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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]

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system's potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical "220-age" formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a rapidly expanding network comprising medical devices, sensors, and software that collect and exchange patient health data. Today, the IoMT has the potential to revolutionize healthcare by offering more personalized care to patients and improving the efficiency of healthcare delivery. However, the IoMT also introduces significant privacy concerns, particularly regarding data privacy. IoMT devices often collect and store large amounts of data about patients' health. These data could be used to track patients' movements, monitor their health habits, and even predict their future health risks. This extensive data collection and surveillance could be a major invasion of patient privacy. Thus, privacy-preserving research in an IoMT context is an important area of research that aims to mitigate these privacy issues. This review paper comprehensively applies the PRISMA methodology to analyze, review, classify, and compare current approaches of preserving patient data privacy within IoMT blockchain-based healthcare environments. [ABSTRACT FROM AUTHOR]

With the rapid growth of the Internet of Things (IoT), the Internet of Medical Things (IoMT) has emerged as a critical sector that enhances convenience and plays a vital role in saving lives. IoMT devices facilitate remote access and control of various medical tools, significantly improving accessibility in the healthcare field. However, the connectivity of these devices to the internet makes them vulnerable to adversarial attacks. Safeguarding medical data becomes a paramount concern, particularly when precise biometric readings are required without compromising patient safety. This paper proposes a fast encryption mechanism to protect the color information in medical videos utilized within the IoMT environment. Our approach involves scrambling medical video frames using a rapid block-splitting method combined with simple operations. Subsequently, the scrambled frames are encrypted using different keys generated from the logistic map. To ensure the practicality of our proposed method in the IoMT setting, we implement the encryption mechanism on a cost-effective Raspberry Pi platform. To evaluate the effectiveness of our proposed mechanism, we conduct comprehensive simulations and security analyses. Notably, we investigate medical test videos during the evaluation process, further validating the applicability of our method. The results confirm our proposed mechanism's robustness by hiding patterns in original videos, achieving high entropy to increase randomness in encrypted videos, reducing the correlation between adjacent pixels in encrypted videos, and resisting various attacks. [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]

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 healthcare sector has undergone a profound transformation, owing to the influential role played by Internet of Medical Things (IoMT) technology. However, there are substantial concerns over these devices' security and privacy-preserving mechanisms. The current literature on IoMT tends to focus on specific security features, rather than wholistic security concerning Confidentiality, Integrity, and Availability (CIA Triad), and the solutions are generally simulated and not tested in a real-world network. The proposed innovative solution is known as Secure-by-Design Real-Time IoMT Architecture for e-Health Population Monitoring (RTPM) and it can manage keys at both ends (IoMT device and IoMT server) to maintain high privacy standards and trust during the monitoring process and enable the IoMT devices to run safely and independently even if the server is compromised. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices' overheads, and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system, i.e., the CIA Triad, and conducts device authentication, protects from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, and prevents non-repudiation attacks in real time. A self-healing solution during the network failure of live e-health monitoring is also incorporated in RTPM. The robustness and stress of the system are tested with different data types and by capturing live network traffic. The system's performance is analysed using different security algorithms with different key sizes of RSA (1024 to 8192 bits), AES (128 to 256 bits), and SHA (256 bits) to support a resource-constraint-powered system when integrating with resource-demanding secure parameters and features. In the future, other security features like intrusion detection and prevention and the user's experience and trust level of such a system will be tested. [ABSTRACT FROM AUTHOR]

Embedded devices are used in many domains, including healthcare, industries, and home automation, all of which entail significant workloads. As a direct consequence, the embedded devices require retrieval and processing of data of large volume, which occupy large memory space in the embedded devices. Compression along with workload characterization is an effective technique for minimizing memory usage and to the improvement of endurance of memory in such devices. This paper investigates the embedded workload characterization using Extreme Learning Machine (ELM) that is particularly suitable for large-scale datasets and real-time applications. Though ELM is single-layer feedforward network, its input weight randomization has a considerable effect on the accuracy of the classification. In this paper, the authors have proposed a hybrid algorithm for the optimization of the randomization of ELM. The Particle Swarm Optimizer (PSO), the Genetic Algorithm (GA), the Ant Colony Optimizer (ACO), and the Whale Optimization Algorithm (WOA) were used in the optimization of the classification process in ELM to increase the accuracy of the results. The input data must be categorized based on the energy consumed by each workload to proceed with further processing according to the system requirements. This paper explores and analyses the performance of hybridized ELM-Genetic algorithm (ELM-GA), ELM-Particle Swarm Optimization (PSO), ELM-Ant Colony Optimizer (ELM-ACO), and ELM- Whale Optimization Algorithm (ELM-WOA) optimization algorithms. The embedded benchmarks Internet of Medical Things (IoMT), Mi Benchmark (MiBench), and Embedded Microprocessor Benchmark Consortium (EEMBC) have been used as a dataset for classification in this paper. The extensive experimental study shows that the hybridized ELM-WOA provides a 98.5 % classification accuracy, 98.1 % specificity, and 98 % sensitivity compared to the other optimization methods discussed in this paper.

