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Wireless sensor networks (WSNs) are a powerful support system for the fundamental infrastructure that is required to monitor physiological and activity parameters (WSN). Wearable devices, which are also referred to as wireless nodes in the scientific world, are what are used in order to measure one or more of the user’s vital signs. Each and every wireless node is a teeny-tiny device that is meant to be supplied with enough amounts of storage space, power, and transmission capability. The loss of data packets may occur during the transmission of data via a wireless medium for a number of reasons. These reasons include interferences, improper deployment circumstances, distance, and inadequate signal strength. The monitoring of a user’s physiological information and postural activity information in various applications, such as home care and hospital care, is the primary emphasis of this study. In this work, the WSN was shown thanks to the introduction of wireless sensor nodes that were created locally. These wireless sensor nodes are used in the process of analyzing many aspects of a network, such as the received signal strength, transmission offset, packet delivery ratio (PDR), and signal-to-noise interference. The work significantly improves the capabilities of conventional WSN by implementing a variety of alternative communication approaches, such as network-coded cooperative communication (NC-CC) and cooperative communication (CC). The system that is being shown makes it feasible to localize the user’s approximate position inside an indoor setting without making use of any camera network connections. This is made possible by the system’s ability to determine the user’s location via triangulation. This is one of the benefits that the system provides. A hospital sensor network, an example of which is being shown here, is capable of doing real-time monitoring of a patient’s postural activity as well as their general health. The method is being promoted in order to ensure that the patient will get assistance in a timely manner that is adequate to his/her needs. Involving NC-CC enables the effective sharing of real-time data among the group of privileged duty nurses while simultaneously minimizing the amount of network traffic, latency, and throughput. This is possible because of NC-CC protocol. The findings of the experiments showed that the proposed method of communication, which is known as dynamic retransmit/rebroadcast decision control, is a significant advancement in the network coding approach that is presently being utilized. This was demonstrated by the fact that the method was shown to be significantly more effective.

Audio-based healthcare technologies are among the most significant applications of pattern recognition and Artificial Intelligence. Lately, a major chunk of the World population has been infected with serious respiratory diseases such as COVID-19. Early recognition of lung health abnormalities can facilitate early intervention, and decrease the mortality rate of the infected population. Research has shown that it is possible to automatically monitor lung health abnormalities through respiratory sounds. In this paper, we propose an approach that employs filter bank energy-based features and Random Forests to classify lung problem types from respiratory sounds. The adventitious sounds, crackles and wheezes appear distinct to the human ear. Moreover, different sounds are characterized by different frequency ranges that are dominant. The proposed approach attempts to distinguish the adventitious sounds (crackles and wheezes) by modeling the human auditory perception of these sounds. Specifically, we propose a respiratory sounds representation technique capable of modeling the dominant frequency range present in such sounds. On a publicly available dataset (ICBHI) of size 6898 cycles spanning over 5h, our results can be compared with the state-of-the-art results, in distinguishing two different types of adventitious sounds: crackles and wheezes.

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.

Implantable biomedical devices (IBDs) play a vital role in today’s healthcare industry. Such applications demand high data rate, low power and small-sized demodulators. This work presents a simple small-sized low-power architecture for differential quadrature phase shift keying (DQPSK) demodulator for these devices. The proposed circuitry is designed in UMC 90-nm CMOS technology and occupies a layout area of 0.015mm². It is operated at 1-V supply voltage with a power consumption of 405$\mu$W. The carrier frequency is 10MHz and the obtained data rate is 20Mbps. Hence it exhibits a high data-rate-to-carrier-frequency (DRCF) ratio of 200% making it ideal for IBDs.

The Internet of Things (IoT) is gaining a tons of attention in numerous industries due to its low-cost autonomous sensor operations. IoT devices in healthcare and medical activities establish an environment that recognizes the patients’ health status such as stress levels, oxygen supply, pulse and warmth, and responds quickly in the event of an emergency. Moreover, various systems founded on low-powered biosensor nodules have been proposed to monitor patients’ medical conditions utilizing Wireless Body Area Network (WBAN); despite the fact that controlling increasing power usage and communication expenses is time-consuming and attention-demanding. Another difficult research problem is data privacy and integrity in the presence of malicious traffic. Therefore, to overcome the above-stated limitations, this research introduces a Safe and the Energy-Efficient Framework for e-Healthcare using Internet of Medical Things (IoMT), whose main goal is to reduce transmission cost but also power usage among biomaterials while sending health records conveniently and, on the other hand, to protect patients’ medical data from unverified and malevolent base stations to increase internet confidentiality and protection.

