Search Results (295)

Smart Rings (SRs) are user-friendly devices capable of measuring various health parameters, making them suitable for remote continuous monitoring in diverse clinical settings. Since the available evidence on the accuracy of SRs recording health data is highly heterogeneous, this systematic review, conducted in accordance with PRISMA guidelines, searched for articles evaluating the efficacy of SRs for sleep, respiratory, and cardiovascular monitoring across the PubMed, SCOPUS, and ProQuest databases. Meta-analyses were conducted for health outcomes evaluated in at least three studies with a comparable study population and design, and the same comparison device. Nineteen articles were included: eleven analyses focused on sleep quality, eight on cardiovascular parameters, and one on oxygen saturation. Studies analysing cardiovascular outcomes found a good accuracy of SRs in measuring heart rate (HR) with a mean bias of −0.4 bpms (limits of agreement (LoAs): −2.7; 1.8). The meta-analyses showed variability in SRs’ efficacy in monitoring total sleep time (mean bias: −21.3 min, LoAs: −69.9, 27.4) and REM duration (mean bias: −18.2 min, LoAs: −33.3, −3.1). The results highlighted the promising potential of SRs for HR monitoring. Further research is needed to clarify the reliability of SRs in monitoring sleep quality and their use directed to a broader range of health parameters. With further development, SRs could become valuable tools for healthcare professionals. Full article

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. Full article

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. Full article

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. Full article

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master–slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system’s effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data. Full article

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%. Full article

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. Full article

In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%. Full article

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases. Full article

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. Full article