Abstract 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.

The healthcare sector has undergone a profound transformation, owing to the influential role played by Internet of Medical Things (IoMT) technology. However, there are substantial concerns over these devices’ security and privacy-preserving mechanisms. The current literature on IoMT tends to focus on specific security features, rather than wholistic security concerning Confidentiality, Integrity, and Availability (CIA Triad), and the solutions are generally simulated and not tested in a real-world network. The proposed innovative solution is known as Secure-by-Design Real-Time IoMT Architecture for e-Health Population Monitoring (RTPM) and it can manage keys at both ends (IoMT device and IoMT server) to maintain high privacy standards and trust during the monitoring process and enable the IoMT devices to run safely and independently even if the server is compromised. However, the session keys are controlled by the trusted IoMT server to lighten the IoMT devices’ overheads, and the session keys are securely exchanged between the client system and the monitoring server. The proposed RTPM focuses on addressing the major security requirements for an IoMT system, i.e., the CIA Triad, and conducts device authentication, protects from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, and prevents non-repudiation attacks in real time. A self-healing solution during the network failure of live e-health monitoring is also incorporated in RTPM. The robustness and stress of the system are tested with different data types and by capturing live network traffic. The system’s performance is analysed using different security algorithms with different key sizes of RSA (1024 to 8192 bits), AES (128 to 256 bits), and SHA (256 bits) to support a resource-constraint-powered system when integrating with resource-demanding secure parameters and features. In the future, other security features like intrusion detection and prevention and the user’s experience and trust level of such a system will be tested.

The consistency and duration of the menstrual cycle exhibit significant associations with specific psychiatric conditions throughout an individual’s lifespan. The proposed methodology surveys the relationship between psychiatric disorders and the length or regularity of the menstrual cycle and analyzes the difficulties undergone by the women. A comprehensive dataset is generated and a mathematical model using an exploratory data analytics approach is developed, in order to establish a correlation between these variables. It utilizes a cyclic methodology, leveraging shared menstrual data and a predictive model derived from vehicles to enhance network learning. A decentralized secure learning procedure is implemented to ensure data privacy and security. The transfer learning techniques helps to enhance the ability to learn from diverse data distributions in IoMT (Internet of Medical Things) networks, improve the robustness of the learning process. This approach presents a practical and effective solution for IoMT network learning, allowing each participant to contribute their individual features to collectively extract valuable insights from the data. The decentralization facilitates end-users in accessing their personal medical records while ensuring privacy, irrespective of their location and time. This system also achieves a minimal delay sensitivity of 3.2%, by providing timely access to the required information.

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]

The consistency and duration of the menstrual cycle exhibit significant associations with specific psychiatric conditions throughout an individual’s lifespan. The proposed methodology surveys the relationship between psychiatric disorders and the length or regularity of the menstrual cycle and analyzes the difficulties undergone by the women. A comprehensive dataset is generated and a mathematical model using an exploratory data analytics approach is developed, in order to establish a correlation between these variables. It utilizes a cyclic methodology, leveraging shared menstrual data and a predictive model derived from vehicles to enhance network learning. A decentralized secure learning procedure is implemented to ensure data privacy and security. The transfer learning techniques helps to enhance the ability to learn from diverse data distributions in IoMT (Internet of Medical Things) networks, improve the robustness of the learning process. This approach presents a practical and effective solution for IoMT network learning, allowing each participant to contribute their individual features to collectively extract valuable insights from the data. The decentralization facilitates end-users in accessing their personal medical records while ensuring privacy, irrespective of their location and time. This system also achieves a minimal delay sensitivity of 3.2%, by providing timely access to the required information. [ABSTRACT FROM AUTHOR]

