Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

A Comprehensive Review of Behavior Change Techniques in Wearables and IoT: Implications for Health and Well-Being

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

Literature review of digital twin in healthcare

This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.

Nurses' Perceptions About Smart Beds in Hospitals

The purpose of this study was to examine nurses' perceptions of the smart mattress equipped with Internet of things, which are incorporated into patients' beds. In addition, their concerns and suggestions about smart mattress were explored. A total of 349 nurses in a tertiary hospital participated in a cross-sectional survey. Data were collected using questionnaires. Descriptive statistical analysis was used for survey data, whereas content analysis was used for qualitative data from open-ended questions. The participants' intention to accept the smart mattresses was 12.5 (SD, 1.73) on average, indicating a high level of acceptance. The participants expected the smart mattresses to decrease their physical work burden, improve work efficiency, and prevent pressure ulcers. However, they were concerned about an increase in other aspects of their workload and in patient safety problems due to false alarms, inaccuracies, and malfunctions of the device. Nurses suggested various features that can be integrated into smart mattress. It is critical to address nurses' perceptions, expectations, and concerns during the conceptual and developmental stage of new technology in order to improve the usability, acceptance, and adoption of smart mattresses and other new innovations in hospital settings.

A graph neural network-enhanced knowledge graph framework for intelligent analysis of policing cases

In this paper, we model a knowledge graph based on graph neural networks, conduct an in-depth study on building knowledge graph embeddings for policing cases, and design a graph neural network-enhanced knowledge graph framework. In detail, we use the label propagation algorithm (LPA) to assist the convolutional graph network (GCN) in training the edge weights of the knowledge graph to construct a policing case prediction method. This improves the traditional convolutional neural network from a single-channel network to a multichannel network to accommodate the multiple feature factors of policing cases. In addition, this expands the perceptual field of the convolutional neural network to improve prediction accuracy. The experimental results show that the multichannel convolutional neural network's prediction accuracy can reach 87.7%. To ensure the efficiency of the security case analysis network, an efficient pairwise feature extraction base module is added to enhance the backbone network, which reduces the number of parameters of the whole network and decreases the complexity of operations. We experimentally demonstrate that this method achieves a better balance of efficiency and performance by obtaining approximate results with 53.5% fewer floating-point operations and 70.2% fewer number parameters than its contemporary work.

Business-Oriented Security Analysis of 6G for eHealth: An Impact Assessment Approach

Following the COVID-19 outbreak, the health sector is undergoing a deep transformation that is increasingly pushing it towards the exploitation of technology, thus fostering the growth of digital health (eHealth). Cellular networks play a pivotal role in promoting the digitalization of healthcare, and researchers are banking on beyond fifth-generation (B5G) and sixth-generation (6G) technologies to reach the turning point, given that, according to forecasts, 5G will not be able to meet future expectations. Security is an aspect that definitely should not be overlooked for the success of eHealth to occur. This work aims to address the security issue from a poorly explored viewpoint, namely that of economics. In this paper, we first describe the main eHealth services, highlighting the key stakeholders involved. Then, we discuss how next-generation technologies could support these services to identify possible business relationships and, therefore, to realize an innovative business-oriented security analysis. A qualitative assessment of the impact of specific security breaches in diverse business conditions is provided. Moreover, we examine a case study in order to show the effects of security attacks in a definite scenario and discuss their impact on business dynamics.

Ensemble deep honey architecture for COVID-19 prediction using CT scan and chest X-ray images

