Health 4.0: Applications, Management, Technologies and Review

The Industry 4.0 Standard (I4S) employs technologies for automation and data exchange through cloud computing, Big Data (BD), Internet of Things (IoT), forms of wireless Internet, 5G technologies, cryptography, the use of semantic database (DB) design, Augmented Reality (AR) and Content-Based Image Retrieval (CBIR). Its healthcare extension is the so-called Health 4.0. This study informs about Health 4.0 and its potential to extend, virtualize and enable new healthcare-related processes (e.g., home care, finitude medicine, and personalized/remotely triggered pharmaceutical treatments) and transform them into services. In the future, these services will be able to virtualize multiple levels of care, connect devices and move to Personalized Medicine (PM). The Health 4.0 Cyber-Physical System (HCPS) contains several types of computers, communications, storage, interfaces, biosensors, and bioactuators. The HCPS paradigm permits observing processes from the real world, as well as monitoring patients before, during and after surgical procedures using biosensors. Besides, HCPSs contain bioactuators that accomplish the intended interventions along with other novel strategies to deploy PM. A biosensor detects some critical outer and inner patient conditions and sends these signals to a Decision-Making Unit (DMU). Mobile devices and wearables are present examples of gadgets containing biosensors. Once the DMU receives signals, they can be compared to the patient’s medical history and, depending on the protocols, a set of measures to handle a given situation will follow. The part responsible for the implementation of the automated mitigation actions are the bioactuators, which can vary from a buzzer to the remote-controlled release of some elements in a capsule inside the patient’s body.             Decentralizing health services is a challenge for the creation of health-related applications. Together, CBIR systems can enable access to information from multimedia and multimodality images, which can aid in patient diagnosis and medical decision-making. Currently, the National Health Service addresses the application of communication tools to patients and medical teams to intensify the transfer of treatments from the hospital to the home, without disruption in outpatient services. HCPS technologies share tools with remote servers, allowing data embedding and BD analysis and permit easy integration of healthcare professionals expertise with intelligent devices.  However, it is undeniable the need for improvements, multidisciplinary discussions, strong laws/protocols, inventories about the impact of novel techniques on patients/caregivers as well as rigorous tests of accuracy until reaching the level of automating any medical care technological initiative

Why Software-Defined Radio (SDR) Matters in Healthcare?

Background: Wireless Body Area Networks (WBANs) have been drawing noteworthy academic and industrial attention. A WBAN states a network dedicated to acquire personal biomedical data via cutting-edge sensors and to transmit healthcare-related commands to particular types of actuators intended for health purposes. Still, different proprietary designs exist, which may lead to biased assessments. This paper studies the role of Software-Defined Radio (SDR) in a WBAN system for inpatient and outpatient monitoring and explains to health professionals the importance of the SDR within WBANs. Methods: A concern related to all wireless networks is their dependence on hardware, which limits reprogramming or reconfiguration alternatives. If an error happens in the equipment, firmware, or software, then, typically, there will be no way to fix system vulnerabilities. SDR solves many fixed-hardware problems with other benefits. Results: SDR entails more healthcare domain dynamics with more network convergence in agreement with the stakeholders involved. Then the SDR perspective can bring in innovation to the healthcare subsystems’ interoperability with recombination/reprogramming of their parts, updating, and malleability. Conclusion: SDR technology has many utilizations in radio environments and is becoming progressively more widespread among all kinds of users. Nowadays, there are many frameworks to manipulate radio signals only with a computer and an inexpensive SDR arrangement. Moreover, providing a very cheap radio receiver/transmitter equipment, SDR devices can be merged with free software to simplify the spectrum analyses, provide interferences detection, deliver efficient frequency distribution assignments, test repeaters' operation while measuring their parameters, identify spectrum intruders and characterize noise according to frequency bands