Intravenous (IV) infusion is a pervasive medical intervention, administered to approximately 90% of hospitalized patients. Phlebitis, characterized by inflammation of the veins resulting from infusion, stands as a prevalent complication, ranking fourth among hospitalacquired infections globally. This research investigates the efficacy of a Smartphone Early Warning System (EWS) display in mitigating the incidence of phlebitis within the Safa treatment room at Aisyiyah Hospital. Employing a pre-experimental research design with a Static-group Comparison approach, 16 respondents were allocated to treatment and control groups. The Mann-Whitney Test, a statistical analysis, unveiled a significant difference (P Value = 0.001 < 0.05) in phlebitis incidence between the treatment group, utilizing the Smartphone EWS display, and the control group, which relied on conventional monitoring methods. Notably, the average rank of phlebitis incidence in the control group (21.12) exceeded that in the treatment group (9.78). This study sheds light on the potential of the Smartphone EWS display to curtail phlebitis during infusion, emphasizing its role in advancing nursing care quality through real-time monitoring and early prevention strategies. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has significantly enhanced the healthcare system by enabling advanced patient monitoring, data analytics, and remote interactions. Given that IoMT devices generate vast amounts of sensitive data, robust privacy mechanisms are essential. This privacy requirement is critical for IoMT as, generally, these devices are very resource-constrained with limited storage, computation, and communication capabilities. Blockchain technology, with its decentralisation, transparency, and immutability, offers a promising solution for improving IoMT data security and privacy. However, the recent emergence of quantum computing necessitates developing measures to maintain the security and integrity of these data against emerging quantum threats. This work addresses the current gap of a comprehensive review and analysis of the research efforts to secure IoMT data using blockchain in the quantum era. We discuss the importance of blockchain for IoMT privacy and analyse the impact of quantum computing on blockchain to justify the need for these works. We also provide a comprehensive review of the existing literature on quantum-resistant techniques for effective blockchain solutions in IoMT applications. From our detailed review, we present challenges and future opportunities for blockchain technology in this domain. [ABSTRACT FROM AUTHOR]

One of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the PIMA Indian Diabetes dataset. Patients with diabetes were classified on the basis of laboratory findings. Pre-processing tasks, such as the handling of imbalanced data and missing values, were performed prior to feature importance and normalisation activities. The algorithm with the best performance was examined using precision, accuracy, F1, recall and area under the curve metrics. Then, all ML models were hyper parametrically tuned via grid search to optimise their performance and reduce their error times. The decision process was also evaluated to further enhance the models. The AdaBoost-ET model performed even when features were not selected for binary classification. The model proposed in this study can predict diabetes with unprecedented high accuracy compared with the models in previous studies. [ABSTRACT FROM AUTHOR]

The study shows that many computer programs using AI have been made to look at the ECG signal and find heart problems. We need to find and treat cardiac diseases early these days if we don't want them to happen. With the help of new technology in health informatics that gathers, sorts, and finds data, there may be new ways to avoid CVDs. Using AI-based methods, it is possible to correctly sort ECG data to find tachycardia. There are several steps in the process of classifying. It is easier for a convolutional neural network to find rhythms. But the health care system still needs to use smart tech to always check on people's heart health. The experts have found some issues in this area. You need better tools to do a great job of analysing ECG data. Sometimes these computer methods don't work right, but they are becoming very helpful for making medical progress. In the field of heart electrophysiology (EP), simple AI have been used for many years. Deep learning techniques are becoming more common once more. This has led to new discoveries in electrocardiography research, like being able to tell when someone is sick by looking at their signature. AI is getting better, and computers, devices, and websites are getting better quickly. This has led to the fast growth of AI-enhanced apps and big data studies. New ways of living, the rise of the internet of things, and better phone systems have made it easier to find people in a community who have atrial fibrillation than it was before. AI has made it possible to get better 3D images of the heart, which led to the idea of virtual hearts and models of heart beats. [ABSTRACT FROM AUTHOR]

The widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis. [ABSTRACT FROM AUTHOR]

