Smart Healthcare Research Articles

SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems

Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a cybersecurity perspective. Edge intelligence-enabled federated learning is a new scheme that utilises decentralised computing that allows data analytics to be carried out at the edge of a network, enhancing data privacy. However, this scheme suffers from privacy attacks, including inference, free-riding, and man-in-the-middle attacks, especially with serverless computing for allocating resources to user needs. Edge intelligence-enabled federated learning requires client data insertion and deletion to authenticate genuine clients and a serverless computing capability to ensure the security of collaborative machine learning models. This work introduces a serverless privacy edge intelligence-based federated learning (SPEI-FL) framework to address these issues. SPEI-FL includes a federated edge aggregator and authentication method to improve the data privacy of federated learning and allow client adaptation and removal without impacting the overall learning processes. It also can classify intruders through serverless computing processes. The proposed framework was evaluated with the unstructured COVID-19 medical chest x-rays and MNIST digit datasets, and the structured BoT-IoT dataset. The performance of the framework is comparable with existing authentication methods and reported a higher accuracy than comparable methods (approximately 90% as compared with the 81% reported by peer methods). The proposed authentication method prevents the exposure of sensitive patient information during medical device authentication and would become the cornerstone of the next generation of medical security with serverless computing.

Smart Healthcare Based Cyber Physical System Modeling by Block Chain with Cloud 6G Network and Machine Learning Techniques

Smart Healthcare Based Cyber Physical System Modeling by Block Chain with Cloud 6G Network and Machine Learning Techniques

A biocompatible triboelectric nanogenerator-based edible electronic skin for morse code transmitters and smart healthcare applications

Skin-inspired electronic devices have gained immense interest due to their application in prosthetics, healthcare, robotics, and humanmachine interactions. Mimicking human skin and incorporating multifunctional properties such as stretchability, flexibility, biocompatibility, biodegradability, and paving the way for self-powered electronic skin (e-skin) is highly desirable. The present work reports the fabrication of biocompatible triboelectric nanogenerator (TENG)-based edible electronic skins made of a biocompatible chitosan film. The fabricated chitosan film was engineered using plasticizers to enhance its flexibility and mechanical strength. To make the entire device biocompatible, edible gold leaf, which is used as a decorative layer in the sweets of the Indian subcontinent, has been used as an electrode. Both the chitosan film and the edible gold electrode were studied for their biocompatibility by immersion in acetic acid and H2O2, where the films were completely dissolved in 1 h and 50 h, respectively. The e-skins were used as the active layer in the triboelectric nanogenerator by working under both vertical contact separation and single electrode modes of triboelectrification. Furthermore, the fabricated device has been used for real-time applications in self-powered sleep monitoring systems as well as for wearable Morse code transmitters. The electrical response and performance in real-time applications prove that the developed e-skin-based device is a promising candidate for future wearable applications in health care, humanmachine interactions and haptics.

Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems

Cardiac patients require prompt and effective treatment to prevent heart attacks through accurate prediction of heart disease. The prognosis of heart disease is complex and requires advanced knowledge and expertise. Healthcare systems are increasingly integrated with the internet of things (IoT) to collect data from sensors for diagnosing and predicting diseases. Current methods employ machine learning (ML) for these tasks, but they often fall short in creating an intelligent framework due to difficulties in handling high-dimensional data. A groundbreaking health system leverages IoT and an optimized long short-term memory (LSTM) algorithm, enhanced by the red deer (RD) algorithm, to accurately diagnose cardiac issues. Continuous monitoring of blood pressure and electrocardiograms (ECG) is conducted through heart monitor devices and smartwatches linked to patients. The gathered data is combined using a feature fusion approach, integrating electronic medical records (EMR) and sensor data for the extraction process. The RD-LSTM model classifies cardiac conditions as either normal or abnormal, and its performance is benchmarked against other deep-learning (DL) models. The RD-LSTM model showed better improvement in prediction accuracy over previous models.

