Electrohysterography (EHG) represents a promising computational approach for non-invasive monitoring of uterine activity during pregnancy and labor. This review summarizes the advancements in signal processing techniques and machine learning algorithms that have been applied to enhance the utility of EHG. Key topics include the extraction and analysis of uterine electrical signals, classification of contractions, and prediction of obstetric outcomes such as preterm and labor/non-labor states. The review emphasizes computational methodologies for signal processing and extraction, including empirical mode decomposition or wavelet transform, and for data classification, such as neural networks or support vector machine, highlighting their performance and limitations. Despite significant progress, challenges persist, such as the lack of standardized protocols, limited datasets, and inconsistent evaluation and annotation metrics, which hinder broader clinical adoption. The integration of additional clinical markers, simultaneous monitoring of maternal and fetal health, and the development of wearable systems for telemedicine present exciting opportunities for future research.

This paper presents a novel software-defined radio (SDR) platform designed to measure the bit error rate (BER) in vehicle-to-vehicle visible light communication (V2V VLC) technology under real-world conditions. Unlike previous studies that have primarily relied on simulations or static tests, this research provides empirical evidence from both stationary and dynamic vehicular scenarios. The proposed system functions as an orthogonal frequency division multiplexing (OFDM) transceiver, employing multistate quadrature amplitude modulation (M-QAM) to modulate individual subcarriers. Extensive experiments were conducted to evaluate the system’s performance, including the effects of varying headlight beam angles and vehicle distances on BER. Notably, stable communication was achieved using 64-QAM modulation at distances up to 10 meters, maintaining a BER below the critical threshold of$\mathbf {10^{-5}}$. These findings demonstrate the feasibility and robustness of integrating VLC with standard vehicle headlight systems, offering a viable solution for enhancing vehicular communication in dynamic traffic environments.

• Hematopoiesis: This process occurs in the bone marrow, where hematopoietic stem cells (HSCs) divide and differentiate to maintain the balance between blood cell lines. Disorders in this process can cause blood diseases such as anemia, leukocytosis, or thrombocytopenia. • Identification of malignant white blood cells in leukemia: Accurate identification of malignant white blood cells through images is important in diagnosing and treating leukemia. This helps doctors make accurate diagnoses and apply appropriate treatments. • New method using Graph Neural Networks (GNN): The author proposes a method for identifying and classifying white blood cells based on images using the Multi-hop Attention GNN method ( Fig. 1 ), along with the following supporting methods: • YOLO-v10: Used for object detection and image preprocessing through the Centre Net network architecture. • Salp Swam Optimization (SSO): Used to select optimal features of white blood cell images, helping to put these features into each node in the GNN architecture for classification. • Dataset and model performance: The dataset includes 16,027 white blood cell images with a resolution of about 42 pixels per 1 μm, annotated and classified into 9 different types of white blood cells. • The YLSSOGNN model achieves a classification accuracy of 99.18 %, while the YLGNN achieves 99.03 %. • Application of the model: The model can be applied to other image objects thanks to the object recognition function of YOLO-v10 and the classification ability of the graph neural network with SSO optimization technique and the Multi-hop Attention GNN model. The process of creating blood cells (hematopoiesis) occurs in the bone marrow, where Hematopoietic Stem Cells (HSCs) are located. The division and differentiation of hematopoietic stem cells are tightly regulated to ensure a balance between blood cell lineages. Disturbances in the process can lead to blood diseases such as anemia, high White Blood Cell (WBC) count, or thrombocytopenia. Detecting malignant leukemia cells based on images is crucial in diagnosing and treating leukemia, helping doctors make accurate diagnoses, and providing appropriate treatment. The author proposes a new method for recognizing and classifying WBC images using the Multi-hop Attention Graph Neural Networks method. The YOLO-v10 method is used for object detection and image preprocessing through the Centre Net network architecture. The Salp Swarm Optimization (SSO) method is deployed to select the features of the WBC images optimally and put the image features into each node in the architecture of the Graph Neural Network (GNN) model to perform classification. The dataset used has an image quality of approximately 42 pixels per 1 μm resolution with a total of 16,027 annotated White Blood Cell images classified into 9 types of WBC with characteristic images of clinically significant pathologies. The classification accuracy of the system of the YLSSOGNN model is 99.18 %, and the classification accuracy of the system of the YLGNN model is 99.03 %. The WBC image recognition and classification model using the post-learning method has a GNN architecture with object recognition function using the YOLO-v10 method and feature extraction and optimization using the SSO method and performs WBC image classification using Multi-hop Attention Graph Neural Networks model, which helps to bring high performance and can apply the model to other types of image objects.