The research work proposed for the X-band microstrip line-based magnetic resonance imaging (MRI) coils has been accomplished with coplanar waveguide feeding and ha highlighted the design parameters to be employed in the internet of medical things (IoMT) features. The proposed research has focused on the wireless body area networks (WBAN) phenomenon in simulated human organs. It has been employed to study the electro-magnetic (EM) parameters of the simulated human organ and the functioning of wearable MRI coils on the human body. Therefore, these coils have been configured in triangle-shaped hierarchical structures, and each layer has been printed on both sides of the conductive strips. These proposed coils utilize a Bakelite substrate with a 1.6-mm thickness and an equivalent dielectric strength of 1.2. It has 69.9 × 85.2 × 1.6 mm3 dimensions and was fabricated using microwave integrated circuits (MIC). These coils have been generated at 8 GHz and this spectrum has been justified with the microwave X band (8–12 GHz) using the standard measured results. Hence, these coils have demonstrated 45.81-dB signal attenuation with a 1-dB standing wave ratio (SWR). Therefore, this research has extended to the different kinds of virtual simulation scenarios in diagnosis applications. Additionally, the research delves into the electromagnetic characteristics, encompassing electric and magnetic fields, the specific absorption ratio (SAR), and temperature. These characteristics are thoroughly analyzed using MRI phantom models within virtual environments. As a result of this comprehensive analysis, the suitability and efficacy of these MRI coils have met rigorous standards. These coils are highly demanded by complicated systems functioning in these bands for IoMT and MRI diagnosis applications. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and implementation. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model's superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems.

The Internet of Medical Things (IoMT) is revolutionizing healthcare by providing remarkable possibilities for remote patient monitoring, instantaneous data analysis, and customized healthcare delivery. However, the widespread use of interconnected medical devices has exposed vulnerabilities to cyber threats, posing significant challenges to the security, privacy, and accessibility of healthcare data and services. The CICIoMT2024 dataset is a crucial resource in IoMT security, offering a wide range of cyber-attacks targeting IoMT devices. This paper uses data balancing techniques like SMOTE and advanced machine learning (ML) models to analyze cyber threats on IoMT devices, aiming to improve healthcare system safety by identifying and mitigating cyberattacks. By conducting extensive experiments, the paper has determined the most effective ML models for three different levels of classification of the dataset: binary, multiclass, and multitype. Employing ML techniques like AdaBoost, Random Forest, kNN, and XGBoost proves to be extremely powerful in accurately categorizing various types of attacks. This study emphasizes the importance of proactive cybersecurity measures in IoMT ecosystems, as well as the effectiveness of ML techniques in protecting healthcare systems from evolving cyber threats.

Abstract Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

Katerina Svandova,&ast; Zdenek Smutny&ast; Faculty of Informatics and Statistics, Prague University of Economics and Business, Prague, Czech Republic&ast;These authors contributed equally to this workCorrespondence: Zdenek Smutny, Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague 3, Prague, Czech Republic, Email zdenek.smutny@vse.czBackground: The massive expansion of the Internet of medical things (IoMT) technology brings many opportunities for improving healthcare. At the same time, their use increases security risks, brings security and privacy concerns, and threatens the functioning of healthcare facilities or healthcare provision.Purpose: This scoping review aims to identify progress in designing risk assessment and management frameworks for IoMT security. The frameworks found are divided into two groups according to whether frameworks address the technological design of risk management or assess technological measures to ensure the security of the IoMT environment. Furthermore, the article intends to find out whether frameworks also include an assessment of organisational measures related to IoMT security.Methods: This review was prepared using PRISMA ScR guidelines. Relevant studies were searched in the citation databases Web of Science and Scopus. The search was limited to articles published in English between 2018 and 17 September 2023. The initial search yielded 1341 articles, of which 44 (3.3%) were included in the scoping review. A qualitative content analysis focused on selected security perspectives and progress in the given area was carried out.Results: Thirty-two articles describe the design of risk assessment and management frameworks. Twelve articles describe the design of frameworks for assessing the security of IoMT devices and possibly offer a comparison of different IoMT alternatives. A description of the included articles was prepared from the selected security perspectives.Conclusion: The review shows the need to create comprehensive or holistic frameworks for operational security and privacy risk management at all layers of the IoMT architecture. It includes the design of specific technological solutions and frameworks for continuously assessing the overall level of information security and privacy of the IoMT environment. Unfortunately, none of the found frameworks offer an assessment of organizational measures even though the importance of the organization measures was highlighted in articles. Another area of interest for researchers could be the design of a general risk management database for IoMT, which would include potential IoMT-related risks connected to a particular device.Keywords: cybersecurity, healthcare, information systems, IoMT, internet of things, IoT, threat, sensors

Abstract The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management.