Enhancing privacy and security in smart healthcare: A blockchain-powered decentralized data dissemination scheme

Dissemination of health data in smart healthcare allows a patient’s health information to be accessible across healthcare communities for the improvement of patients’ care services. In our work, data collected from a patient’s automated health device is distributed to the communities through the blockchain network. Blockchain technology paves the way for the health sector to run in a decentralized system. To achieve a secure decentralized data dissemination, the transmitted health data must be reliable, that is, data is assured originated from a legitimate device without unlawful alteration. However, verification of such reliability may jeopardize a patient’s privacy. In healthcare, privacy is of the utmost importance to protect sensitive health data. At the same time, should misbehavior arise, malicious entities should be held accountable. We satisfy these conflicting requirements of reliability, privacy and accountability with our proposed secure decentralized data dissemination scheme for smart healthcare that integrates a certificate-based signcryption with a proxy re-encryption (Sign-Proxy) scheme and an Improved Delegated Proof-of-Stake (DPoS) consensus protocol for the system construction. The Sign-Proxy provides users a desirable level of security while delivering lightweight computation for limited computing capabilities of automated health devices. In parallel, the Improved DPoS facilitates the streamlined and secure adoption of a consortium blockchain framework. The security and performance analysis indicate that the proposed scheme is resilience against adversarial attacks, achieves overall efficiency, and feasible for real-world deployment.

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

Cyber Security Risk Assessment Methods for Smart Healthcare

The perverseness of the Internet of Things (IoT) has reached the healthcare sector, where interconnected medical devices are transforming how we deliver and manage health. These devices, linked wirelessly, create a vast network known as Internet of Medical Things (IoMT), that integrates seamlessly with the broader healthcare system. This interconnected infrastructure facilitates the exchange of massive amounts of patient data, paving the way for a more distributed and data-driven approach to healthcare. In healthcare’s ICT environment rely heavily on electronic health records (EHR), e-prescribing systems and other systems. Protecting this sensitive data necessitates robust cybersecurity measures. Even though data security is an indirect cost, it is crucial for healthcare systems. In general, patient trust in a healthcare system depends on the infrastructure’s ability to protect data from security and privacy threats. A critical component of this strategy is security risk assessment. A risk is an indirect cost borne by those designing these systems. The risk assessment process identifies, evaluates, and prioritizes potential threats to the organization’s assets including hardware and software. By assessing risks before acting, healthcare organizations can effectively allocate resources to mitigate the most critical vulnerabilities. While broadly applicable cyber risk assessment frameworks like NIST, ISO, and OCTAVE exist, they may lack a strong risk between assets, threats and impacts, and controls. Therefore, they do not provide a comprehensive picture for healthcare specifically. A more thorough examination is required to establish a strong association between protected systems, potential threats, and the resulting risks. This paper critically reviews these frameworks and their methodologies and limitations for healthcare cybersecurity. Even though data security is an indirect cost, it is crucial for healthcare systems.

WSOMedMark: Robust and optimized dual image watermarking using RDWT and its authentication using BRISK and MSER features for smart healthcare system

WSOMedMark: Robust and optimized dual image watermarking using RDWT and its authentication using BRISK and MSER features for smart healthcare system

Wearable multichannel-active pressurized pulse sensing platform

With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human–computer interaction. Due to the high sensitivity (460.1 kPa−1), high linearity (R2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual’s blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

RASNet: Recurrent aggregation neural network for safe and efficient drug recommendation

Drug recommendation is one of the most crucial research topics in smart healthcare. Its goal is to provide a set of safe drug combination based on the patient’s electronic health records (EHRs). Drug recommendation is challenging because it is difficult to obtain an appropriate representation of patient’s health state from these personalized historical records. Meanwhile, drug recommendation must prioritize the safety of drug combination because drug–drug interactions (DDIs) could result in side effects. To address these issues, this paper proposes a novel recurrent aggregation neural network for safe drug recommendation, called RASNet. RASNet introduces a straightforward but efficient recurrent aggregation neural network to capture historical records related to the patient’s health state of the current visit, which could improve the performance of EHR-based personalized modeling, particularly in cases where the patient’s condition changes periodically. Furthermore, this paper presents a novel exponential controller for DDI loss to enhance the safety of drug combination. The proposed controller not only balances the DDI rate between the safety and accuracy of the drug recommendation but also ensures the performance even when the DDI rate is low. Extensive experiments on the MIMIC-III dataset demonstrate that RASNet achieves state-of-the-art performance. Moreover, RASNet exhibits excellent efficiency and safety in drug recommendation.