SDR-Based High-Definition Video Transmission for Biomedical Engineering

Background: Software-Defined Radio (SDR) frameworks from cellular telephone base stations, e.g., Multiservice Distributed Access System (MDAS) and small cells, employ extensively integrated RF agile transceivers. The Internet of Medical Things (IoMT) is the collection of medical devices and applications that connect to healthcare IT systems through online computer networks. Medical devices equipped with Wi-Fi allow M2M communication, which is the backbone of IoMT and associated devices linked to cloud platforms containing stored data to be analyzed. Examples of IoMT include remote patient monitoring of people with chronic or long-term conditions, tracking patient medication orders and the location of patients admitted to hospitals, and patients' wearables to send info to caregivers. Infusion pumps connected to dashboards and hospital beds rigged with sensors measuring patients' vital signs are medical devices that can be converted to or deployed as IoMT technology. Methods: This work proposes an SDR architecture to allow wireless High-Definition (HD) video broadcast for biomedical applications. This text examines a Wideband Wireless Video (WWV) signal chain implementation using the transceivers, the data transmitted volume, the matching occupied RF bandwidth, the communication distance, the transmitter’s power, and the implementation of the PHY layer as Orthogonal Frequency Division Multiplexing (OFDM) with test results to evade RF interference. Results: As the IoMT grows, the amount of possible IoMT uses increases. Many mobile devices employ Near Field Communication (NFC) Radio Frequency Identification (RFID) tags allowing them to share data with IT systems. RFID tags in medical equipment and supplies allow hospital staff can remain aware of the quantities they have in stock. The practice of using IoMT devices to observe patients in their homes remotely is also known as telemedicine. This kind of treatment spares patients from traveling to healthcare facilities whenever they have a medical question or change in their condition. Conclusion: An SDR-based HD biomedical video transmission is proposed, with its benefits and disadvantages for biomedical WWV are discussed. The security of IoMT sensitive data is a developing concern for healthcare providers

Biomedical Cyber-Physical Systems in the Light of Database as a Service (DBaaS) Paradigm

Background: A database (DB) to store indexed information about drug delivery, test, and their temporal behavior is paramount in new Biomedical Cyber-Physical Systems (BCPSs). The term Database as a Service (DBaaS) means that a corporation delivers the hardware, software, and other infrastructure required by companies to operate their databases according to their demands instead of keeping an internal data warehouse. Methods: BCPSs attributes are presented and discussed.  One needs to retrieve detailed knowledge reliably to make adequate healthcare treatment decisions. Furthermore, these DBs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. There are Search Query Language (SQL), and NoSQL DBs.  Results: This work investigates how to retrieve biomedical-related knowledge reliably to make adequate healthcare treatment decisions. Furthermore, Biomedical DBaaSs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. Conclusion: A NoSQL DB allows more flexibility with changes while the BCPSs are running, which allows for queries and data handling according to the context and situation. A DBaaS must be adaptive and permit the DB management within an extensive variety of distinctive sources, modalities, dimensionalities, and data handling according to conventional ways

Content-Based Image Retrieval (CBIR) in Big Histological Image Databases

Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge. Methods: This work discusses a CBIR framework to extract and handle histological data, histological metadata, integrated patient records, specimen metadata, attributes, and similar stored files. This work presents a scalable image-retrieval framework for intelligent HI analysis with real-time retrieval. The potential applications of this framework include image-guided diagnosis, decision support, healthcare education, and efficient biological data management. Results: The considerable amount of biological-related data prompted the development and deployment of large-scale databases and data-driven techniques to bridge the semantic gap between images and diagnostic information. The new cloud computing technologies and the concept of cyber-physical systems have improved the CBIR architectures considerably. The proposed scalable architecture relies on CI and validates performance on several HIs acquired from microscopic tissues. Extensive assessments show improvements in terms of disease classification and retrieval tests. Conclusion: This research effort significant contributions are twofold. 1) Defining a  comprehensive and large-scale CBIR framework to analyze HIs with high-dimensional features and CI methods successfully. 2) high-performance updating and optimization strategies improve the querying while better handling new training samples than traditional methods