In this era, digital technologies such as artificial intelligence, the Internet of Things (IoT) and blockchain are gaining popularity in research and academia. The supply chain management application is the key to achieving many benefits from AIoT and blockchain technology. However, these technologies have many issues, such as sustainability, a green environment, and multiconstraints (e.g., time, energy, cost, and CO2) for supply chain management applications. This article presents sustainable, secure blockchain-assisted AIoT and green multiconstraint supply chain systems. Initially, we present a secure and sustainable methodology that securely validates the supply chain management system data. For the green environment, we consider the problem a combinatorial problem consisting of different constraints such as time, energy, cost, and carbon dioxide (CO2). To solve this problem for supply chain management jobs, we present a multiconstraint genetic algorithm deep convolutional neural network (MCGA-DCNN) algorithm methodology. The objective is to reduce total processing time, total processing energy consumption, cost, and the CO2 environment as a green environment for supply chain management jobs. The genetic algorithm is evolutionary, where the fitness function optimizes the multiconstraint weights at the runtime based on DCNN and provides the optimal solutions for jobs. Simulation results show that MCGA-DCNN minimized the time, energy, cost, and CO2 and securely validated all transactions for all supply chain management jobs compared to existing schemes.

Increases in population and prosperity are linked to a worldwide rise in garbage. The “classification” and “recycling” of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model.

Fetal examinations are a significant and challenging field of healthcare. Cardiotocography is the most commonly used method for monitoring fetal heart rate and uterine contractions. As a promising alternative to cardiotocography, fetal phonocardiography is beginning to emerge. It is an entirely non-invasive, passive, and low-cost method. However, it is tough to estimate the ideal form of the fetal sound signal in most cases due to the presence of disturbances. The disturbances originate from movements or rotations of the fetal body, making fetal heart sound processing difficult. This study presents an automatic method for segmenting the fetal heart sounds in a phonocardiographic signal that is loaded with different types of disturbances and analyzes which of these disturbances most affect segmentation accuracy. To provide a comprehensive investigation, we propose a hybrid classifier based on Transformer and eXtreme Gradient Boosting, short for XGBoost, to improve segmentation performance by decision-making integration. 2000 segments of data from the Research Resource for Complex Physiologic Signals, PhysioNet repository, and created synthetic data (873 recordings) were used for the experiment. In the S1 label, our proposed method ranks first among all compared algorithms in precision, recall, F1, and accuracy score, tying with Transformer in recall score. It achieves an accuracy increase of 5% and 1.3% compared to XGBoost and Transformer, respectively. Similarly, in the S2 label, there is a precision score increase of 5.8% and 3.7% compared to XGBoost and Transformer, respectively. In general, our proposed method shows effective and promising performance..

Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users’ healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel “Pattern-Proof Malware Validation” (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%.

The selection of antivirus masks is an important problem in the context of the ongoing COVID-19 pandemic. Multiple attribute decision-making (MADM) algorithmic approaches can be used to evaluate and compare different masks based on multiple criteria, such as effectiveness, comfort, and cost. An aggregation of interval-valued multi-fuzzy hypersoft sets provides a flexible framework for handling uncertainty and imprecision in the MADM process. This approach allows for the integration of multiple sources of information such as expert opinions and empirical data, and considers the different levels of uncertainty and ambiguity associated with each criterion. By using the matrix-manipulated aggregation of interval-valued multi-fuzzy hypersoft sets like the induced fuzzy matrix, α-level matrix, threshold matrix, and mid-threshold matrix, an algorithm is proposed for the optimal selection of material for manufacturing antivirus masks. The robustness of the algorithm is maintained by following simple computation-based stages that enable a wide range of multidisciplinary readers to understand the idea vividly. By using this algorithm, it is possible to improve the accuracy and reliability of the decision-making process and to better balance the trade-offs between the different criteria, i.e., the computed results of the proposed algorithm and the structural aspects of the proposed approach are both compared with some relevant existing structures. Computation-based and structural comparisons are presented to assess the adaptability and reliability of the study. The first one is meant to check reliability, while the second is meant to check flexibility. In both cases, however, the presented approach yields the required standard. By comparing the prospective structure to the relevant developed model, the implications of the proposed framework are explored.

