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The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks. To overcome these limitations, we present an intelligent two-phase dual authentication framework that improves the security of sensor-to-server communication in IoMT environments. During the registration phase, our framework is based on Elliptic Curve Diffie-Hellman (ECDH) for rapid key exchange, and during real-time communication, our framework uses the Advanced Encryption Standard Galois Counter Mode (AES-GCM) to encrypt data securely. The efficiency of the proposed framework was rigorously tested through simulations that evaluated encryption-decryption time, computational cost, latency, and packet delivery ratio. The security resilience was also evaluated against man-in-the-middle, replay, and brute force attacks. The results show that encryption/decryption time is reduced by over 45%, overall computational cost by 45.38%, and latency by 28.42% over existing approaches. Furthermore, the framework achieved a high packet delivery ratio and strong defense against cyber threats for maintaining the confidentiality and integrity of the medical data across IoMT networks. However, the dual authentication approach doesn't affect the functionality of medical IoT devices while enhancing IoMT security, which makes it an ideal integration option for existing healthcare systems. [ABSTRACT FROM AUTHOR]

Providing security to Internet of Medical Things (IoMT) is significant worldwide problem for future generations its implementation to be successful. The traditional security methodologies developed for IoMT struggles with the specific issues of high false positives and lower detection rate. Therefore, the proposed work aims to develop a ground-breaking intrusion detection model, named as, Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA) for increasing the security of IoMT networks. Here, the data transformation and normalization processes are applied to balance the dataset's properties. Then, an Intriguing Group Teaching Optimization (IGTO) algorithm is applied to choose the most correlated and essential traits from the normalized dataset for effective intrusion detection. Consequently, a Conditional Probabilistic Deep Auto-Encoder (CPDAE) model is used to more accurately classify the type of intrusion with system complexity. This study uses the BoT-IoT, Kaggle invasion dataset, and ToN-IoT open benchmarking datasets for evaluation and performance assessments. Among all, the proposed GTPDA with its various performance metrics presented, achieves an impressive 98.8% precision, 99% recall, 98.8% F1-score, and 99% accuracy, showing its significant performance in ensuring IoMT network security. [ABSTRACT FROM AUTHOR]

Technological advancements in Information and Communication Technology (ICT) have transformed the computing paradigm, introducing various communication channels, with the Internet of Things (IoT) playing a crucial role. The Internet of Medical Things (IoMT) is a specialized category within IoT, enabling medical devices to communicate for sharing sensitive data, and improving patient care. However, these advancements also pose security and privacy challenges, including replay, man-in-the-middle, impersonation, and other attacks. To address these issues, machine learning algorithms are extensively employed in Intrusion Detection Systems (IDS) to dynamically detect and classify attacks at the network and host levels. Researchers have developed numerous supervised and unsupervised algorithms for reliable anomaly detection. The primary challenge lies in adapting IDS models to the dynamic and random behaviour of malicious attacks while designing scalable solutions. This paper explores the use of a Convolutional Neural Network (CNN) with Elephant Herding Optimization to create an effective IDS in the IoMT environment, aiming to classify and predict unforeseen cyberattacks. The CNN model undergoes pre-processing, optimization, and tuning of network parameters using hyperparameter selection methods. Experimental results, comparing the CNN with other machine learning algorithms on a benchmark intrusion detection dataset, demonstrate that the proposed model outperforms existing approaches. The CNN model exhibits a 17% increase in accuracy and a 35% decrease in time complexity, facilitating faster alerts to prevent post-effects of intrusion in sensitive cloud data storage. [ABSTRACT FROM AUTHOR]