Internet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can be enhanced by using IoMT for remote patient monitoring and data-driven diagnosis powered by machine learning algorithms. In the context of IoMT, federated learning (FL) is an excellent choice to manage machine learning (ML) algorithms to drive this analysis. This is because FL models can be trained in a distributed manner on local heterogeneous datasets that all contribute to the "collective wisdom". The model parameters can be regulated and shared without sharing the actual health data, ensuring confidentiality and security. This paper makes a case for the viability of FL-based analysis of data acquired via IoMT by presenting some use cases and recent work in this area and proposing a novel framework for data analysis using FL specifically in the context of mental stress detection. It shows that FL-based methods can significantly reduce the required communication overhead for each local device from 10.02MB/day up to only 754B/day as compared to non-FL techniques. [ABSTRACT FROM AUTHOR]

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison. [ABSTRACT FROM AUTHOR]

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption. [ABSTRACT FROM AUTHOR]

The management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management. [ABSTRACT FROM AUTHOR]

The medical cyber-physical system utilizes various Internet of Medical Things (IoMT) devices that are connected to the network for real-time management and medication of patient. The resource constraint IoMT devices require energy-efficient lightweight security schemes to protect medical data. To the best of our knowledge, majority of the existing studies concentrate either on security or on energy efficiency issues. The main contribution of this research is to develop an energy-efficient lightweight authentication method without sacrificing the security level. The authentication is done using multiple factors, namely password, physical unclonable function (PUF), Chebyshev polynomial, smartcard and fuzzy extractor. Chebyshev polynomial is used for non-identification of the private key by any attacker, whereas PUF protects the smart card from cloning by generating unique challenge–response pairs. Apart from authentication, the proposed scheme includes Blockchain-enabled distributed trustable ecosystem among independent participants where miner is selected using lightweight fuzzy system. The proposed scheme carries out the formal security analysis using real or random model which is perseverance against different external attacks and the security verification has been done using AVISPA (Automate Validation of Internet Security Protocols and Applications) tool. As a major finding, the simulation result using NS-3 simulator confirms that the proposed study outperforms the existing studies in terms of packet loss rate, throughput, end-to-end delay, computational cost and communication cost. [ABSTRACT FROM AUTHOR]

The emergence of remote health monitoring and increased at-home care emphasizes the importance of patient adherence outside the clinical setting. This is particularly pertinent in the treatment of Attention Deficit Hyperactivity Disorder (ADHD) in pediatric patients, as the population inherently has difficulty remembering and initiating treatment tasks. Neurostimulation is an emerging treatment modality for pediatric ADHD and requires strict adherence to a treatment regimen to be followed in an at-home setting. Thus, to achieve the desired therapeutic effect, careful attention must be paid to design features that can passively promote and effectively monitor therapeutic adherence. This work describes instrumentation designed to support a clinical trial protocol that tests whether choice of color, or color itself, can statistically significantly increase adherence rates in pediatric ADHD patients in an extraclinical environment. This is made possible through the development and application of an internet-of-things approach in a remote adherence monitoring technology that can be implemented in forthcoming neurostimulation devices for pediatric patient use. This instrumentation requires minimal input from the user, is durable and resistant to physical damage, and provides accurate adherence data to parents and physicians, increasing assurance that neurostimulation devices are effective for at-home care. [ABSTRACT FROM AUTHOR]

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem. [ABSTRACT FROM AUTHOR]

The availability of drugs across the country is a direct measure for fairer public health. Several issues have been reported drastically related to various organizations that fail to provide quality medicines on time. There has been a consistent increase in cases where the treatment, as well as exempted drugs, were supplied due to the unavailability of proper traceability of the supply chain. Several parties are involved in the supply and have similar interests that may defer the adequate shareability of the drugs. The existing system for managing the drug supply chain suffers from several backlogs. The loss of information, unavailability of resources to track the proper medicinal storage, transparency of information sharing between various stakeholders and sequential access. The applicability of the decentralized model emerging from the blockchain can apply to one of the perfect solutions in this case. The drug traceability chain can be deployed to a Ledger-based blockchain that may result in decentralized information. Continuous supply from the Internet of Things (IoT) based devices might be handy as the middleware for providing a trustworthy, safe, and proper transaction-oriented system. The data integrity, along with the provenance resulting from the IoT-connected devices, is an effective solution towards managing the supply chain and drug traceability. This study presents a model that can provide a token-based blockchain that will help provide a cost-efficient and secure system for a reliable drug supply chain. [ABSTRACT FROM AUTHOR]

