Search

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

The development of novel biomedical sensors as highly promising devices/tools in early diagnosis and therapy monitoring of many diseases and disorders has recently witnessed unprecedented growth; more and faster than ever. Nonetheless, on the eve of Industry 5.0 and by learning from defects of current sensors in smart diagnostics of pandemics, there is still a long way to go to achieve the ideal biomedical sensors capable of meeting the growing needs and expectations for smart biomedical/diagnostic sensing through eHealth systems. Herein, an overview is provided to highlight the importance and necessity of an inevitable transition in the era of digital health/Healthcare 4.0 towards smart biomedical/diagnostic sensing and how to approach it via new digital technologies including Internet of Things (IoT), artificial intelligence, IoT gateways (smartphones, readers), etc. This review will bring together the different types of smartphone/reader-based biomedical sensors, which have been employing for a wide variety of optical/electrical/electrochemical biosensing applications and paving the way for future eHealth diagnostic devices by moving towards smart biomedical sensing. Here, alongside highlighting the characteristics/criteria that should be met by the developed sensors towards smart biomedical sensing, the challenging issues ahead are delineated along with a comprehensive outlook on this extremely necessary field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Internet of Things (IoT)-based health monitoring is a key focus in Healthcare 4.0 for continuously tracking Vital signs and other health biometrics. While IoT technologies like sensors, connectivity, hardware, software, and batteries have been extensively studied, there has been a lack of emphasis on evaluating human-centered health capabilities using long-term data collected by the Internet of Medical Things (IoMT). This paper tackles the problem of human-centered Vital signs-based health long-term assessment by proposing a hybrid approach that includes model-free multi-profile monitoring, optimization models, control charts, and profile capability indices. In this new approach, a curves dissimilarity index-based profile monitoring method is employed to address the complexity and outlier elimination issues in the traditional regression model-based profile monitoring approach, and a mixed-integer linear mathematical model is developed to assign human-centered tolerance to the dissimilarity index of each Vital sign profile. Also, control charts are employed to monitor the health of individuals according to each Vital sign, and overall. In addition, the profile capability index and non-confirming probability values are interpreted as long-term health measures. The efficacy of the newly proposed method is demonstrated in a simulation study based on an individual's six Vital signs generated data set using MATLAB, CPLEX, and MINITAB. [ABSTRACT FROM AUTHOR]

Continuous monitoring of individuals' health, particularly those with chronic diseases, out of healthcare centers could result in lower patient traffic in healthcare centers, much more real-time health control, and faster emergency services. Hence, using the internet of things (IoTs) as an enabler of Industry 4.0 facilitating remote health monitoring has gained more attention in recent years. Although plenty of research has been focused on IoT-based health monitoring, they neglected emerging concepts like human-centered health data analytics as a significant requirement in the Healthcare 5.0 era. This paper contributes to the status of human-centered IoT-based health monitoring by conducting a descriptive analysis of the corresponding literature according to biometrics monitored, applied software, hardware, sensors, and communication models, highlighting the lack of consideration of long-term, human-centered health monitoring in the existing IoT-based health monitoring literature. Results showed that the focus of the literature has mostly been on information transit technology development and not human-centered data analytics. In addition, a gap analysis of the current literature recommendations emphasized multi-biometrics monitoring and cybersecurity, not human-centered health data analysis. Finally, several guidelines are provided for human-centered IoT-based health monitoring in future research. Article Highlights: Descriptive analysis of literature on IoT-based health monitoring. Highlighting the lack of attention to the human-centered IoT-based health monitoring. Proposing guidelines for human-centered IoT-based health monitoring. [ABSTRACT FROM AUTHOR]

The healthcare industry has witnessed significant interest in applying DTs (DTs), due to technological advancements. DTs are virtual replicas of physical entities that adapt to real‐time data, enabling predictions of their physical counterparts. DT technology enhances understanding of disease occurrence, enabling more accurate diagnoses and treatments. Integrating emerging technologies like big data, cloud computing, Virtual Reality (VR), and internet‐of‐things (IoT) provides a solid foundation for DT implementation in healthcare. However, defining DTs within the healthcare context still has become increasingly challenging. Therefore, exploring the potential of DTs in healthcare contributes to research, emphasizing their transformative impact on personalized medicine and precision healthcare. In this study, we present diverse healthcare applications of DTs, including healthcare 4.0, cardiac analysis, monitoring and management, data privacy, socio‐ethical, and surgical. Moreover, this paper discusses the software and simulations of DTs that can be used in these applications of healthcare, as well as, the future trends of DTs in healthcare. This article is categorized under:Application Areas > Health CareTechnologies > Computational Intelligence [ABSTRACT FROM AUTHOR]

Abstract Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy.

