatively low. Hence, a high uncertainty of spreading trace might be incurred, which further incurs the reduction of localization accuracy obtained through existed source localization methods, especially the observer-based ones. Aiming to solve the source localization problem with a low infection rate, we propose a novel localization algorithm, i.e., path-based source identification (PBSI). First, a small number of nodes are selected and designated as observers. After the propagation process triggered by sources, we can obtain a snapshot. Later, a label is assigned to represent whether a node is infected or not, and observers are supposed to record the paths through which nodes are successfully infected. Based on source centrality theory, observers make the labels flow in the direction recorded during the label iteration process, which ensures the labels of nodes in the direction of the source increase gradually. Extensive experiments indicate that the proposed PBSI can handle source localization problems for both single and multi-source scenarios with better performance than that of state-of-the-art algorithms under different propagation models.

Learning effectiveness of 3D virtual reality in hospitality training: a situated cognitive perspective

Purpose>Training is one of the key dimensions of internal marketing. Virtual reality (VR), a computer technology that replicates an environment (real or imagined) and simulates a user’s physical presence in that environment to allow for user interaction, offers unique opportunities from a training perspective, such as allowing users to improve their skills without the consequence of failing real customers or the need to be in the real environment physically. This study aims to focus on comparing the effectiveness of VR hospitality training with that of real-world hospitality training.Design/methodology/approach>This study adopts situated cognition theory to empirically test the effect of the awareness of contextual variables (social interaction, location and task) on learning and compare learning outcomes between tourism training in VR and real-world experimental settings.Findings>Results indicate that location and task awareness enhance cognitive absorption, but social awareness does not influence cognitive absorption. There is no significant difference between training in real-world and VR environments. Finally, cognitive absorption has a positive effect on mental model change (the learning outcome).Originality/value>This result advances the theoretical understanding on the significance of learning context by applying situated cognition theory in hospitality training and has significant implications for training that aims for rigor and efficiency within cost, location and time constraints.

(Re)Designing Technical Documentation About COVID-19 with and for Indigenous Communities in Gainesville, Florida, Oaxaca de Juárez, Mexico, and Quetzaltenango, Guatemala

Background: In this article, we document how our team of translators, interpreters, technical communicators, and health justice workers is collaborating to (re)design COVID-19-related technical documentation for and with Indigenous language speakers in Gainesville, FL, USA;Oaxaca de Juarez, Mexico;and Quetzaltenango, Guatemala. Literature review: Although (mis)representations of Indigenous communities have been an ongoing issue in and beyond technical communication, the COVID-19 pandemic has brought added attention to how government institutions and other agencies fail to consider the cultural values, languages, and communication practices of Indigenous communities when writing, designing, and sharing technical information. Research questions: 1. How can technical communicators work toward social justice in health through collaborative design with Indigenous language speakers?2. How can technical documentation about COVID-19 be (re)designed alongside members of vulnerable communities to redress oppressive representations while increasing access and usability?Methodology: Through interviews and other participatory design activities conducted with Indigenous language speakers in the US, Guatemala, and Mexico, we illustrate how Western approaches to creating technical documentation, particularly in health-related contexts such as the COVID-19 pandemic, put communities at risk by failing to localize health messaging for Indigenous audiences. We then document our work intended to collaboratively design and translate COVID-19-related technical information alongside those Indigenous language speakers to benefit Indigenous language speakers in Gainesville and other parts of North Central Florida. Results: Through this discussion, we highlight how technical communicators can collaborate with Indigenous language speakers to create, translate, and share multilingual technical documents that can contribute to social justice efforts by enhancing language access. Conclusion: Through collaborations with Indigenous language speakers, translators, and interpreters, social/health justice projects in technical communication can be combined, localized, and adapted to better serve and represent the diversity of people, languages, and cultures that continue to increase in our world. IEEE