With superior computing power and efficient data collection capability, Internet of Medical Things (IoMT) significantly improves the accuracy and convenience of medical work. As most communications are over open networks, it is critical to encrypt data to ensure confidentiality before uploading them to cloud storage servers (CSSs). Public key encryption with keyword search (PEKS) allows users to search for specific keywords in ciphertext and plays an essential role in IoMT. However, PEKS still has the following problems: 1. As a semi-trusted third party, the CSSs may provide wrong search results to save computing and bandwidth resources. 2. Single-keyword searches often produce many irrelevant results, which is undoubtedly a waste of computing and bandwidth resources. 3. Most PEKS schemes rely on bilinear pairings, resulting in computational inefficiencies. 4. Public key infrastructure (PKI)-based or identity-based PEKS schemes face the problem of certificate management or key escrow. 5. Most PEKS schemes are vulnerable to offline keyword guessing attacks, online keyword guessing attacks, and insider keyword guessing attacks. We present a certificateless verifiable and pairing-free conjunctive public keyword searchable encryption (CLVPFC-PEKS) scheme. An efficiency analysis shows that the performance advantage of the new scheme is far superior to that of the existing scheme. More importantly, we provide proof of security under the standard model (SM) to ensure the reliability of the scheme in practical applications. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is still a relatively new field of research, and its potential to be used in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasises IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualisation, and artificial intelligence in medical applications. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices has been carried out. After the year 2001, an integrated outcome of 986 articles was initially selected, and by applying the inclusion–exclusion criterion, a total of 597 articles were identified. 23 new studies have been finally found, including records from websites and citations. This review then analyses different sensor monitoring circuits in detail, considering an Intensive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented. [ABSTRACT FROM AUTHOR]

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure. [ABSTRACT FROM AUTHOR]

Lactate, once merely regarded as an indicator of tissue hypoxia and muscular fatigue, has now gained prominence as a pivotal biomarker across various medical disciplines. Recent research has unveiled its critical role as a high-value prognostic marker in critical care medicine. The current practice of lactate detection involves periodic blood sampling. This approach is invasive and confined to measurements at six-hour intervals, leading to resource expenditure, time consumption, and patient discomfort. This review addresses non-invasive sensors that enable continuous monitoring of lactate in critical care patients. After the introduction, it discusses the iontophoresis system, followed by a description of the structural materials that are universally employed to create an interface between the integumentary system and the sensor. Subsequently, each method is detailed according to its physical principle, outlining its advantages, limitations, and pertinent aspects. The study concludes with a discussion and conclusions, aiming at the design of an intelligent sensor (Internet of Medical Things or IoMT) to facilitate continuous lactate monitoring and enhance the clinical decision-making support system in critical care medicine. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is an evolving field in healthcare that connects medical devices to the Internet to enable efficient data sharing and health information collection. The IoMT aims to improve the quality of healthcare, facilitate diagnosis and treatment, and enhance patient safety. Nonetheless, the IoMT networks are usually exposed to multiple security attacks. Also, recent studies indicate that security protocols contain flaws in protecting patient data. Thus, data must be protected by innovative security protocols. In our work, we propose a Medical Security Protocol (MedSecP) to support security in IoMT. The proposed protocol adopts the Twofish encryption, Naive Bayes (NB), and decision tree (DT) within the private blockchain (PBC) Fog Computing (FC) to build robust security procedures. The Twofish encryption algorithm is used to provide medical information concealment. In our proposed protocol, the type of data is first determined, and accurate and appropriate medical decisions are made based on the collected data using a decision tree algorithm, and then rapid classification of the patient data is done using the Naive Bayes algorithm. Confidential medical data is then encrypted using the Twofish algorithm to ensure the confidentiality of this data and prevent unauthorized access. Finally, this encrypted medical data is stored using blockchain technology. Twofish, NB, and DT are organized to work harmoniously with the PBC. The latter manages and distributes data peer-to-peer in IoMT. We leverage Fog Computing to speed up decision-making without resorting to the remote cloud. We analyzed our protocol in terms of security and performance. Our results indicate that MedSecP provides reliable security against attacks as the protocol demonstrated an average security attack response rate of 97.20%, demonstrating its resistance to external threats by keeping the encrypted medical data, classified and achieving appropriate medical decisions. In terms of performance, MedSecP has demonstrated an average security response time of around 50ms, providing fast and efficient performance. In MedSecP, the highest value for encryption is 0.000015 ms, and decryption is 0.000017 ms when applying the Twofish algorithm which is considered extremely suitable for implementing health systems operations compared to existing encryption algorithms. Consequently, MedSecP provides lightweight operations in support of complex security measures that qualify it to support healthcare institutions.