Cardiovascular disease encompasses conditions affecting the heart and blood vessels, leading to ailments such as coronary artery disease, heart failure, and myocardial infarction (MI), which occurs when blood flow to the heart is obstructed. Early prediction of MI is vital to prevent severe damage and enhance survival rates. Traditionally, an electrocardiogram (ECG) is employed to detect cardiovascular anomalies, but its requirement for multiple electrodes placed on various body locations makes continuous monitoring difficult. Current research gaps involve the necessity for medical assistance during ECG monitoring, data variability, accuracy, early symptom prediction, and limited data availability due to the sensitive nature of medical records. To address these issues, introduce EMIP-CardioPPG, a novel mathematical framework for early MI prediction using CardioPPG, a non-invasive method that utilizes photoplethysmography (PPG) signals to monitor heart rate (HR) and detect cardiovascular abnormalities. Our approach comprises four steps: first, acquiring data from the same individual using two different sources, a self-created IoMT device and a 4-channel BIOPAC-Mp-36 device; second, preprocessing the data by denoising, filtering, normalizing, and removing motion artifacts; and third, employing mathematical calculations to determine heart rate variability (HRV) and HR, enhancing PPG signal features for early MI prediction. Fourth, evaluate our model performance using machine learning algorithms such as ridge regression, support vector classifier, independent component analysis, singular value decomposition, random forest, and XGBoost, PAN-TOMPKINS algorithm achieving overall accuracy of 97.91% for HRV from our IomT device and 98.83% for HR from our BIOPAC-MP-36. [ABSTRACT FROM AUTHOR]

This research introduces an innovative algorithm for the encryption and decryption of greyscale digital imaging and communications in medicine images utilizing Laplace transforms. The proposed method presents a ground breaking approach to image encryption, effectively concealing visual information and ensuring a robust, secure, and reliable encryption process. By leveraging the inherent strengths of Laplace transform, the algorithm guarantees the complete retrieval of the original image without any loss, provided the correct decryption key is used. To thoroughly evaluate the performance of the algorithm, multiple tests were conducted, including extensive statistical analyses and assessments of encryption quality. Key performance metrics were carefully measured, including correlation coefficients and entropy values, which ranged from 7.89 to 7.99. Additionally, the algorithm's effectiveness was demonstrated through peak signal‐to‐noise ratio values, which spanned from 7.597 to 9.915, indicating the degree of similarity between the original and encrypted images. Furthermore, the number of pixels change rate values, ranging from 99.519241 to 99.609375, highlighted the algorithm's ability to produce significantly different encrypted images from the original. The unified average changing intensity values, falling between 35.72345678 and 35.78233456, further underscored the algorithm's proficiency in altering pixel intensities uniformly. Overall, this research offers a significant advancement in the field of image encryption, combining theoretical robustness with practical efficiency. [ABSTRACT FROM AUTHOR]

The rapid advances in the Internet of Things (IoT) and the increment of its users have opened a door to put into service IoT in healthcare, known as the Internet of Medical Things (IoMT), which consists of cloud, fog, and edge computing. IoMT has shown to be a flexible framework to remove traditional healthcare limits through utilizing technology/techniques and innovative digital devices to monitor patients' conditions and to address medical service problems such as inaccuracy and long response time. It also could be helpful within critical and unexpected circumstances like epidemic diseases. Fortunately, some methods, including Edge Computing, were introduced to improve the healthcare system, make it more effective and solve problems. In this work, a Systematic Literature Review (SLR) was used to study Edge Computing solutions in healthcare, evaluate efficient therapeutic approaches, and demonstrate key factors that have not been considered in previous studies. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a notion in which smart devices seamlessly integrate with physical and virtual resources. These resources are accessible online and available to anyone to provide value-added information. The rapid advancement of technology has resulted in the creation of various features and capabilities for end-users and applications. Smart devices generate a large amount of data on the Internet of Things (IoT) and aggregate it on various server platforms, which is crucial for data processing, aggregating, and controlling. The accelerated development of technology has led to the creation of various features and capabilities for end-users and applications. Despite its benefits, IoT technology is plagued by several security and privacy concerns that must be addressed to ensure widespread acceptance. The accelerated technological progress has created a multitude of features and capabilities, yet it has also posed substantial challenges. To address this problem, researchers and practitioners have adopted numerous schemes to aggregate data while preserving data privacy. Data privacy is critical to protect against network entities inside the network, data operators, and external eavesdroppers. This survey paper delves into the security and privacy challenges associated with IoT and explores recent solutions, such as APPA, blockchain-enabled IoT, LPDA, EF-IDASC, and two secure privacy-preserving data aggregation schemes - PPLS. Additionally, it outlines several open research challenges and their anticipated solutions. [ABSTRACT FROM AUTHOR]