Recently, Internet of Things (IoT) technology has become popular and applicable in different fields. The exchangeable data through IoT systems are extremely huge. This poses a great challenge in constructing a scalable and reliable computing scheme for IoT environments. Therefore, an efficient computing scheme for an IoT-enabled healthcare environment was proposed in this paper. The computing scheme idea was based on the use of cloud, fog, mist and edge computing strategies after dividing their resources into N-tiers. For easy management, these tiers were clustered depending on their geographical distribution. The number of tiers for each computing strategy dynamically changed depending on the computing load. Each tier had its own capabilities. Therefore, selecting a computing strategy and its tier was achieved regarding the data requirements and the specs of each computing resource. To evaluate the proposed computing scheme, a network simulator package (NS-3) was used to construct a simulation for the IoT-enabled healthcare environment and the computing scheme. Most simulation results of our computing scheme were compared with the cloud/fog/mist, cloud/fog and cloud computing schemes. Finally, the simulation results proved that the proposed computing scheme outperformed the performance of other traditional computing schemes regarding the following performance metrics: Delay, utilization, energy consumption, beneficial users and different healthcare organization complexities.

Considerable research has recently focused on integrating cyber-physical systems in a social context. However, several challenges remain concerning appropriate methodologies, frameworks and techniques for supporting socio-cyber-physical collaboration. Existing systems do not recognize how cyber-physical resources can be socially connected so that they interact in collaborative decision-making like humans. Furthermore, the lack of semantic representations for heterogeneous cyber-social-collaborative networks limits integration, interoperability and knowledge discovery from their underlying data sources. Semantic Web ontology models can help to overcome this limitation by semantically describing and interconnecting cyber-physical objects and human participants in a social space. This research addresses the establishment of both cyber-physical and human relationships and their interactions within a social-collaborative network. We discuss how nonhuman resources can be represented as socially connected nodes and utilized by software agents. A software agent-centric Semantic Social-Collaborative Network (SSCN) is then presented that provides functionality to represent and manage cyber-physical resources in a social network. It is supported by an extended ontology model for semantically describing human and nonhuman resources and their social interactions. A software agent has been implemented to perform some actions on behalf of the nonhuman resources to achieve cyber-physical collaboration. It is demonstrated within a real-world decision support system, GRiST (www.egrist.org), used by mental-health services in the UK.

Mental illnesses are becoming increasingly prevalent, in turn leading to an increased interest in exploring artificial intelligence (AI) solutions to facilitate and enhance healthcare processes ranging from diagnosis to monitoring and treatment. In contrast to application areas where black box systems may be acceptable, explainability in healthcare applications is essential, especially in the case of diagnosing complex and sensitive mental health issues. In this paper, we first summarize recent developments in AI research for mental health, followed by an overview of approaches to explainable AI and their potential benefits in healthcare settings. We then present a recent case study of applying explainable AI for ADHD diagnosis which is used as a basis to identify challenges in realizing explainable AI solutions for mental health diagnosis and potential future research directions to address these challenges.

Sepsis is a common and deadly condition that must be treated eloquently within 19 hours. Numerous deep learning techniques, including Recurrent Neural Networks, Convolution Neural Networks, Long Short-Term Memory, and Gated Recurrent Units, have been suggested for diagnosing long-term sepsis. Regardless, a sizable portion of them are computationally risky and have precision problems. The primary issue described is that output will degrade, and resource utilization will expand proportionately as the volume of dependencies grows. To overcome these issues, we propose a G-Sep technique utilizing Bidirectional Gated Recurrent Unit Algorithm, which consumes much less resource to detect the disease and in a short time with better accuracy than the existing methods to diagnose the sepsis. AI models could assist with distinguishing potential clinical factors and give better than existing conventional low-execution models. The proposed model is implemented utilizing Conda and Tensorflow Framework using the California Inpatient Severe Sepsis (CISS) Patient Dataset. The comparative simulation of the various existing models and the proposed G-Sep model is done using Conda and Tensor frameworks. The simulation results revealed that the proposed model had outperformed other frameworks in terms of mean average precision (mAP), receiver operating characteristic curve (ROC), and Area under the ROC Curve (AUROC) metrics linearly.