The Internet of Medical Things (IoMT) has transformed healthcare by connecting medical devices, sensors, and patients, significantly improving patient care. However, the sensitive data exchanged through IoMT is vulnerable to security attacks, raising serious privacy concerns. Traditional key sharing mechanisms are susceptible to compromise, posing risks to data integrity. This paper proposes a Timestamp-based Secret Key Generation (T-SKG) scheme for resource-constrained devices, generating a secret key at the patient's device and regenerating it at the doctor's device, thus eliminating direct key sharing and minimizing key compromise risks. Simulation results using MATLAB and Java demonstrate the T-SKG scheme's resilience against guessing, birthday, and brute force attacks. Specifically, there is only a 9 % chance of key compromise in a guessing attack if the attacker knows the key sequence pattern, while the scheme remains secure against brute force and birthday attacks within a specified timeframe. The T-SKG scheme is integrated into a healthcare framework to securely transmit health vitals collected using the MySignals sensor kit. For confidentiality, the Data Encryption Standard (DES) with various Cipher Block modes (ECB, CBC, CTR) is employed.

Major depressive disorder (MDD) is a mental ailment marked by a loss of interest in activities, persistent depression, and hopelessness. MDD has been on the rise in society in recent decades for varied reasons and has spurred suicidal tendencies among individuals. Early detection, continuous monitoring, and effective treatment are crucial for its impact on quality of life and society. EEG signal models the brain’s electrical activities and has emerged as a potential tool to assess the depression status of a person. Due to advancements in sensor technology, fast, convenient, and cost-effective EEG acquisition is now possible, resulting in many EEG-based healthcare monitoring applications in recent years. This work proposes an EEG-headset-based smart monitoring system for real-time diagnosis of MDD in the Internet of Medical Things (IoMT) framework. In this study, we proposed a novel Linear Graph Convolution Network-Transformer-based deep learning approach for categorizing MDD through a time-frequency analysis of EEG signals. The Stockwell transform (S-transform) is employed to exploit the spectro-temporal information from the EEG and the resulting 2D representation is then fed into customized Linear Graph Convolution Network for MDD detection. We have utilized the Weighted Focal Binary Hinge Loss function, specifically designed for customized Linear Graph Convolution Network, to improve learning and handle unbalanced input. Subsequently, a novel Transformer model is designed to refine the MDD classification further. The proposed methodology named SLiTRANet, blends spectral analysis with the S-transform, graph-based learning with Linear Graph Convolution Network, and the sequence modeling capability of the Transformer. The proposed SLiTRANet model can be further integrated within an IoMT framework for automated real-time MDD diagnosis using EEG signals. The proposed methodology is evaluated on two publicly available datasets, MODMA and HUSM datasets. The evaluation results demonstrate the superior performance of the proposed SLiTRANet framework against the existing pre-trained and hybrid deep learning models, achieving remarkable accuracy, sensitivity, specificity, and precision rates of 99.92%, 99.90%, 99.95%, and 99.97%, respectively on HUSM dataset followed by an equally good performance on MODMA dataset with an accuracy of 99.68%. The proposed comprehensive approach implemented on two varied datasets highlights significant advancements in depression detection by outperforming state-of-art approaches.

Smart Healthcare has brought advantages to both healthcare professionals and individuals living in remote areas. However, deliberately or inadvertently altering medical records can result in an inaccurate diagnosis. As a result of telemedicine, patients can easily communicate with their healthcare providers, and the Internet of Medical Things (IoMT) devices enable remote consultations, allowing for timely diagnosis and treatment. Medical data can be protected by watermarking, a combination of steganography, and fingerprinting. This paper proposes a hash-based approach using the Discrete Cosine Integer Wavelet Transform (DCIWT) to provide secure watermarking for confidential medical data. To preserve critical Region of Interest (RoI) integrity, we use SHA-256 cryptographic hash algorithm. The hashed Region of Interest (hRoI) is embedded in the Region of Non-Interest (RoNI) by DCIWT. To enhance the segmentation process, we employ adaptive thresholding, which dynamically adjusts the threshold based on local image characteristics, ensuring accurate segmentation of RoI and RoNI across varying image qualities. The hidden data is significantly affected if the watermarked image is altered. As a result of comparing the original RoI with the extracted RoI, we determine if an image has been altered. Tampered regions are be recovered by extracting the RoI and using it for diagnosis. The proposed digital watermarking technique maintains RoI integrity using message digests and histograms. Through extensive experiment results, we demonstrate that the perceptual properties of the images are intact in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