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

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients' privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore. In this paper, we propose an efficient three-factor cluster-based user authentication protocol (3ECAP). Specifically, we establish the security association between the user and the sensor cluster through fine-grained access control based on Merkle, which perfectly achieves the segmentation of permission. We then demonstrate that 3ECAP can address the privilege escalation attack caused by permission segmentation. Moreover, we further analyze the security performance and communication cost using formal and non-formal security analysis, Proverif, and NS3. Simulation results demonstrated the robustness of 3ECAP against various cyber-attacks and its applicability in an IoMT environment with limited storage resources. [ABSTRACT FROM AUTHOR]

There are no obvious clinical signs of a silent disease until it causes irreversible damage. As computerized technologies become more common for early detection of ailments, severity can be minimized. In order to minimize the severity of ailments, computerized technologies are being used more and more commonly for early detection through internet of things (IoT) platforms for e-Health, sometimes called the "internet of medical things (IoMT)". Nevertheless, unpredictability, inapplicability, and instability were problems with previous automated decision-making models. In this study, we present a hybrid model that combines optimum learning and iterative neighborhood component analysis (iNCA) by relying on neighborhood component analysis and feature aggregation. The support vector machine (SVM) algorithm has shown promising results when classifying numerous diseases based on water cycle algorithms (WCA). By using the WCA method, it is possible to find effective parameters at the local and global levels. In addition, the features with the lowest error level are selected from the pool of features. Hence, we developed a procedure to improve diagnostic accuracy and avoid overfitting. Using the IoMT platform, we validated the method on diabetes, hepatitis, breast cancer, and dermatology data from the UCI database. Moreover, we compared the proposed strategy with the state-of-the-art and thus, our decision-making system performed better than similar methods in identifying silent diseases. The proposed approach combines a model-oriented hybrid design with IoMT platform to assign proper treatments to patients and be both clinically applicable and appropriate for computer-aided design. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT) where medical devices communicate with one another to share sensitive data. The integration of medical devices into the IoT has greatly assisted the development of the IoMT. These advancements facilitate effective communication and providing care for patients in the healthcare sector. However, they also face specific security and privacy concerns, such as malware attacks and denial of service (DoS) attacks. To overcome this problem, intrusion detection systems (IDS) are introduced, specifically employing deep learning (DL) methodologies. This study proposes a deep learning-based binary dragonfly algorithm (BDA) with a dynamic architecture for arti- ficial neural networks2 (DAN2) model for implementing a robust and accurate IDS in IoMT. The IDS has the following stages: collection of data, preprocessing, selection of features, and classification. The IoMT dataset is employed to train the model to get improved outcomes. The standard scalar technique is used for the data preprocessing process. The BDA algorithm is used for feature selection (FS) of the preprocessed data. The DAN2 model is implemented to classify the selected data and to improve the classification accuracy. The dataset was further divided for training and testing of the model. The performance of the BDA-DAN2 model is assessed utilizing the evaluation parameters of accuracy, recall, precision, and F1-score. The BDA-DAN2 model demonstrates superior performance with 99.12% accuracy, 99.28% precision, 99.40% recall, and 98.56% F1-score during training, and 98.92% accuracy, 98.50% precision, 98.68% recall, and 97.90% F1-score during testing. Experiments confirmed that the binary dragonfly algorithm with the DAN2 (BDA-DAN2) model has the highest accuracy compared to the existing models. [ABSTRACT FROM AUTHOR]

