Smart Healthcare Research Articles

Wireless Alerts and Data Monitoring from BNNO-MWCNTs/PDMS Composite Film-Based TENG Integrated Inhaler for Smart Healthcare Application.

In recent years, the implementation of energy-harvesting technology in medical equipment has attracted significant interest owing to its potential for self-powered and smart healthcare systems. Herein, the integration of a triboelectric nanogenerator (TENG) is proposed into an inhaler for energy-harvesting and smart inhalation monitoring. For this initially, barium sodium niobium oxide (Ba2NaNb5O15) microparticles (BNNO MPs) are synthesized via a facile solid-state synthesis process. The BNNO MPs with ferroelectricity and high dielectric constant are incorporated into polydimethylsiloxane (PDMS) polymer to make BNNO/PDMS composite films (CFs) for TENG fabrication. The fabricated TENG is operated in a contact-separation mode, and its electrical output performance is compared to establish the optimal BNNO MPs concentration. Furthermore, multi-wall carbon nanotubes (MWCNTs), a conductive filler material, are used to enhance the electrical conductivity of the CFs, thereby improving the electrical output performance of the TENG. The robustness/durability of the proposed BNNO-MWCNTs/PDMS CF-based TENG are investigated. The proposed TENG device is demonstrated to harvest electrical energy from mechanical motions via regular human activities and power portable electronics. The TENG is integrated into the inhaler casing to count the number of sprays remaining in the canister, send the notification to a smartphone via Bluetooth, and harvest energy.

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

Federated Learning Empowered Real-Time Medical Data Processing Method for Smart Healthcare.

Computer-aided diagnosis (CAD) has always been an important research topic for applying artificial intelligence in smart healthcare. Sufficient medical data are one of the most critical factors in CAD research. However, medical data are usually obtained in chronological order and cannot be collected all at once, which poses difficulties for the application of deep learning technology in the medical field. The traditional batch learning method consumes considerable time and space resources for real-time medical data, and the incremental learning method often leads to catastrophic forgetting. To solve these problems, we propose a real-time medical data processing method based on federated learning. We divide the process into the model stage and the exemplar stage. In the model stage, we use the federated learning method to fuse the old and new models to mitigate the catastrophic forgetting problem of the new model. In the exemplar stage, we use the most representative exemplars selected from the old data to help the new model review the old knowledge, which further mitigates the catastrophic forgetting problem of the new model. We use this method to conduct experiments on a simulated medical real-time data stream. The experimental results show that our method can learn a disease diagnosis model from a continuous medical real-time data stream. As the amount of data increases, the performance of the disease diagnosis model continues to improve, and the catastrophic forgetting problem has been effectively mitigated. Compared with the traditional batch learning method, our method can significantly save time and space resources.

A Rapid Review of How Model‐based Systems Engineering is Used in Healthcare Systems

AbstractThis study presents the results from rapid review of how model‐based systems engineering (MBSE) is utilized in healthcare systems (HSs). We conduct a review of the last twelve years and find that MBSE adoption in HSs is accelerating, with use of various MBSE languages and tools, as well as their integration with other simulation and modeling techniques. We find that similar to engineered systems, the most common MBSE language is systems modeling language (SysML), followed by unified modeling language (UML) and others. Additionally, we observe that MBSE methods are frequently used in conjunction with other analytical techniques, such as simulation and co‐simulations, to analyze and enhance various HS operations, or to assist with making tradeoffs between HS attributes such as quality and cost. Moreover, we provide a non‐exhaustive classification of current research based on two dimensions: healthcare applications and MBSE use cases. Notably, MBSE is being implemented generally with patient‐centric objectives in various HS domains, including IoT‐enabled smart healthcare, clinical medicine, medical device development, healthcare process enhancement, and healthcare facilities management. While the primary MBSE use case involves modeling different aspects of healthcare operations, there is a significant number of studies that pursue requirements engineering, systems analysis, integration, verification and validation, as well as risk analysis and management. Furthermore, we identify two promising research gaps. First, there is a need for the integration of MBSE with state‐of‐the‐art data‐driven analytical methodologies such as hybrid simulation and artificial intelligence techniques. Second, HSs could greatly benefit from representing the cognitive functions and processes of human decision‐makers in the loop, such as healthcare providers (e.g., doctors and nurses), who are instrumental in sustaining the HS performance and functionality. We contend that MBSE and other SE methods and techniques could improve HSs design, operations, and management; while fostering resilience and long‐term sustainability.

I-Health: SDN-Based Fog Architecture for IIoT Applications in Healthcare.