Recently, the infectious disease COVID-19 remains to have a catastrophic effect on the lives of human beings all over the world. To combat this deadliest disease, it is essential to screen the affected people quickly and least inexpensively. Radiological examination is considered the most feasible step toward attaining this objective; however, chest X-ray (CXR) and computed tomography (CT) are the most easily accessible and inexpensive options. This paper proposes a novel ensemble deep learning-based solution to predict the COVID-19-positive patients using CXR and CT images. The main aim of the proposed model is to provide an effective COVID-19 prediction model with a robust diagnosis and increase the prediction performance. Initially, pre-processing, like image resizing and noise removal, is employed using image scaling and median filtering techniques to enhance the input data for further processing. Various data augmentation styles, such as flipping and rotation, are applied to capable the model to learn the variations during training and attain better results on a small dataset. Finally, a new ensemble deep honey architecture (EDHA) model is introduced to effectively classify the COVID-19-positive and -negative cases. EDHA combines three pre-trained architectures like ShuffleNet, SqueezeNet, and DenseNet-201, to detect the class value. Moreover, a new optimization algorithm, the honey badger algorithm (HBA), is adapted in EDHA to determine the best values for the hyper-parameters of the proposed model. The proposed EDHA is implemented in the Python platform and evaluates the performance in terms of accuracy, sensitivity, specificity, precision, f1-score, AUC, and MCC. The proposed model has utilized the publicly available CXR and CT datasets to test the solution's efficiency. As a result, the simulated outcomes showed that the proposed EDHA had achieved better performance than the existing techniques in terms of Accuracy, Sensitivity, Specificity, Precision, F1-Score, MCC, AUC, and Computation time are 99.1%, 99%, 98.6%, 99.6%, 98.9%, 99.2%, 0.98, and 820 s using the CXR dataset.

COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach

COVID-19 data analysis and prediction from patient data repository collected from hospitals and health organizations. Users' credentials and personal information are at risk; it could be an unrecoverable issue worldwide. A Homomorphic identification of possible breaches could be more appropriate for minimizing the risk factors in preventing personal data. Individual user privacy preservation is a must-needed research focus in various fields. Health data generated and collected information from multiple scenarios increasing the complexity involved in maintaining secret patient information. A homomorphic-based systematic approach with a deep learning process could reduce depicts and illegal functionality of unknown organizations trying to have relation to the environment and physical and social relations. This article addresses the homomorphic standard system functionality, which refers to all the functional aspects of deep learning system requirements in COVID-19 health management. Moreover, this paper spotlights the metric privacy incorporation for improving the Deep Learning System (DPLS) approaches for solving the healthcare system's complex issues. It is absorbed from the result analysis Homomorphic-based privacy observation metric gradually improves the effectiveness of the deep learning process in COVID-19-health care management.

The role of data platforms in COVID-19 crisis: a smart city perspective

Purpose

The Covid-19 pandemic has profoundly affected urban communities, generating the need for an immediate response from local governance. The availability of urban data platforms in some smart cities helped the relevant actors to develop various solutions in an innovative and highly contextual way. The purpose of this paper is to explore the role of data platforms in smart cities in the context of the Covid-19 crisis.

Design/methodology/approach

A total of 85 studies were identified using the Clarivate Analytics Web of Science electronic library. After applying exclusion and inclusion criteria, 61 publications were considered appropriate and reasonable for the research, being read in-depth. Finally, only 52 studies presented relevant information for the topic and were synthesized following the defined research questions. During the research, the authors included in the paper other interesting references found in selected articles and important information regarding the role of data in the fight against Covid-19 in smart cities available on the Internet and social media, with the intention to capture both academic and practical perspectives.

Findings

The authors' main conclusion suggests that based on their previous expertise in collecting, processing and analyzing data from multiple sources, some smart cities quickly adapted their data platforms for an efficient response against Covid-19. The results highlight the importance of open data, data sharing, innovative thinking, the collaboration between public and private stakeholders, and the participation of citizens, especially in these difficult times.

Practical implications

The city managers and data operators can use the presented case studies and findings to identify relevant data-driven smart solutions in the fight against Covid-19 or another crisis.

Social implications

The performance of smart cities is a social concern since the population of urban communities is continuously growing. By reviewing the adoption of information technologies-based solutions to improve the quality of citizens' life, the paper emphasizes their potential in societies in which information technology is embedded, especially during a major crisis.

Originality/value

This research re-emphasizes the importance of collecting data in smart cities, the role of the diversity of their sources and the necessity of citizens, companies and government synergetic involvement, especially in a pandemic context. The existence of smart solutions to process and extract information and knowledge from large data sets was essential for many actors involved in smart cities, helping them in the decision-making process. Based on previous expertise, some smart cities quickly adapted their data platforms for an efficient response against Covid-19. The paper analyzes also these success cases that can be considered models to be adopted by other municipalities in similar circumstances.