Applications of the Internet-of-Things (IoT) in healthcare have a great potential since they bring, in a cost effective manner, supreme solutions to large scale medical-care. The Internet-of-Medical-Things (IoMT) connects patients to caregivers and facilitates remote healthcare capabilities. Regardless of their expansion, especially during the COVID19 pandemic, IoMT applications encounter critical types of security risks. Many research efforts were conducted to help designing reliable E-Health Systems (EHS), but compliance and privacy-preserving solutions for EHS still require a lot of work. To address this requirement, we focus on reliability enhancement of security policies in the context of EHS. We especially deal with risk management within the data processing and storage area, in IoMT systems, composed mainly of cloud/private databases that store confidential medical data. Malicious users and attackers can discover and leak unauthorized data via exploiting authorized information and may expand their rights by using advanced features such as database functional dependencies. In such critical systems, identifying and evaluating risks associated to non authorized accesses and policies misconfigurations is highly required. We address, in this paper, the analysis and the management of the compliance of concrete security policies based on appropriate risk metrics. Our solution enhances a well-established formal verification and validation approach that allows identifying non-compliance anomalies in concrete policies with a quantified risk-assessment approach for evaluating risks. A case of application is presented as an example to illustrate the relevance of our proposal. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things has played a dominant role in various real-time problems and given solutions via sensor signals. Monitoring the patient health status of Internet of Medical Things (IoMT) facilitates communication between wearable sensor devices and patients through a wireless network. Heart illness is one of the reasons for the increasing death rate in the world. Diagnosing the disease is done by the fusion of multi-sensor device signals. Much research has been done in predicting the disease and treating it correctly. However, the issues are accuracy, consumption time, and inefficiency. To overcome these issues, this paper proposed an efficient algorithm for fusing the multi-sensor signals from wearable sensor devices, classifying the medical signal data and predicting heart disease using the hybrid technique of kernel random forest with the Black Hole Optimization algorithm (KRF-BHO). This KRF-BHO is used for sensor data fusion, while XG-Boost is used to classify echocardiogram images. Accuracy in the training phase with multi-sensor data fusion data set of proposed work KRF-BHO with XGBoost classifier is 94.12%; in the testing phase, the accuracy rate is 95.89%. Similarly, for the Cleveland Dataset, the proposed work KRF-BHO with XGBoost classifier is 95.78%; in the testing phase, the accuracy rate is 96.21%. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) has great potential in delivering medical services. In IoMT, data users (e.g., doctors) may want to process data collected by sensors attached to data owners' body (e.g., patients). As sensors lack computing resources, confidential outsourcing the data to a server becomes necessary due to its sensitivity. Using homomorphic encryption raises limitations in secure processing. First, as decrypting the processed result requires the data owners' secret key, they must be online or share it with data users. Second, when processing is performed on the data of multiple data owners, the interaction becomes harder. Finally, if the processed result is sensitive, it lacks confidentiality as data owners may access it. In this paper, we propose a non-interactive homomorphic multi-party computation (HMPC) protocol, addressing the limitations efficiently. In HMPC, data owners encrypt their data with their own key and store it in a cloud server. Then, data users select the required data from the cloud server and outsource their own encrypted data to the server for processing. Afterwards, they decrypt the result regardless of the circuit computed and without interaction with the data owners. Our security and performance analyses demonstrate that HMPC is provably secure and applicable. [ABSTRACT FROM AUTHOR]

Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non‐target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM‐FL). We first adopt fine‐grained model trainer participation policies for federated learning, using the attribute‐based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword‐based search and model users' attribute‐based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies. [ABSTRACT FROM AUTHOR]

Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women's vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer. [ABSTRACT FROM AUTHOR]

In most developed countries, the medical healthcare system is experiencing rapid development from the stage of clinical information to the stage of information dissemination. In all of these countries, it is undeniable that the Internet of Medical Things (IoMT) technologies have contributed in order to develop information medical healthcare. In reality, the development of smart medical healthcare has been hindered by the protection of medical privacy, according to research and acceptance. This is especially true as telecommunications systems continue to expand and wireless sensor networks (WSN) develop, as well as ways to penetrate those checks that have become increasingly difficult. In the smart healthcare system, protecting users' information remains an outstanding issue. IoMT features and the protection of privacy and security have led to the development of an extended privacy homomorphism algorithm based on scrambling matrixes, an encryption algorithm enhanced by Modified RSA (mRSA), and a method of encrypted data compression that ensures data confidentiality. For the above purpose, we built a prototype system on a demo temporary domain using both hardware and software. According to the results, the proposed scheme protects E-healthcare from potential threats by providing stakeholders with a secure interface and preventing unauthorized users from accessing the mCloud, thus ensuring privacy. E-healthcare services based on cloud technology are protected by the proposed scheme because it is simple and robust. [ABSTRACT FROM AUTHOR]

Abstract Providing security to Internet of Medical Things (IoMT) is significant worldwide problem for future generations its implementation to be successful. The traditional security methodologies developed for IoMT struggles with the specific issues of high false positives and lower detection rate. Therefore, the proposed work aims to develop a ground-breaking intrusion detection model, named as, Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA) for increasing the security of IoMT networks. Here, the data transformation and normalization processes are applied to balance the dataset’s properties. Then, an Intriguing Group Teaching Optimization (IGTO) algorithm is applied to choose the most correlated and essential traits from the normalized dataset for effective intrusion detection. Consequently, a Conditional Probabilistic Deep Auto-Encoder (CPDAE) model is used to more accurately classify the type of intrusion with system complexity. This study uses the BoT-IoT, Kaggle invasion dataset, and ToN-IoT open benchmarking datasets for evaluation and performance assessments. Among all, the proposed GTPDA with its various performance metrics presented, achieves an impressive 98.8% precision, 99% recall, 98.8% F1-score, and 99% accuracy, showing its significant performance in ensuring IoMT network security.