Cloud-based Healthcare 4.0 systems have research challenges with secure medical data processing, especially biomedical image processing with privacy protection. Medical records are generally text/numerical or multimedia. Multimedia data includes X-ray scans, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, etc. Transferring biomedical multimedia data to medical authorities raises various security concerns. This paper proposes a one-of-a-kind blockchain-based secure biomedical image processing system that maintains anonymity. The integrated Healthcare 4.0 assisted multimedia image processing architecture includes an edge layer, fog computing layer, cloud storage layer, and blockchain layer. The edge layer collects and sends periodic medical information from the patient to the higher layer. The multimedia data from the edge layer is securely preserved in blockchain-assisted cloud storage through fog nodes using lightweight cryptography. Medical users then safely search such data for medical treatment or monitoring. Lightweight cryptographic procedures are proposed by employing Elliptic Curve Cryptography (ECC) with Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Digital Signature (ECDS) algorithm to secure biomedical image processing while maintaining privacy (ECDSA). The proposed technique is experimented with using publically available chest X-ray and CT images. The experimental results revealed that the proposed model shows higher computational efficiency (encryption and decryption time), Peak to Signal Noise Ratio (PSNR), and Meas Square Error (MSE). [ABSTRACT FROM AUTHOR]

Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier of fog computing can reduce expensive latency by bringing storage, processing, and networking closer to sensor nodes. Due to the growing variety of medical data and service types, there is a crucial need for efficient and secure architecture for sensor-based health-monitoring devices connected to fog nodes. In this paper, we present publish/subscribe and interest/resource-based non-DHT-based peer-to-peer (P2P) RC-based architecture for resource discovery. The publish/subscribe communication model provides a scalable way to handle large volumes of data and messages in real time, while allowing fine-grained access control to messages, thus enabling heightened security. Our two − level overlay network consists of (1) a transit ring containing group-heads representing a particular resource type, and (2) a completely connected group of peers. Our theoretical analysis shows that our search latency is independent of the number of peers. Additionally, the complexity of the intra-group data-lookup protocol is constant, and the complexity of the inter-group data lookup is O(n), where n is the total number of resource types present in the network. Overall, it therefore allows the system to handle large data throughput in a flexible, cost-effective, and secure way for medical IoT systems. [ABSTRACT FROM AUTHOR]

Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy. [ABSTRACT FROM AUTHOR]

From the last few years, we have witnessed an exponential increase in the usage of delay-sensitive applications by the end-users because of the paradigm shift and revolution in different technologies starting from 1G to 5G most of which are having focus on QoS and QoE to the end-users. The existing standards on 5G mainly concentrate on the following issues: 10 Gigabyte data rate, 1-millisecond latency, high bandwidth per unit area, 99.999% availability, 100% coverage and 90% reduction in network energy usage. Hence, 5G has the capability to support various types of communications from the low power Local Area Network (LAN) to Wide Area Networks (WAN) with low-latency and high-speed. It allows human-to-human (H2H), human-to-machine (H2M) interactions for exchanging of data and signals. So, for better data transmission between different entities (for example, smart objects located across different geographic locations), efficient communication between billions of smart devices is required and the associated technology is called as "Internet of Things". However, issues such as- latency-tolerant, low-data rate, high complexity, privacy and security in existing solutions may deteriorate the performance of any implemented solution in this environment. To mitigate the above-mentioned problems, the literature suggests that fog computing can be one of the options as it provides ultra-low-latency for Tactile-based applications. Tactile Internet is an emerging technology used for H2M interactions to support high-reliability, ultra-responsive, and high fidelity. Keeping focus on all the aforementioned challenges and constraints, in this paper, we provide an analysis on the usage of the strong backbone infrastructure of fog computing for 5G-enabled Tactile Internet with a maximum bandwidth of 1 Gigabyte having the minimum latency of 1-millisecond. It supports low-latency and high-reliability in Tactile-based applications. Keeping focus on the issues such as- resource management, communication infrastructure, fog orchestration, fog networking, healthcare, security and privacy of fog system in Tactile-based applications, we have explored and compared the existing state-of-the-art proposals using various parameters such as- energy-efficient, QoS, scalability, mobility, and interoperability. In addition, a number of open research challenges of fog computing for 5G-enabled Tactile Internet are also explored to provide deep insights to the readers. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution (4IR or Industry 4.0), its communication and information enabling technologies are revolutionizing the production and services applications. In the healthcare domain, electronic health (eHealth) is evolving towards healthcare 4.0 through the Internet of things, big data analytics, Cloud and Fog computing technologies. There is need for early cancer detection, which can be achieved by new imaging technologies for example Terahertz imaging and Infrared thermography. The integration of such new imaging modalities and wearable sensor networks with computer aided diagnosis systems (CAD) and IoT systems with interconnectivity and interoperability could enable continuous, remote observation of cancer diagnosed patients by experts to monitor the success of treatment procedures, side effects of drugs, statistical analysis and making predictions of cancer based on age and demographics etc. for timely intervention and personalized care. In the healthcare 4.0 paradigm, there is need not only for smart diagnosis but also care through remote patient monitoring. The vital signs associated with cancer and clinical information can be integrated to make the system culminate into a smart system with health records. This work proposes an IoT enabled deep learning framework for classification of breast images; we propose the development of both custom CNN model and fine-tuned, pretrained CNN models capable of multimodal image classification. IoT simulation and cloud processing is performed in the ThingSpeak cloud platform with email alert capability. The performance of the CNN models has been evaluated using the accuracy, sensitivity and specificity metrics which achieved 98.44%, 98.44% and 98.45% respectively for the custom CNN model together with the receiver operating characteristics curves and confusion matrices. [ABSTRACT FROM AUTHOR]