The Industrial Internet of Things (IIoT) has been introduced in an era of increasingly broad potentials in the medical industry. In recent years, IIoT-based healthcare applications have grown in popularity, with the majority of them relying on Wireless Body Area Network (WBAN) for flexibility. There have been a few recent works that have investigated SDN-based fog architecture for constructing smart healthcare systems. However, the best fog node from the fog layer must be identified and limit the transmission of unnecessary data. To address this issue, the Intelligent Software-defined Fog Architecture (i-Health) is developed in this work. Based on the prior data pattern of each patient, the controller will decide whether to send the data to the fog layer. Furthermore, we introduced the Fog Ranking Service (FRS) and Fog Probing Service (FPS) to select the best fog node. The performance comparison reveals that the proposed i-Health outperforms existing benchmark approaches.

A robust multi-key authority system for privacy-preserving distribution and access control of healthcare data

Innovation in medical technology and communication has rapidly empowered the development of smart healthcare devices. This has led to privacy breaches, threats and vulnerabilities to sensitive patient data that result in unwanted or targeted advertising. Previous research has focused on protecting access to sensitive patient data from unauthorized entities, especially by defining roles of healthcare entities in the overall system with their access privileges. However, such efforts need to be further robust due to the involvement of a single key authority that may lead to a critical point of failure. In this paper, this vulnerability has been addressed by developing a novel approach to crucially increase the number of key authorities using homomorphic encryption. The proposed approach ensures genuine access to the verified entity by forming a subsystem of t key authorities from a total of n authorities (t<n). This creates rigorous challenge to a malicious attacker, obfuscating the selection and functioning of key access packets in a multi-key authority setup. The results of the proposed approach achieve medical data confidentiality, entity authentication, and strategic data sharing. The security of the proposed approach is assessed for different vulnerabilities of the overall system using a challenge–response game model. Moreover, the proposed approach is found to be better and secure as compared to existing schemes.

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent, unpredictable attacks, affects individuals of all ages. IoT-based seizure monitoring can greatly enhance seizure patients' quality of life. IoT device acquires patient data and transmits it to a computer program so that doctors can examine it. Currently, doctors invest significant manual effort in inspecting Electroencephalograph (EEG) signals to identify seizure activity. However, EEG-based seizure detection algorithms face challenges in real-world scenarios due to non-stationary EEG data and variable seizure patterns among patients and recording sessions. Therefore, a sophisticated computer-based approach is necessary to analyze complex EEG records. In this work, the authors proposed a hybrid approach by combining traditional convolution neural (CN) and recurrent neural networks (RNN) along with an attention mechanism for the automatic recognition of epileptic seizures through EEG signal analysis. This attention mechanism focuses on significant subsets of EEG data for class recognition, resulting in improved model performance. The proposed methods are evaluated using a publicly available UCI epileptic seizure recognition dataset, which consists of five classes: four normal conditions and one abnormal seizure condition. Experimental results demonstrate that the suggested approach achieves an overall accuracy of 97.05% for the five-class EEG recognition data, with an accuracy of 99.52% for binary classification distinguishing seizure cases from normal instances. Furthermore, the proposed intelligent seizure recognition model is compatible with an IoMT (Internet of Medical Things) cloud-based smart healthcare framework.

Deployment of Edge Computing for Smart Healthcare Systems on Cloud Computing Platform

Improving the productivity of health services is a prime concern for authorities all over the country. Yet, offering patients with scalable healthcare services while cutting costs is a complicated matter. Edge computing technologies and next-generation mobile computing infrastructure are one of the most effective alternatives for integrating efficient healthcare since they can provide real-time and cost-effective patient monitoring systems. Edge computing enables doctors to deliver care to remote areas where connectivity could not be great and skilled healthcare personnel may not be available. The proposed paper discuss the past research work and contribution in the field of smart Healthcare. The Paper further depicts general edge computing architecture for smart Healthcare system and trade-offs of Edge computing. The paper also discuss use cases of smart Healthcare system based on edge computing advantages of Smart- health and the challenges associated with the deployment.