Abstract The Internet of Things (IoT) is a notion in which smart devices seamlessly integrate with physical and virtual resources. These resources are accessible online and available to anyone to provide value-added information. The rapid advancement of technology has resulted in the creation of various features and capabilities for end-users and applications. Smart devices generate a large amount of data on the Internet of Things (IoT) and aggregate it on various server platforms, which is crucial for data processing, aggregating, and controlling. The accelerated development of technology has led to the creation of various features and capabilities for end-users and applications. Despite its benefits, IoT technology is plagued by several security and privacy concerns that must be addressed to ensure widespread acceptance. The accelerated technological progress has created a multitude of features and capabilities, yet it has also posed substantial challenges. To address this problem, researchers and practitioners have adopted numerous schemes to aggregate data while preserving data privacy. Data privacy is critical to protect against network entities inside the network, data operators, and external eavesdroppers. This survey paper delves into the security and privacy challenges associated with IoT and explores recent solutions, such as APPA, blockchain-enabled IoT, LPDA, EF-IDASC, and two secure privacy-preserving data aggregation schemes - PPLS. Additionally, it outlines several open research challenges and their anticipated solutions.

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

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people's lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results. [ABSTRACT FROM AUTHOR]

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for multiple sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the STORMS pilot feasibility study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for multiple sclerosis patients. [ABSTRACT FROM AUTHOR]

To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound. [ABSTRACT FROM AUTHOR]

Accurate and early diagnosis of COVID-19 can reduce the mortality rate caused by the disease across the globe. Computer-aided diagnosis (CAD) helps radiologists efficiently extract and diagnose the abnormal portions. The healthcare market is currently experiencing rapid development owing to the Internet of Things (IoT). This paper proposes a framework that integrates machine learning and intelligence-based e-Health service systems that can be used as an application of the Internet of Medical Things (IoMT) for the early diagnosis of COVID-19 disease. This framework consists of a classification approach for diagnosing the abnormalities in lung CT images using a whale optimisation algorithm (WOA) optimised wavelet neural network (WNN). WOA optimises the input features, initial weights, hidden nodes, momentum constant, and learning parameters of a WNN in the proposed system. The proposed approach extracts the Laws 16 Texture Energy Measures (LTEM) from the preprocessed CT lung images and classifies the abnormal regions with the help of a WNN classifier. The proposed framework is evaluated using a publicly available COVID-19 dataset that contains both theCOVID-19 and non-COVID-19 cases. The result shows that theproposed approach has a sensitivity of 82%, a specificity of 73.3%, and an accuracy of 84.8%. [ABSTRACT FROM AUTHOR]

In this era, feature clustering is a prominent technique in data mining. Feature clustering has also huge applications in biomedical research for multiple purposes including grouping, feature reduction, and many more. The Internet of Medical Things (IoMT) is a promising and emerging field of research that is having a major impact on knowledge retrieval and networking. IoMT also has significant application in biomedical research regarding remote monitoring and remote healthcare services. In this COVID-19 pandemic situation, psychological effects and human reactions have become a major concern of further research. A dataset can be reduced in size by using feature selection techniques. To facilitate subsequent processing, this will make the data easier to manage. Feature selection is also used to clean, reduce, and reduce dimensions of data. The clustering method has proven to be a powerful tool for finding patterns and structures in both labeled and unlabeled datasets. Our study basically provides various state-of-the-art methods regarding medical IoMT for remote healthcare, feature clustering for information retrieval regarding biomedical science. In this study, we have studied five different types of feature selection algorithms such as minimum redundancy maximum relevance (mRMR), random forest, normalized mutual information feature selection (NMIFS), F-test, and chi-square and five different types of clustering algorithms like hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN) clustering, K-means clustering, shrinkage clustering, and fuzzy C-means clustering. Finally, this study is very useful to understand and apply the appropriate IoMT, feature clustering, and catharsis on the various biomedical applications for the benevolence of society. [ABSTRACT FROM AUTHOR]