What Is the Real-Life Experience of Older Adults on Smart Healthcare Technologies? An Exploratory Interview Study

Introduction: Smart healthcare technologies (SHCTs) exhibit the great potential to support older Hong Kong adults with their health problems. Although there are various SHCTs in the Hong Kong market, and some adoption predictors have been proposed and investigated, little is known about older users’ views on and real-life experiences with these technologies. This exploratory study examined the experiences, functional needs, and barriers of three kinds of SHCT (i.e., smart wearable devices, smart health monitors, and healthcare applications) with older adults in real life. Methods: A convenience sampling method was applied to recruit twenty-two older adults from the Hong Kong community. The interview was designed in semi-structured and conducted in a face-to-face setting. The content analysis was used to summarize the older adults’ functional needs and barriers in real life. Results: We found older adults mainly applied SHCTs to address physical health, but there are few technological solutions for mental health in practice. There are four types of barriers in using SHCT. However, social support in Hong Kong community greatly helps reduce the barriers in technology use. Based on the findings, we discussed the possible solutions based on the social and technology perspective. Conclusion: Current technologies still could not fully address older adults’ needs for healthy aging, and various barriers still hinder the actual adoption. By deeply understanding and considering the social context, technology innovation can facilitate the adoption of SHCT and promote a healthy aging society.

Waste plastics to prepare eutectogel: Wearable, highly deformable, transparent flexible ionic conductors

The emergence of flexible conductor wearable electronics will revolutionize motion detection and smart healthcare. Eutectogel (E-GEL) is more economical, resistance to extreme temperatures, green and environmentally friendly compared to ionic gels and hydrogels. PET is generally recycled as a conventional polymer. This paper presents a new process for the production of flexible ionic conductor from PET. Here, we using waste PET bottles as raw material, and the green, ternary low-eutectic solvent (DES) ChCl/EG/Zn(Ac)2 as a catalyst, synthesized waterborne polyurethane by the one-pot method. By modulating the types and densities of multiple hydrogen bonds and supramolecular interactions of binary DES (ChCl/EG), using WPU, P(AAm-AA) and ChCl/EG, responsive E-GELs with excellent mechanical (stress of 8.87 MPa, strain of 680 %), excellent ionic conductivity. In addition, the E-GEL can be loaded-unloaded continuously for 1000 cycles at 25 % strain, that demonstrates excellent fatigue resistance. DES give eutectogel the ability to resist both −50 ℃ to 80 ℃ temperatures. As a demonstration of the device performance, wearable strain sensors assembled using E-GEL can monitor and recognize complex human movements in a fast, real-time manner.

Cyber Physical System Based Smart Healthcare System with Deep Learning Architectures with Data Analytics

Cyber Physical System Based Smart Healthcare System with Deep Learning Architectures with Data Analytics

INTEGRATING MACHINE LEARNING AND BIG DATA ANALYTICS FOR REAL-TIME DISEASE DETECTION IN SMART HEALTHCARE SYSTEMS

The integration of machine learning (ML) and big data analytics within smart healthcare systems represents a transformative advancement in medical services, emphasizing efficiency, accuracy, and patient-centered care. This paper investigates the application of these advanced technologies in real-time disease detection, showcasing their potential to revolutionize healthcare delivery. Smart healthcare systems leverage a multitude of technological components, including Internet of Things (IoT) devices, sensors, and artificial intelligence (AI), to enable continuous monitoring and diagnostics. This real-time monitoring facilitates prompt interventions and treatment adjustments, which is particularly advantageous for managing chronic conditions and acute illnesses where timely responses are critical to improving patient outcomes. Despite the evident benefits, traditional healthcare infrastructures face significant challenges such as delays in diagnosis due to manual processes, inefficient data handling resulting in data silos, and limited interoperability between different healthcare providers, leading to worsened health outcomes and increased healthcare costs. The integration of ML and big data analytics offers promising solutions to these challenges. ML algorithms can process vast amounts of healthcare data to identify patterns and predict outcomes with high accuracy, such as recognizing early signs of diseases like cancer or diabetes from medical images or electronic health records (EHRs). Big data analytics complements ML by providing the necessary infrastructure to handle and process large volumes of health data, enabling the collection, storage, and analysis of structured data from EHRs, unstructured data from clinical notes, and real-time data from wearable devices. By integrating these technologies, healthcare providers can gain deeper insights into patient health trends and outcomes, leading to more informed decision-making and better patient management. This study employs a qualitative research design, focusing on five genuine case studies: the Mayo Clinic's predictive analytics for heart disease, Cleveland Clinic's use of ML for cancer diagnosis, Kaiser Permanente's diabetes management program, Johns Hopkins Hospital's sepsis detection system, and Mount Sinai Health System's genomic data analysis. Each case study is chosen for its relevance and comprehensive data, detailing the specific healthcare environment and context. This paper interprets these findings in the broader context of smart healthcare systems and existing literature, emphasizing the importance of these technologies in modernizing healthcare and addressing inefficiencies. The challenges encountered during integration, such as data privacy concerns and interoperability issues, are examined along with implemented solutions.