Detection of COVID-19 using deep learning techniques and classification methods

Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID-19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID-19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.

Automated artificial intelligence-enabled proactive preparedness real-time system for accurate prediction of COVID-19 infections— Performance evaluation

COVID-19 is a contagious disease that has infected over half a billion people worldwide. Due to the rapid spread of the virus, countries are facing challenges to cope with the infection growth. In particular, healthcare organizations face difficulties efficiently provisioning medical staff, equipment, hospital beds, and quarantine centers. Machine and deep learning models have been used to predict infections, but the selection of the model is challenging for a data analyst. This paper proposes an automated Artificial Intelligence-enabled proactive preparedness real-time system that selects a learning model based on the temporal distribution of the evolution of infection. The proposed system integrates a novel methodology in determining the suitable learning model, producing an accurate forecasting algorithm with no human intervention. Numerical experiments and comparative analysis were carried out between our proposed and state-of-the-art approaches. The results show that the proposed system predicts infections with 72.1% less Mean Absolute Percentage Error (MAPE) and 65.2% lower Root Mean Square Error (RMSE) on average than state-of-the-art approaches.

Lessons Learned in Piloting a Digital Personalized COVID-19 "Radar" on a University Campus

Health authorities encouraged the use of digital contact tracing mobile applications (apps) during the COVID-19 pandemic, but the level of adoption was low because apps offered few direct benefits to counterbalance risks to personal privacy. Adoption of such apps could improve if they provided benefits to users. NOVID (COVID-19 Radar), a smartphone app, provided users with personalized data on social proximity of COVID-19 cases and exposed contacts. We analyzed uptake of NOVID at the Georgia Institute of Technology (Georgia Tech) during the 2020-2021 academic year. Data included anonymous NOVID users who self-identified with Georgia Tech and their first- and second-degree network contacts. NOVID achieved 13%-30% adoption at Georgia Tech. Because of technical challenges, adoption waned after an initial peak. The largest increases in adoption (from 41 to 3704) followed administrative promotion of NOVID. Adoption increased modestly (from 2512 to 2661) after faculty- and student-led promotion, such as distribution of door hangers and a public seminar. Two-thirds of on-campus NOVID users were connected to a large network of other users, enabling them to receive data on social proximity of COVID-19 cases and exposed contacts. Network cohesion was observed to emerge rapidly when adoption rates passed just 10%, consistent with estimates from network theory. The key lesson learned in this case study is that top-down administrative promotion outperforms bottom-up grassroots promotion. Relatively high levels of adoption and network cohesion, despite technical challenges during the Georgia Tech pilot of NOVID, illustrate the promise of digital contact tracing when apps provide privacy and inherently beneficial personalized data to their users, especially in regions where Google Apple Exposure Notification is not available.

Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications

Nowadays, the concept of smart sustainable governance is wrapped around basic principles such as: (i) transparency, (ii) accountability, (iii) stakeholders’ involvement, and iv) citizens’ participation. It is through these principles that are influenced by information and communication technologies (ICT), Internet of Things (IoT), and artificial intelligence, that the practices employed by citizens and their interaction with electronic government (e-government) are diversified. Previously, the misleading concepts of the smart city implied only the objective of the local level or public officials to utilize technology. However, the recent European experience and research studies have led to a more comprehensive notion that refers to the search for intelligent solutions which allow modern sustainable cities to enhance the quality of services provided to citizens and to improve the management of urban mobility. The smart city is based on the usage of connected sensors, data management, and analytics platforms to improve the quality and functioning of built-environment systems. The aim of this paper is to understand the effects of the pandemic on smart cities and to accentuate major exercises that can be learned for post-COVID sustainable urban management and patterns. The lessons and implications outlined in this paper can be used to enforce social distancing community measures in an effective and timely way, and to optimize the use of resources in smart and sustainable cities in critical situations. The paper offers a conceptual overview and serves as a stepping-stone to extensive research and the deployment of sustainable smart city platforms and intelligent transportation systems (a sub-area of smart city applications) after the COVID-19 pandemic using a case study from Russia. Overall, our results demonstrate that the COVID-19 crisis encompasses an excellent opportunity for urban planners and policy makers to take transformative actions towards creating cities that are more intelligent and sustainable.