The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the healthcare industry has significantly boosted the importance of real-time remote patient monitoring. The Internet of Medical Things (IoMT) systems facilitate the seamless transfer of health records to data centers, allowing medical professionals and caregivers to analyze, process, and access them. This data is often stored in cloud-based systems. Nevertheless, the transmission of data and execution of computations in a cloud environment may lead to delays and affect the efficiency of real-time healthcare services. In addition, the use of edge computing (EC) layers has become prevalent in performing local data processing and storage to reduce service response times for IoMT applications. The main objective of this article is to develop an adaptive EC infrastructure for IoMT systems, with a specific emphasis on maintaining optimal performance for real-time health services. It also designs a model to predict the server resources required to meet service level agreements (SLAs) regarding response time. Simulation results demonstrate that EC significantly improves service response time for real-time IoMT applications. The proposed model can accurately and efficiently predict the computing resources required for medical data services to achieve SLAs under varying workload conditions. [ABSTRACT FROM AUTHOR]

Biofeedback therapy is useful for treatment of functional defecation disorders but is not widely available and is labor intensive. We developed an Internet-of-Medical-Things (IoMT) device, enabling self-guided biofeedback therapy. This study assesses the safety and efficacy of self-guided biofeedback therapy using the IoMT device in comparison to standard operator-led therapy. Patients experiencing urge or seepage fecal incontinence (≥1 episode/week) were randomly assigned to either our IoMT system or to the conventional anorectal manometry-based therapy. Both interventions comprised six weekly sessions, focusing on enhancing anal strength, endurance, and coordination. The novel device facilitated self-guided therapy via visual instructions on a companion app. Primary outcomes included safety/tolerability, changes in Vaizey severity scores, and alterations in anorectal pressure profiles. Twenty-five patients (22 females, 3 males) participated, with 13 in the novel device group and 12 in the standard therapy group. Both groups showed significant reductions in symptom severity scores: IoMT device group -4.2 (95% CI: -4.06, -4.34, p = 0.018), and the standard therapy group -4.8 (95% CI: -4.31, -5.29, p = 0.028). Anal sphincter resting pressure and sustained squeeze time improved significantly in both groups, and the novel device group demonstrated an increase in maximum sphincter squeeze pressure. There were no significant differences between the therapy groups. Importantly, the experimental device was well-tolerated compared with standard therapy, with no serious adverse events observed. This study demonstrates the comparable efficacy of self-administered biofeedback using the IoMT device with traditional biofeedback therapy. The results demonstrates the potential of the IoMT device as a safe, self-guided method for FI therapy, offering convenience and effectiveness in fecal incontinence management. [ABSTRACT FROM AUTHOR]

Lung cancer emerges as a major factor in cancer-related fatalities in the current generation, and it is predicted to continue having a long-term impact. Detecting symptoms early becomes crucial for effective treatment, underscoring innovative therapy's necessity. Many researchers have conducted extensive work in this area, yet challenges such as high false-positive rates and achieving high accuracy in detection continue to complicate accurate diagnosis. In this research, we aim to develop an ecologically considerate lung cancer therapy prototype model that maximizes resource utilization by leveraging recent advancements in computational intelligence. We also propose an Internet of Medical Things (IoMT)-based, consumer-focused integrated framework to implement the suggested approach, providing patients with appropriate care. Our proposed method employs Logistic Regression, MLP Classifier, Gaussian NB Classifier, and Intelligent Feature Selection using K-Means and Fuzzy Logic to enhance detection procedures in lung cancer dataset. Additionally, ensemble learning is incorporated through a voting classifier. The proposed model's effectiveness is improved through hyperparameter tuning via grid search. The proposed model's performance is demonstrated through comparative analysis with existing NB, J48, and SVM approaches, achieving a 98.50% accuracy rate. The efficiency gains from this approach have the potential to save a significant amount of time and cost. This study underscores the potential of computational intelligence and IoMT in developing effective, resource-efficient lung cancer therapies. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things (IoMT), the vulnerability of federated learning (FL) to single points of failure, low‐quality nodes, and poisoning attacks necessitates innovative solutions. This article introduces a FL‐driven dual‐blockchain approach to address these challenges and improve data sharing and reputation management. Our approach comprises two blockchains: the Model Quality Blockchain (MQchain) and the Reputation Incentive Blockchain (RIchain). MQchain utilizes an enhanced Proof of Quality (PoQ) consensus algorithm to exclude low‐quality nodes from participating in aggregation, effectively mitigating single points of failure and poisoning attacks by leveraging node reputation and quality thresholds. In parallel, RIchain incorporates a reputation evaluation, incentive mechanism, and index query mechanism, allowing for rapid and comprehensive node evaluation, thus identifying high‐reputation nodes for MQchain. Security analysis confirms the theoretical soundness of the proposed method. Experimental evaluation using real medical datasets, specifically MedMNIST, demonstrates the remarkable resilience of our approach against attacks compared to three alternative methods. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios. [ABSTRACT FROM AUTHOR]