Tele-rehabilitation has garnered significant interest among clinicians and researchers with its potential to transform cardiac rehabilitation, affecting millions of patients annually. A critical requirement of tele-cardiac rehabilitation is a fail-safe, highly interactive system with timely feedback to both the patient and the therapists or physicians. Current systems often assume ideal network conditions, neglecting the nuances of real-world deployment. We have designed, developed, and tested an end-to-end tele-cardiac rehabilitation system that seamlessly combines Internet of Medical Things (IoMT) devices and AI-powered abnormality and activity detection, providing a fail-safe and real-time actionable closed-feedback loop system for the patient and the doctor. A pilot study evaluates system performance across diverse mobile networks in varying conditions (stable or unstable). The RESNET-18 model for cardiac abnormality detection (0.71 F1-score) and the VGG-16 model for human activity classification (0.89 F1-score) demonstrate significant performance. Furthermore, we optimize these models for edge devices, demonstrating significant speed improvements compared to cloud servers (up to 33 times faster).

The rise of smart health technologies has offered benefits such as remote patient monitoring, personalized therapy, and early disease detection. However this advancement has also raised concerns about security and privacy of patient data. Authentication is essential for protecting patient data and smart health devices. The existing scheme is identified with security vulnerabilities in terms of stolen verifier, impersonation, and man in middle attacks. Motivated by this, authors have presented a secure, lightweight authentication technique for smart health environments for a remote monitoring scenario employing a Multiple Factor approach and a lightweight algorithm. The scheme is verified through formal and informal analysis. The scheme demonstrated enhanced authentication performance and security with processing overhead of (0.04 milliseconds) and transmission overhead of (704 bits), in comparison to the baseline scheme. This system has the potential to improve data security and privacy in a variety of smart health scenarios.

Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework that adapts multiple pulsatile signal modalities and generates machine-learning-ready datasets agnostic to downstream medical tasks. Results: a dataset preprocessed by Pulse2AI improved systolic blood pressure estimation by 29.58%, from 11.41 to 8.03 mmHg in root-mean-square-error (RMSE) and its diastolic counterpart by 26.01%, from 7.93 to 5.87 mmHg in RMSE. For respiration rate (RR) estimation, Pulse2AI boosted performance by 19.69%, from 1.47 to 1.18 breaths per minute (BrPM) in mean-absolute-error (MAE). Conclusion: Pulse2AI turns pulsatile signals into machine learning (ML) ready datasets for arbitrary remote health monitoring tasks. We tested Pulse2AI on multiple pulsatile modalities and demonstrated its efficacy in two medical applications. This work bridges valuable assets in remote sensing and internet of medical things to ML-ready datasets for medical modeling.

The Internet of Things (IoT) has shown remarkable competence in uniting devices and internet connectivity to make modern life’s applications a reality. These networked devices, empowered with computing and communications, usually exhibit an exponential increment in network traffic to enhance the end-user quality of experience (QoE). The exponential increase in network traffic makes the traditional communication system congested. To reduce the burden of network traffic, the data could be offloaded to device-to-device (D2D)-based infrastructure. This offloading scheme results in an innovative, eco-friendly, and sustainable data dissemination approach known as social networking and is mostly done in opportunistic ways. In this paper, we propose a scheme to explore the performance of the existing routing protocols with the movements and spatial closeness relations among ‘things’, termed as Internet of Moving Things (IoMT). The spatial closeness known as traces will help to make optimal data offloading decisions while considering the mobility traces of humans in the social network. The proposed mechanism is designed to optimally utilize moving physical objects to be involved as a third party in the data communication process between the sources and the destinations rather than using the traditional infrastructure while considering the quality of service (QoS). The simulation results have corroborated the proposed scheme and showed the remarkable performance of routing algorithms while incorporating mobility traces.

Anomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring. In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. FusionNet’s architecture leverages the diversity of these algorithms to achieve high accuracy and precision. We evaluate FusionNet’s performance on two distinct datasets, Dataset 1 and Dataset 2, and compare it with traditional machine learning models, including SVM, KNN, and RF. The results demonstrate that FusionNet consistently outperforms these models across both datasets in terms of accuracy, precision, recall, and F1 score. On Dataset 1, FusionNet achieves an accuracy of 98.5% and on Dataset 2, it attains an accuracy of 99.5%. FusionNet’s remarkable ability to detect anomalies with exceptional accuracy underscores its potential for real-world applications. This study highlights the significance of FusionNet as a robust model for anomaly detection and provides insights into its superior performance over traditional models. The results emphasize the promising prospects of FusionNet in security, healthcare, and other domains where accurate anomaly detection is crucial.

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.