Close-Contact Detection Using a Single Camera for Sports Considering Occlusion

The Coronavirus disease 2019 (COVID-19) is still prevalent in the world. Exercise is important to maintain our health while dealing with infectious diseases. Social distancing is more important during exercise because we may not be able to wear masks to avoid breathing problems, heatstroke, etc. To maintain social distancing during exercise, we develop a close-contact detection system using a single camera especially for sports in schools and gyms. We rely on a single camera because of the deployment cost. The system recognizes people from a video and estimates the interpersonal distance for close-contact detection. The challenge is the occlusion of people, which leads to false negatives in close-contact detection. To solve the problem, we leverage the observation that most false negatives in human detection are caused by occlusion owing to other people. This is because there are few obstacles in sports facilities. Based on the above observation, we assume that a person still exists near the last detected position even when s/he disappears in the proximity of other people. For evaluation, we recorded 834 videos that were 112 min long in total including various scenarios with 2724 close-contacts. The results show that the F1-score of close-contact detection and tracking are 83.6% and 67.3%, respectively. We also confirmed that the start and end time errors are within 1 s for more than 80% of the close-contacts. © 2013 IEEE.

Multiple Binocular Cameras-Based Indoor Localization Technique Using Deep Learning and Multimodal Fusion

In this paper, an image based indoor localization technique using multiple binocular cameras is proposed by the deep learning and multimodal fusion. First, by taking advantage of the cross-model correlations between various multimodal images for localization purpose, the obtained images are concatenated to form two new modalities: three-channel gray image and three-channel depth image. Then, a two-stream convolutional neural network (CNN) is used for multimodal feature extraction which can ensure the independent of each image modality. Moreover, a decision-level fusion rule is proposed to fuse the extracted features with the linear weight sum method. At last, in order to make use of the feature correlation between each image modality, the fused feature is extracted once again by two convolutional max-pooling blocks. The shrinkage Loss based loss function is designed to obtain the position based regression function at last. Field tests show that the proposed algorithm can obtain more accurate position estimation than other existing image based localization approaches.

An Improved Localization of Mobile Robotic System Based on AMCL Algorithm

In the Mobile Robotics domain, the ability of robots to locate themselves is one of the most important events. By locating, mobile robots can obtain information about the environment and continuously track their position and direction. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is applied most often in robot localization, a two-dimensional environment probabilistic localization system to improve the problems such as high computational complexity and hijacking of mobile robots that exist in the traditional MCL method. The proposed method is based on 2D laser information, range finder information, and AMCL to accomplish the localization task. Furthermore, an optimized AMCL algorithm is proposed to increase the accuracy of localization in terrain that is easy to fail to locate, have a chance to locate successfully when a localization error occurs, and apply the optimized AMCL to the mobile robot system. From the experimental results, we know that the improved AMCL algorithm can enhance the positioning accuracy of the robot effectively, which has better practicality than the original AMCL.

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review.

Digital contact tracing provides an expeditious and comprehensive way to collect and analyze data on people's proximity, location, movement, and health status. However, this technique raises concerns about data privacy and its overall effectiveness. This paper contributes to this debate as it provides a systematic review of digital contact tracing studies between January 1, 2020, and March 31, 2021. Following the PRISMA protocol for systematic reviews and the CHEERS statement for quality assessment, 580 papers were initially screened, and 19 papers were included in a qualitative synthesis. We add to the current literature in three ways. First, we evaluate whether digital contact tracing can mitigate COVID-19 by either reducing the effective reproductive number or the infected cases. Second, we study whether digital is more effective than manual contact tracing. Third, we analyze how proximity/location awareness technologies affect data privacy and population participation. We also discuss proximity/location accuracy problems arising when these technologies are applied in different built environments (i.e., home, transport, mall, park). This review provides a strong rationale for using digital contact tracing under specific requirements. Outcomes may inform current digital contact tracing implementation efforts worldwide regarding the potential benefits, technical limitations, and trade-offs between effectiveness and privacy.