Smart Cities towards Artificial Intelligence

The creation of smart cities has come a long way in recent years, especially in domains that use AI, such IoT-based components, security systems, smart living options, and environmental monitoring. These advancements seek to build surroundings that meet human requirements by utilizing cutting edge technologies such as automated parking, smart healthcare, and seismic monitoring systems. Even with these encouraging advancements, the current generation of smart cities is still unable to attain genuine autonomy, particularly when it comes to component-level autonomous situation management and decision-making. The transformational perspective proposed in this work views the constituent components of smart environments as self-governing agents with the ability to make decisions. The purpose of these autonomous agents is to improve security and smart living by being able to self-manage and react automatically to different situations. Such autonomous agents can be integrated to accomplish the goal of artificially intelligent smart cities. In smart cities, autonomous agents may behave pro-actively without human involvement by continually monitoring and analyzing data, anticipating possible problems, and taking proactive actions. In the context of smart healthcare, these agents may, for example, track a patient's vitals in real time, anticipate possible health emergencies, notify medical personnel, or even start emergency procedures. Autonomous agents may dynamically assign parking spots in the context of smart car parking based on real-time data, maximizing space use and minimizing traffic congestion. Agents that can autonomously manage emergency actions, such as directing people to safety and managing infrastructure to minimize damage, might be beneficial for earthquake detecting systems. As autonomous entities endowed with decision-making skills are included, we are moving closer to truly intelligent cities. Because of these agents' capacity for independent operation and quick response to changing circumstances, smart cities are able to offer a better quality of security and service while accommodating the changing demands of its residents. As a result, smart cities that have these intelligent agents installed will be more responsive, efficient, and resilient—ultimately capturing the spirit of artificial intelligence in urban planning.

Adoption of Bloom Filter and Firebase Framework to Enhance Authentication Time for Healthcare Systems Based on Blockchain Technology

Healthcare offers several advantages for actual-time smart healthcare. security concerns are growing due to its constrained computing power, storage capacity, and self-defense capabilities. The tamper-resistant decentralized architecture of more recent blockchain-based authentication solutions gives them significant security features, but they come with a high resource cost because they need a lot of processing power, additional storage, and lengthy authentication processes. Therefore, these challenges offer impediments to achieving the optimal degrees of temporal efficiency and scalability, which are critical for the effective operation of large-scale, time-sensitive IoHT systems. Our work provides an authentication solution specifically created for healthcare systems to address these issues. We work in three phases: initializing, registering, logging in, and authenticating. The suggested system combines blockchain technology, Firebase Framework, Bloom Filter, Multi-Factor authentication, and other elements to improve security and efficiency at the same time. We use the Python programming language to simulate the work, and our findings indicate that the Bloom filter decreases the amount of time it takes to determine whether a person is in the system compared to the previous way. Moreover, using Firebase may reduce transaction numbers by up to 73%. Using the Scyther tool, a security analysis of the proposed scheme proved that the suggested plan is safe from possible threats and maintains the IoHT system's scalability

Intelligent Health Surveillance: Cognitive IoT Enhanced by Rapid Machine Learning

Abstract: The quality of life for diabetic patients is significantly improved through continuous monitoring. Integrating various technologies such as the Internet of Things (IoT), embedded software, communication technologies, artificial intelligence, and smart devices helps reduce the financial burden on the healthcare system. Advances in communication technologies have facilitated personalized and remote healthcare. To meet the growing demand for advanced e-health applications, developing intelligent healthcare systems and increasing the number of applications connected to the network is essential. Consequently, to achieve critical needs like high bandwidth and energy efficiency, the 5G network must support smart healthcare applications. This research proposes an intelligent infrastructure for monitoring diabetes patients using machine learning methods. The architecture incorporates smart devices, sensors, and mobile phones to provide comprehensive coverage of the patient's body. Data collected from the patient is analyzed and classified using machine learning to produce a diagnosis. Several machine learning methods were tested to evaluate the proposed prediction system, and simulation results indicated that the Sequential Minimum Optimization (SMO) method provides higher classification accuracy, sensitivity, and precision than other techniques.

Enhancing security in smart healthcare systems: Using intelligent edge computing with a novel Salp Swarm Optimization and radial basis neural network algorithm

A smart healthcare system (SHS) is a health service system that employs advanced technologies such as wearable devices, the Internet of Things (IoT), and mobile internet to dynamically access information and connect people and institutions related to healthcare, thereby actively managing and responding to medical ecosystem needs. Edge computing (EC) plays a significant role in SHS as it enables real-time data processing and analysis at the data source, which reduces latency and improves medical intervention speed. However, the integration of patient information, including electronic health records (EHRs), into the SHS framework induces security and privacy concerns. To address these issues, an intelligent EC framework was proposed in this study. The objective of this study is to accurately identify security threats and ensure secure data transmission in the SHS environment. The proposed EC framework leverages the effectiveness of Salp Swarm Optimization and Radial Basis Functional Neural Network (SS-RBFN) for enhancing security and data privacy. The proposed methodology commences with the collection of healthcare information, which is then pre-processed to ensure the consistency and quality of the database for further analysis. Subsequently, the SS-RBFN algorithm was trained using the pre-processed database to distinguish between normal and malicious data streams accurately, offering continuous monitoring in the SHS environment. Additionally, a Rivest-Shamir-Adelman (RSA) approach was applied to safeguard data against security threats during transmission to cloud storage. The proposed model was trained and validated using the IoT-based healthcare database available at Kaggle, and the experimental results demonstrated that it achieved 99.87 % accuracy, 99.76 % precision, 99.49 % f-measure, 98.99 % recall, 97.37 % throughput, and 1.2s latency. Furthermore, the results achieved by the proposed model were compared with the existing models to validate its effectiveness in enhancing security.