Objective of the study: This article aims to present a systematic literature review on the application of blockchain in the healthcare supply chain. Relevance/originality: The relevance and originality consist in the presentation of a systematic literature review that focuses and considers the concepts of blockchain, healthcare and supply chain as equally important, in the studied time frame. Methodology/approach: The systematic literature review followed a protocol based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), performing the search in eight databases, selecting conference and journal articles based on their content in sequential steps. Main results: As a result, 122 publications were selected. The number of publications in the field has been found to grow rapidly, and sensors and the Internet of Things (IoT) are two prominent minor topics. Three research challenges were raised. Theoretical/methodological contributions: Provide understanding to academics and professionals in the field about the state of the art of applying the blockchain in the healthcare supply chain, both in terms of the evolution of the literature, as well as in terms of raising new research. Social/management contributions: Raising discussions on solutions to healthcare supply chain issues, involving for example data integrity and privacy in electronic medical reports, drug counterfeiting and the use of sensors and the internet of things. [ABSTRACT FROM AUTHOR]

Background: the increasing adoption of smart and wearable sensors in the healthcare domain empowers the development of cutting-edge medical applications. Smart hospitals can employ sensors and applications for critical decision-making based on real-time monitoring of patients and equipment. In this context, quality of service (QoS) is essential to ensure the reliability of application data. Methods: we developed a QoS-aware sensor middleware for healthcare 4.0 that orchestrates data from several sensors in a hybrid operating room. We deployed depth imaging sensors and real-time location tags to monitor surgeries in real-time, providing data to medical applications. Results: an experimental evaluation in an actual hybrid operating room demonstrates that the solution can reduce the jitter of sensor samples up to 90.3%. Conclusions: the main contribution of this article relies on the QoS Service Elasticity strategy that aims to provide QoS for applications. The development and installation were demonstrated to be complex, but possible to achieve. [ABSTRACT FROM AUTHOR]

Background: the increasing adoption of smart and wearable sensors in the healthcare domain empowers the development of cutting-edge medical applications. Smart hospitals can employ sensors and applications for critical decision-making based on real-time monitoring of patients and equipment. In this context, quality of service (QoS) is essential to ensure the reliability of application data. Methods: we developed a QoS-aware sensor middleware for healthcare 4.0 that orchestrates data from several sensors in a hybrid operating room. We deployed depth imaging sensors and real-time location tags to monitor surgeries in real-time, providing data to medical applications. Results: an experimental evaluation in an actual hybrid operating room demonstrates that the solution can reduce the jitter of sensor samples up to 90.3%. Conclusions: the main contribution of this article relies on the QoS Service Elasticity strategy that aims to provide QoS for applications. The development and installation were demonstrated to be complex, but possible to achieve.

At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm. [ABSTRACT FROM AUTHOR]

• Identification of heterogeneous data sources, recognizing the ones of unknown type. • Dynamic mapping of data sources' APIs of known type with those of unknown type. • Data collection from data sources through a dynamic data acquisition API. • Correlation of data sources' quality and their corresponding data quality. • Efficient results, ensuring end-to-end both data sources and data quality. Healthcare 4.0 is being hailed as the current industrial revolution in the healthcare domain, dealing with billions of heterogeneous IoT data sources that are connected over the Internet and aim at providing real-time health-related information for citizens and patients. It is of major importance to utilize an automated way to identify the quality levels of these data sources, in order to obtain reliable health data. In this manuscript, we demonstrate an innovative mechanism for assessing the quality of various datasets in correlation with the quality of the corresponding data sources. For that purpose, the mechanism follows a 5-stepped approach through which the available data sources are detected, identified and connected to health platforms, where finally their data is gathered. Once the data is obtained, the mechanism cleans it and correlates it with the quality measurements that are captured from each different data source, in order to finally decide whether these data sources are being characterized as qualitative or not, and thus their data is kept for further analysis. The proposed mechanism is evaluated through an experiment using a sample of 18 existing heterogeneous medical data sources. Based on the captured results, we were able to identify a data source of unknown type, recognizing that it was a body weight scale. Afterwards, we were able to find out that the API method that was responsible for gathering data out of this data source was the getMeasurements() method, while combining both the body weight scale's quality and its derived data quality, we could decide that this data source was considered as qualitative enough. By taking full advantage of capturing the quality of a data source through measuring and correlating both the data source's quality itself and the quality of its derived data, the proposed mechanism provides efficient results, being able to ensure end-to-end both data sources and data quality. [ABSTRACT FROM AUTHOR]