This paper presents the augmented Internet of Things (AIoT) framework for cooperatively distributed deep blockchain-assisted vehicle networks. AIoT framework splits the vehicle application into various tasks while executing them on different computing nodes. The vehicle application has different constraints, such as security, time, and accuracy, which are considered during processing them on parallel computing nodes (e.g., fog and cloud). We propose a partitioned AIoT scheme, dividing vehicular tasks into local and remote tasks. The objective is to minimize delays and efficiently execute urgent tasks such as vehicle, pedestrian, and traffic signals on local vehicles. The existing blockchain technologies suffer from many security issues, such as anonymous node issues and malware attacks in blockchain blocks. This is why we present the combined deep convolutional neural network (DCNN)-assisted proof-of-trust miner (PoTM) scheme. It safely handles tasks in different blocks. The smart contract is a human-written piece of code in blockchain technologies so that malicious code can be integrated into blockchain blocks during the registration of vehicles among nodes. The main limitation of smart contracts is that they are not changeable and cannot be changed once executed for any block. To avoid this situation, we present an augmented adaptive Trust Management Credibility Score Scheme (TMCSS) scheme that registers the vehicles before starting any services at blockchain miners. These registration certificates are changeable once DCNN detects any malicious activity in the vehicle data. Simulation results show that the proposed schemes improved delays by 35%, reduced the failure ratio of transactions by 39%, and enhanced overall transactions with the minimum failure compared to existing blockchain technologies for road-cooperation services in networks.

Alzheimer's disease (AD) is a memory-related disease that occurs in the human brain where neurons become degenerative. It is an evolved form of dementia that deteriorates over time. Machine learning, an extended version of deep learning, has appeared as an optimistic strategy for AD detection. Regardless, the existing AD detection approaches have yet to acquire the expected accuracy, mainly due to unreasonable data for training and testing. In this paper, we present the Federated Deep Convolutional Neural Network Alzheimer Detection Schemes (FDCNN-AS), specifically designed for varying age groups. FDCNN-AS is an efficient framework that contains architecture, algorithm flow, and implementation. It manages AD data from various laboratories and processes it in additional clinics. Our method mixes training data models from different types of data such as positron emission tomography, summed tomography, magnetic resonance imaging, blood tests, and questionnaires about synaptic degeneration. Further, we look at some restrictions that have yet to be addressed in AD detection. These include seeing AD at different ages, extrapolating the severity of brain damage, comparing treatment and recovery rates, and finding benign and malignant ranges in AD data that has been collected. To ensure secure and privacy-preserving learning, we execute FDCNN-AS within a federated learning environment that concerns considerable laboratories and clinics. Within this setup, we operate the generic deep convolutional neural network. The experimental results indicate that FDCNN-AS performs optimally, reaching a remarkable 99% accuracy in detecting dementia Alzheimer's in the human brain.

The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short-term memory (LSTM) schemes is the best way to meet the problem’s limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications.

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FL-BETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users' obtaining real-time healthcare services. Many patients and doctors' hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost.

This pilot comparative study evaluates the usability of the alternative approaches to magnetic resonance (MR) cardiac triggering based on ballistocardiography (BCG): fiber-optic sensor (O-BCG) and pneumatic sensor (P-BCG). The comparison includes both the objective and subjective assessment of the proposed sensors in comparison with a gold standard of ECG-based triggering. The objective evaluation included several image quality assessment (IQA) parameters, whereas the subjective analysis was performed by 10 experts rating the diagnostic quality (scale 1 - 3, 1 corresponding to the best image quality and 3 the worst one). Moreover, for each examination, we provided the examination time and comfort rating (scale 1 - 3). The study was performed on 10 healthy subjects. All data were acquired on a 3 T SIEMENS MAGNETOM Prisma. In image quality analysis, all approaches reached comparable results, with ECG slightly outperforming the BCG-based methods, especially according to the objective metrics. The subjective evaluation proved the best quality of ECG (average score of 1.68) and higher performance of P-BCG (1.97) than O-BCG (2.03). In terms of the comfort rating and total examination time, the ECG method achieved the worst results, i.e. the highest score and the longest examination time: 2.6 and 10:49 s, respectively. The BCG-based alternatives achieved comparable results (P-BCG 1.5 and 8:06 s; OBCG 1.9, 9:08 s). This study confirmed that the proposed BCG-based alternative approaches to MR cardiac triggering offer comparable quality of resulting images with the benefits of reduced examination time and increased patient comfort.

CardioTocoGraphy (CTG) is a fundamental procedure to support fetal diagnosis during pregnancy and labor, which involves registration and analysis of fetal heart rate and uterine contraction signals. As the fetal state assessment based on visual evaluation of the signal printouts is demanding, methods supporting the diagnostic decision are explored. To provide the qualitative assessment of the fetal condition, a fuzzy classifier can be applied. Its main advantages include the knowledge base (fuzzy rule base) linguistic interpretability. As fuzzy models involving fewer rules are easier to interpret, procedures of Rule Base Simplification (RBS) are the topic of research. This work describes the RBS method based on the fuzzy clustering with eps-hyperballs integrated with the evolutionary strategy framework. Its objective is to decrease a rule base size of zero order Takagi-Sugeno-Kang (TSK) fuzzy classifier while maintaining high CTG signals classification quality. The efficiency of the proposed RBS procedure was confirmed based on the evaluation of CTG signals from the benchmark CTU-UHB database.