Managerial decision making problems in the healthcare industry often involve considerations of customer occupancy by time of day and day of week. Through a case study at a large tertiary care hospital, we discuss a number of issues that arise in analyzing occupancy data which have implications for design of healthcare operations oriented data warehouses and analysis tools. We offer practical solutions to these problems including a transaction oriented database design, a general database framework and software tool for analysis of occupancy related data and a method for simulating entity flow from the data mart.

The management of competing stakeholders has emerged as an important topic for formulating business strategies. This is especially the case in the complicated business environment like the healthcare IT (Information Technology) industries. This paper proposes a methodology to formulate business strategies based on stakeholders' demands. Our methodology begins with the understanding of stakeholders' demands. This understanding is particularly useful for businesses with conflicting stakeholders. Our methodology consists of four phases: current business analysis, strategy development, strategy evaluation, and strategy implementation. Power, legitimacy, urgency, interdependence, cooperation, and conflict are used as stakeholders related variables. Strategic alternatives are derived on the basis of stakeholders' demands. Resolution, replacement, integration, reaggregation, and balance guidelines are employed for this derivation. Strategic alternatives are then evaluated according to a business social performance index. In order to demonstrate the practical usefulness of our methodology, three business cases for the Korean healthcare IT industry are illustrated. The case results imply that our methodology is useful for strategy formulation, especially in the case of competing business stakeholders.

Research in Customer Relationship Management (CRM) in the healthcare sector is in a developing stage and demands further research to get more in-depth insight. We present a comprehensive model to develop and prioritize CRM readiness factors in hospitals using fuzzy DEMATEL-ANP, which helps top managers allocate their limited resources to enhance the required infrastructure for a CRM system. After extracting proper readiness factors, multiple criteria decision-making (MCDM) techniques are applied to assess CRM readiness. First, using the fuzzy decision-making trial and evaluation laboratory approach (DEMATEL), the interdependent relations amongst criteria are designated. Second, the fuzzy analytic network process (ANP) is applied to weigh the sub-criteria. Top management support and structure are the most critical factors which play an essential role in the CRM readiness concept. The importance of top management’s factor has been investigated in many previous works. The structure has been neglected in the previous studies; however, our results demonstrate that it should be considered a crucial factor. This study’s findings can facilitate the CRM system’s adoption process to be employed by decision-makers within hospitals to mitigate the failure rate of the CRM system’s implementation, leading to providing plenty of advantages to the patient association and hospitals. The results of this paper can also have a contribution to the implementation of CRM and artificial intelligence (AI) as an innovative strategy in organizations, particularly hospitals.

The use of Robot-Assisted Therapy (RAT) in healthcare interventions has increasingly received research attention. However, a lot of RAT studies are conducted under Wizard of Oz (WoZ) techniques in which the robots are teleoperated or pre-programmed. The trend of RAT is moving towards (partially) autonomous control in which the robot behavior control architecture plays a significant role in creating effective human–robot interaction by engaging and motivating human users into the therapeutic processes. This paper describes the state-of-the-art of the autonomous behavior control architectures currently developed for social robots in healthcare interventions, considering both clinical and exploratory studies. We also present certain requirements that an architecture used in RAT study should acquire, which provide roboticists and therapists an inspiration to orient their designs and implementations on the basis of their targeted RAT applications.

One of the major areas where social robots are finding their place in society is for healthcare-related applications. Yet, very little research has mapped the deployment of socially assistive robots (SARs) in real settings. By using a documentary research method, we traced back 279 experiences of SARs deployments in hospitals, elderly care centers, occupational health centers, private homes, and educational institutions worldwide that involved 52 different robot models. We retrieved, analyzed, and classified the functions that SARs develop in these experiences, the areas in which they are deployed, the principal manufacturers, and the robot models that are being adopted. The functions we identified for SARs are entertainment, companionship, telepresence, edutainment, providing general and personalized information or advice, monitoring, promotion of physical exercise and rehabilitation, testing and pre-diagnosis, delivering supplies, patient registration, giving location indications, patient simulator, protective measure enforcement, medication and well-being adherence, translating and having conversations in multiple languages, psychological therapy, patrolling, interacting with digital devices, and disinfection. Our work provides an in-depth picture of the current state of the art of SARs’ deployment in real scenarios for healthcare-related applications and contributes to understanding better the role of these machines in the healthcare sector.