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Intrusion detection in the IoMT (Internet of Medical Things) represents the process of keeping track of and discovering unauthorized or malicious actions in medical devices and networks. Some of its benefits include early detection of potential threats, prevention of data breaches, and protection of patient privacy. Aside from these benefits, some difficulties are evident, like alarm fatigue due to false positives, the complexity in the standardizing detection across different devices, and resource limits that hinder qualitative implementations, thus leaving some vulnerabilities in the healthcare infrastructure. This paper proposes a new Efficient Intrusion Detection model based on the Correlation-Based Feature Selection and the OptCNN-LSTM model to address these problems. The proposed methodology comprises five key phases: (i) Data Acquisition (ii) Pre-processing (iii) Feature Extraction (iv) Feature Selection (v) OptCNN-LSTM Model-based intrusion detection. The raw data is first gathered and then preprocessed using z-score normalization and data cleaning. Then, the best features are extracted using central tendency, the degree of dispersion, and correlation. A mixed IHHO-PSO feature with the Correlation-based Feature Selection (CFS) framework is employed to choose the best features amongst the collected features. At last, the OptCNN-LSTM model is performed to detect the intrusion in the IoMT based on features-selected data. The CNN is tuned using the Levy Flight Optimization (LF) which can be further combined with the LSTM to get the expected results. The code is written in Python and the model is then run to determine its performance which is measured in terms of accuracy, precision, f-measure, and a Receiver Operating Characteristic Curve (ROC). Compared to the current models, the proposed model has the highest accuracies 97.6% and 96.5% for learning rates 70 and 80, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has paved the way for innovative approaches to collecting and managing medical data. With the large and sensitive medical data being processed hence, the need for a strong identity and privacy become necessary. The present paper suggests a comprehensive method of PriMedGuard which aims at protection of the personal medical information. The first stage will be data collection from devices and sensors, then data cleaning to transform the data into the required format. There is also a safety system in the system that registers and authenticates authorized entities as well as ETDO (Enhanced Tasmanian Devil Optimization algorithm) is used for generating asymmetric cryptographic keys. The data is encrypted using the Secure Bit-Count Transmutation (SBCT) Data Encryption Algorithm and then put in the locations provided by the InterPlanetary File System (IPFS), a decentralized and distributed storage system. A safe smart contract on the blockchain is created so that the data retrieval is secure and MedSecEnsemble Detection is proposed as an intrusion detection technique in the IoMT network. By using this method, data will stay available while at the same time integrity, confidentiality and protection against vulnerabilities are ensured. Hence, the Internet of Medical Things ecosystem will be secured from unauthorized access and possible security threats. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare. [ABSTRACT FROM AUTHOR]