Deep reinforcement learning based latency-energy minimization in smart healthcare network

Significant breakthroughs in the Internet of Things (IoT) and 5G technologies have driven several smart healthcare activities, leading to a flood of computationally intensive applications in smart healthcare networks. Mobile Edge Computing (MEC) is considered as an efficient solution to provide powerful computing capabilities to latency or energy sensitive nodes. The low-latency and high-reliability requirements of healthcare application services can be met through optimal offloading and resource allocation for the computational tasks of the nodes. In this study, we established a system model consisting of two types of nodes by considering nondivisible and trade-off computational tasks between latency and energy consumption. To minimize processing cost of the system tasks, a Mixed-Integer Nonlinear Programming (MINLP) task offloading problem is proposed. Furthermore, this problem is decomposed into task offloading decisions and resource allocation problems. The resource allocation problem is solved using traditional optimization algorithms, and the offloading decision problem is solved using a deep reinforcement learning algorithm. We propose an Online Offloading based on the Deep Reinforcement Learning (OO-DRL) algorithm with parallel deep neural networks and a weight-sensitive experience replay mechanism. Simulation results show that, compared with several existing methods, our proposed algorithm can perform real-time task offloading in a smart healthcare network in dynamically varying environments and reduce the system task processing cost.

Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.

A Novel Collaborative SRU Network With Dynamic Behaviour Aggregation, Reduced Communication Overhead and Explainable Features.

Leakage and tampering problems in collection and transmission of biomedical data have attracted much attention as these concerns instigates negative impression regarding privacy, security, and reputation of medical networks. This article presents a novel security model that establishes a threat-vector database based on the dynamic behaviours of smart healthcare systems. Then, an improved and privacy-preserved SRU network is designed that aims to alleviate fading gradient issue and enhance the learning process by reducing computational cost. Then, an intelligent federated learning algorithm is deployed to enable multiple healthcare networks to form a collaborative security model in a personalized manner without the loss of privacy. The proposed security method is both parallelizable and computationally effective since the dynamic behaviour aggregation strategy empowers the model to work collaboratively and reduce communication overhead by dynamically adjusting the number of participating clients. Additionally, the visualization of the decision process based on the explainability of features enhances the understanding of security experts by enabling them to comprehend the underlying data evidence and causal reasoning. Compared to existing methods, the proposed security method is capable of thoroughly analyzing and detecting severe security threats with high accuracy, reduce overhead and lower computation cost along with enhanced privacy of biomedical data.

Channel Adaptive and Sparsity Personalized Federated Learning for Privacy Protection in Smart Healthcare Systems.

With the booming development of Smart Healthcare Systems (SHSs), employing federated learning (FL) in SHS devices has become a research hotspot. FL, as a distributed learning framework, can train models without sharing the original data among users, and then protect the user privacy. Existing research has proposed many methods to improve the security and efficiency of FL, which may not fully consider the characteristics of SHSs. Specifically, the requirements of privacy protection and efficiency pose significant challenges to FL. Current studies have struggled to balance privacy security and efficiency, and the degradation of model training efficiency in SHSs can be critical to patient health. Therefore, to improve the privacy protection of healthcare data and ensure communication efficiency, this work proposes a novel personalized FL framework based on Communication quality and Adaptive Sparsification (pFedCAS). In order to achieve privacy protection, a control unit is proposed and introduced to adjust the sparsity of the local model adaptively. To further improve the training efficiency, a selection unit is added during global model aggregation to select suitable clients for parameter updates. Finally, we validate the proposed method operated on the HAM10000 dataset. Simulation results validate that pFedCAS can not only improve privacy protection, but also gain an improvement of 15% in training accuracy and a reduction of 30% in training costs based on communication quality. The simulation results also validate the excellent robustness of pFedCAS to non-iid data.

Editorial AI Driven Internet of Medical Things for Smart Healthcare Applications: Challenges and Future Trends

Editorial AI Driven Internet of Medical Things for Smart Healthcare Applications: Challenges and Future Trends