In the medical field, Internet of Things (IoT) applications allow for real-time diagnosis and remote patient monitoring, commonly called Internet of Health Things (IoHT). However, cybersecurity attacks may interrupt hospital operations and threaten patients’ health and well-being due to this integration. Hence, developing an Intrusion Detection System (IDS) suited explicitly for healthcare systems is essential to ensure efficiency and accuracy. Nevertheless, it is challenging to integrate anomaly-based IDS frameworks in healthcare systems as they necessitate additional processing time, temporal feature retention, and increased complexity. Therefore, a deep learning system based on SqueezeNet and NasNet is presented in this paper to detect intrusions in a healthcare setting. In this, SqueezeNet is employed to extract more significant features. On the other hand, network breaches while data transmission across distinct locations are detected by the NasNet-based classifier. In addition, the Rider Optimization Algorithm (ROA) is applied to adjust the classifier’s hyperparameters, guaranteeing that it would accurately detect attacks. Moreover, the Auxiliary Classifier Generative Adversarial Network (ACGAN) approach is integrated into the proposed framework to avoid data imbalance. Applying different performance constraints, the proposed approach is thoroughly assessed on three publicly available datasets (TON-IoT, ECU-IoHT, and WUSTL-EHMS). The results show that the proposed deep learning-based cybersecurity model outperforms traditional methods and produces better outcomes.

Raising children is challenging and requires lots of care. Parents always have to provide proper care to their children in time, like hydration and clothing. However, it is difficult to always stay alert or be aware of the care required at proper moments. One reason is that parents nowadays are busy. This especially applies to single parent, or the one who needs to raise multiple children.

This paper presents the use of an integrated multi-sensors together with a mobile application to help keep track of unusual situations concerning a child. By monitoring the changes in surrounding temperature, motions, and air pressure acquired from the sensors, our mobile application can infer the physiological needs of the children with the heat equilibrium assumption. As the thermal environment in the human body is mainly governed by the heat balance equation, we fuse all available sensor readings to the equation so as to estimate the change in situation of a child over a certain period of time. Our system can then notify the parents of the necessary care, including hydration, dining, clothing and ear barotrauma relieving. The proposed application can greatly relieve some of the mental load and pressure of the parents in taking care of children.

At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2

A body sensor network system has been developed for ubiquitous health monitoring of multiple mobile subjects, which is referred to as UbiHealth. On the body, there are micro-sensors to capture physiological signals of electrocardiography (ECG), blood pressure, respiration and temperature, as well as context information of activity and position. Sensors are coordinated by an on-body gateway, where data are collected, pre-processed and wirelessly sent to the server. The server receives, stores and processes signals from multiple gateways, providing overview of those subjects on a local map, and real-time health status of individual subjects. The application scenarios include, for example, health monitoring for rescue team members in a hazard, and elderly health monitoring in a community.

From early industrial prototypes in the 1960s and 1970s to sophisticated systems integrated into contemporary medical practice, healthcare robotics has come a long way in the last 10 years. Human potential has been enhanced by robotics in many ways, most notably in the areas of safety, accuracy, and repeatability. When paired with artificial intelligence (AI), these developments have enormous potential for the healthcare industry in the 21st century. These days, robots help in various places, such as healthcare facilities, assisted living apartments, and rehabilitation centers. For example, Aethon’s TUG robots carry supplies throughout hospitals effectively and lessen the effort of hospital staff. The main applications of healthcare robotics, including telepresence, rehabilitation, and operating rooms, are outlined in this chapter. Giraff and other telepresence robots allow doctors to observe patients from a distance. Hugo^TM RAS system from Medtronic has recently garnered notice because of its availability as a modular minimally invasive surgery solution that directly competes with the Da Vinci System in hospitals across the globe. Taking a focus on surgery rooms, telemedicine, and assistive care, this manuscript offers a broad review of the most recent advancements in healthcare robotics. It highlights the difficulties in properly integrating these technologies into the medical field.

This chapter addresses the key issues impacting the adoption of information technology (IT) in healthcare organizations. Four case studies were developed from different hospitals through a series of in-depth interviews to determine the factors affecting their IT adoption. The findings reveal that, at the organization level, these hospitals primarily make decisions to adopt new technologies based on project-related costs. At the individual level, the complications in the implementation process play a major role as they are influenced by human-related issues. Apart from the identification of key factors from the case studies, the latter part of this chapter also presents the mental framework of IT adoption for healthcare organizations developed based on the technology acceptance model.