The global burden of disease caused by cardiovascular diseases (CVDs) is increasing despite technical advancements in healthcare because of a dramatic rise in the developing nations that are experiencing rapid health transitions. The World Health Organization (WHO) estimates 17.9 million deaths worldwide in 2021 and is connected to CVDs, or 32% of all deaths. Since ancient times, people have experimented with the methods that extend their lives. The proposed technology is still a long way for attaining the aim of lessening the mortality rates. Early detection and proactive management of CVD risk factors are crucial for reducing the burden of these diseases. In recent years, researchers have been exploring the potential of deep learning methods for predicting cardiovascular disease risk depending upon data collected from IoMT devices. Deep learning (DL) methods used for cardiovascular diseases prediction have been popular in this domain. Several DL techniques are implemented to accomplish efficient prediction-based CVD. There are several steps in the CVD employing deep learning model. IoT sensors and deep learning techniques are used to process large amounts of patient-related biomedical data, enabling doctors to closely monitor their patients and make choices in real-time. An outline of the IoT, sensors, and deep learning is provided after a discussion of cardiac disease and its existing treatments. A complete analysis of the current and pertinent deep-learning techniques for heart disease prediction is reviewed. The result shows the performance metrics of the comparison of different deep learning approaches. This review is undertaken by pulling data from 44 papers published between the years 2020 and 2023, provides a thorough statistical analysis. Finally, this survey will be beneficial for CVD prediction researchers. [ABSTRACT FROM AUTHOR]

As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization. [ABSTRACT FROM AUTHOR]

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper. [ABSTRACT FROM AUTHOR]

This paper proposes a learning-based model for the resource-constrained edge nodes in the blockchain-enabled Internet of Medical Things (IoMT) systems to realize efficient querying. Three layers are designed in the new model: data evaluation layer, data storage layer and data distribution layer. The data evaluation layer extracts the features from medical data and evaluates their values based on the Extreme Learning Machine (ELM) method. Then, in the data storage layer, according to the value of medical data, a novelty data structure called Merkle–Huffman tree (M-H tree) is established. Compared with the Merkle tree, high-value data (frequently accessed data) in M-H tree is saved closer to the root node and can be found faster. In the data distribution layer, the sharding-based blockchain model is adopted to increase the storage scalability of the IoMT system. Finally, the experimental results show that the new learning-based model can effectively improve the query speed of the blockchain-enabled medical system by about 3.5% and free up large amounts of storage space on IoMT devices. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of a portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. The follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad applications in preventative healthcare. [ABSTRACT FROM AUTHOR]

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy. [ABSTRACT FROM AUTHOR]

Traditional triage tools hospitals use face limitations in handling the increasing number of patients and analyzing complex data. These ongoing challenges in patient triage necessitate the development of more effective prediction methods. This study aims to use machine learning (ML) to create an automated triage model for remote patients in telemedicine systems, providing more accurate health services and health assessments of urgent cases in real time. A comparative study was conducted to ascertain how well different supervised machine learning models, like SVM, RF, DT, LR, NB, and KNN, evaluated patient triage outcomes for outpatient care. Hence, data from diverse, rapidly generated sources is crucial for informed patient triage decisions. Collected through IoMT-enabled sensors, it includes sensory data (ECG, blood pressure, SpO2, temperature) and non-sensory text frame measurements. The study examined six supervised machine learning algorithms. These models were trained using patient medical data and validated by assessing their performance. Supervised ML technology was implemented in Hadoop and Spark environments to identify individuals with chronic illnesses accurately. A dataset of 55,680 patient records was used to evaluate methods and determine the best match for disease prediction. The simulation results highlight the powerful integration of ML in telemedicine to analyze data from heterogeneous IoMT devices, indicating that the Decision Tree (DT) algorithm outperformed the other five machine learning algorithms by 93.50% in terms of performance and accuracy metrics. This result provides practical insights for developing automated triage models in telemedicine systems. [ABSTRACT FROM AUTHOR]

In wearable optical sensing applications whose target tissue is not superficial, such as deep tissue oximetry, the task of embedded system design has to strike a balance between two competing factors. On one hand, the sensing task is assisted by increasing the radiated energy into the body, which in turn, improves the signal-to-noise ratio (SNR) of the deep tissue at the sensor. On the other hand, patient safety consideration imposes a constraint on the amount of radiated energy into the body. In this paper, we study the trade-offs between the two factors by exploring the design space of the light source activation pulse. Furthermore, we propose BASS, an algorithm that leverages the activation pulse design space exploration, which further optimizes deep tissue SNR via spectral averaging, while ensuring the radiated energy into the body meets a safe upper bound. The effectiveness of the proposed technique is demonstrated via analytical derivations, simulations, and in vivo measurements in both pregnant sheep models and human subjects.