Analyzing the Shortcomings in Smart Healthcare for Remote Home Care-A Case Study of the Taiwan Market.

In Taiwan, remote healthcare initially focused on telemedicine, with systematic development starting in 2007 through the "Remote Care Pilot Program" and subsequent initiatives. Significant advancements came with the "Remote Health Care Services Development Plan" in 2010, which integratedArtificial intelligence and Information and communications technologies, enhancing smart healthcare in home care. This study investigated strategic gaps in smart healthcare applications for remote home care using a mixed-methods approach, particularly the Analytic Hierarchy Process (AHP). This study identified and ranked strategic gaps, including "legal regulations", "economic factors", "user behavior habits", "policy and culture", and "environment and technology". Findings, based on in-depth interviews with 6 experts and 16 AHP questionnaire samples, highlight "legal regulations" and "user behavior habits" as critical areas needing attention. Addressing these gaps can improve user acceptance and the effectiveness of smart healthcare applications, providing valuable insights for future research and practice in making remote home care more comprehensive and efficient.

An adaptive secure internet of things and cloud based disease classification strategy for smart healthcare industry

An adaptive secure internet of things and cloud based disease classification strategy for smart healthcare industry

PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

AbstractRecent advancements in distributed computing systems have shown promising prospects in enabling the effective usage of many next‐generation applications. These applications include a wide range of fields, such as healthcare, interactive gaming, video streaming, and other related technologies. Among such solutions are the evolving vehicular fog computing (VFC) frameworks that make use of IEEE and 3GPP protocols and use advanced optimization algorithms. However, these approaches often rely on outdated protocols or computationally intensive mathematical techniques for solving or representing their optimization models. Additionally, some of these frameworks have not thoroughly considered the type of application during their evaluation and validation phases. In response to these challenges, we have developed the “predictive analytics and modules” (PAM) framework, which operates on a time and event‐driven basis. It utilizes up‐to‐date 3GPP protocols to address the inherent unpredictability of VFC‐enabled distributed computing systems required in smart healthcare systems. Through a combination of a greedy heuristic approach and a distributed offloading architecture, PAM efficiently optimizes decisions related to task offloading and computation allocation. This is achieved through specialized algorithms that provide support to computationally weaker devices, all within a time frame of under 100 ms. To assess the performance of PAM in comparison to three benchmark methodologies, the evaluation pathways that we employed are average response time, probability density function, pareto‐analysis, algorithmic run time, and algorithmic complexity.

Lightweight and privacy-preserving device-to-device authentication to enable secure transitive communication in IoT-based smart healthcare systems

Lightweight and privacy-preserving device-to-device authentication to enable secure transitive communication in IoT-based smart healthcare systems

Extended water wave optimization (EWWO) technique: a proposed approach for task scheduling in IoMT and healthcare applications

AbstractThe Internet of Medical Things (IoMT) is a version of the Internet of Things. It is getting the attention of researchers because it can be used in a wide range of smart healthcare systems. One of the main advancements employed recently is the IoMT-cloud, which allows users to access cloud services remotely over the internet. These cloud services require an efficient task scheduling approach that satisfies the Quality of Service parameters with a low energy consumption. This paper presents an overview of the integration of IoMT and cloud computing technologies. Besides,this work proposes an efficient Extended Water Wave Optimization (EWWO) task scheduling in the IoMT Cloud for healthcare applications. EWWO algorithm performs based on its operations propagation, refraction and breaking. The proposed EWWO scheduling technique minimizes the energy consumption, makespan time, execution time and increases the resource utilization. Cloudsim simulator is used to simulate the IoMT-Cloud environment to verify the effectiveness of EWWO technique. The performance has been evaluated based on various parameters such as energy consumption, makespan time and execution time.

Edge computing in IoT for smart healthcare

Edge computing in IoT for smart healthcare

6G Wireless Communication Cyber Physical System Based Smart Healthcare Using Quantum Optimization with Machine Learning

6G Wireless Communication Cyber Physical System Based Smart Healthcare Using Quantum Optimization with Machine Learning

Big data and predictive analytics: A systematic review of applications

Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.