Utility, barriers and facilitators to the use of connected health to support families impacted by paediatric cancer: a qualitative analysis

AIM

As healthcare systems are increasingly burdened, the efficiencies and cost savings offered by connected health (CH, i.e. two-way communicative healthcare technologies such as eHealth or mHealth) present an attractive solution for supporting families impacted by cancer. More research is required, however, to examine attitudes towards CH to better facilitate its use in practice. This study seeks to examine the utility, barriers and facilitators of CH use for families affected by paediatric cancer living in Ireland.

METHODS

Healthcare professionals (n = 5) and parents of children with cancer (n = 7) completed semi-structured interviews on their experiences of and attitudes to CH via Microsoft Teams. A reflexive thematic approach to analysis was employed.

RESULTS

CH was perceived to provide support for a number of current needs with themes of 'shifting responsibilities', 'individualisation of care' and 'knowledge as power'. Through facilitating communication, information sharing and monitoring of child health, CH was perceived to support decreased parental burden and increased parental control, with positive child outcomes thought likely. Perceived barriers and facilitators to the use of CH included the 'importance of trust', 'pace of change' and 'access'.

CONCLUSION

While results suggest an acceptance of CH across key stakeholders, barriers and facilitators should be considered to support effective implementation. While further analysis of the efficacy of CH to support families impacted by paediatric cancer is needed, these findings highlight key areas where CH may be effectively employed.

Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control

COVID-19 is a life-threatening contagious virus that has spread across the globe rapidly. To reduce the outbreak impact of COVID-19 virus illness, continual identification and remote surveillance of patients are essential. Medical service delivery based on the Internet of Things (IoT) technology backed up by the fog-cloud paradigm is an efficient and time-sensitive solution for remote patient surveillance. Conspicuously, a comprehensive framework based on Radio Frequency Identification Device (RFID) and body-wearable sensor technologies supported by the fog-cloud platform is proposed for the identification and management of COVID-19 patients. The J48 decision tree is used to assess the infection degree of the user based on corresponding symptoms. RFID is used to detect Temporal Proximity Interactions (TPI) among users. Using TPI quantification, Temporal Network Analysis is used to analyze and track the current stage of the COVID-19 spread. The statistical performance and accuracy of the framework are assessed by utilizing synthetically-generated data for 250,000 users. Based on the comparative analysis, the proposed framework acquired an enhanced measure of classification accuracy, and sensitivity of 96.68% and 94.65% respectively. Moreover, significant improvement has been registered for proposed fog-cloud-based data analysis in terms of Temporal Delay efficacy, Precision, and F-measure.

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system.

Blockchain for COVID-19: a comprehensive review

The rampant and sudden outbreak of the SARS-CoV-2 coronavirus also called COVID-19 and its uncontrollable spread have led to a global crisis. COVID-19 is a highly contagious disease and the only way to fight with it is to follow social distancing and Non-Pharmaceutical Interventions (NPIs). Moreover, this virus is increasing exponentially day-by-day and a huge amount of data from this disease is also generated at the fast pace. So, there is a need to store, manage, and analyze this huge amount of data efficiently to get meaningful insights from it, which further helps medical professionals to tackle this global pandemic situation. Moreover, this data is to be passed through an open channel, i.e., the Internet, which opens the doors for the intruders to perform some malicious activities. Blockchain (BC) emerges as a technology that can manage the data in an efficient, transparent manner and also preserve the privacy of all the stakeholders. It can also aid in transaction authorization and verification in the supply chain or payments. Motivated by these facts, in this paper, we present a comprehensive review on the adoption of BC to tackle COVID-19 situations. We also present a case study on BC-based digital vaccine passports and analyzed its complexity. Finally, we analyzed the research challenges and future directions in this emerging area.