Fetal phonocardiography (fPCG) is becoming a very useful tool for monitoring the foetus status. Measured signals in the abdominal area are often affected by many artifacts, so the Finite Impulse Response Filter (FIR filter) for noise suppression has been tested in this work. Testing is performed on 37 synthetic records and the evaluation of filter efficiency is performed on the basis of determining the signal-to noise-ratio (SNR) and using Bland-Atman statistics and the calculated value of the standard deviation (SD), sensitivity value (Se), Positive predictive value (PPV), accuracy (ACC) and F1, which is the overall probability of correct detection of beats. Testing is done for different FIR filter settings, and the results have shown that the FIR filter can be effectively used to reduce noise in the fPCG signal when the filter order is set to 1000.

This work deals with fetal phonocardiogram (fPCG) processing. Two classical de-noising methods: wavelet transform (WT) method and the finite impulse response filter (FIR filter) are used and compared. For testing, real recordings were used and the evaluation was based on subjective observation and listening to signal improvement after performing individual methods. The results showed that both methods contributed to improved fPCG extraction from noise abdominal phonocardiogram (aPCG). On the basis of the subjective evaluation, in the end, we identified WT as a more appropriate method for processing the fPCG signal.

This paper deals with the extraction of the fetal ECG signal (fECG) using Adaptive Linear Neuron (ADALINE). The fetal ECG signal contains very important information about the health of the fetus not only during labor but also during pregnancy. We introduce the design, implementation and optimization of the adaptive system for fECG signal extraction from the abdominal ECG signal (aECG) using the ADALINE method. The verification of the proposed adaptive system was carried out on both synthetic and real data from clinical practice.

Long-term monitoring of the fetal heart rate (fHR) is important to control the fetal well-being. One of the promising methods for fetal monitoring is fetal phonocardiography which is based on recording fetal heart sounds. This paper presents a compact low-cost device for multi lead fHR measurement. The device introduced allows monitoring of fHR non-invasively and with no side effects for the mother or the foetus. In this case, the device presented is used to measure HR of the adult to compare the performance of various stethoscope heads. In addition to this measurement, the LabVIEW application for signal processing and analysis was improved.

The real-time location systems (RTLS) take recently important part in many location-aware systems especially in indoor objects localization tasks. In the health-care environment, bed tracking, patient monitoring or critical equipment tracking are important applications of such systems. The paper deals with currently developed RTLS based on the Infra-Red (IR) light. We develop the system primarily for University Hospital of Ostrava however the general structure of the system a hardware components can be used in other systems. In the article we introduce one part of the localization system - the Anchor. This paper also presents an energy assessment and testing of the power consumption of anchor performance. The result section brings major conclusions and possible directions for future work.

eHealth is a term that clearly characterizes highly modern, electronic healthcare. The technological concepts and systems falling into the category of eHealth are based on the use of information and communication technologies which affect health, healthcare in general, and include a number of services and systems to support therapy preventive care, diagnostic systems and healthcare management. A specific part of the set of eHealth technologies is the telemedicine services which allow patients to communicate with their physician remotely. This includes capabilities of patient monitoring using the Internet access service or mobile communication systems. Regulation (EU) 2015/2120 of the European Parliament and of the Council laying down measures concerning open Internet access entered into effect on 25 November 2015. The purpose of this Regulation was to lay down common rules for equal and non-discriminatory treatment of traffic when providing Internet access services and to ensure the related rights of the end users. Access to open Internet (net neutrality) represents the foundation building block of eHealth services. Czech Telecommunication Office (CTU) has defined the method of monitoring of the occurrence of discrepancies as indicators of the fact that the performance of the Internet access service does not achieve the agreed-upon parameters or, as the case may be, unavailability of the service itself, in the form of a method based on the recommendation of IETF RFC 6349. Based on a general mathematical model of the access network and practical experience from the real traffic, it turns out that it is useful to add to the current method also measurement of qualitative data parameters pursuant to ITU-T Y.1564 standard where the results of such defined comprehensive method are capable of indicating insufficient capacity of the distribution network (backhaul network), or other anomalies in the access network which could cause occurrence of discrepancies or even unavailability of the Internet access service or the eHealth service. The article discusses the issue of the impact of qualitative parameters on the compliance with net neutrality rules.

This paper focuses on current trends in non-invasive monitoring of vital functions of the human body using SMART sensors. It describes a sensor pad made of a thin layer of polydimethylsiloxane (PDMS), which contains one symmetrically positioned fibre Bragg grating (FBG). The PDMS material is immune to electromagnetic interferences (EMI) and inert to human skin. By combining fibre-optic technology and PDMS, the SMART PAD sensor is formed which is used not only for classical hospital environments, but also for electromagnetically interfered environments, for example magnetic resonance imaging (MRI). The basic monitored parameters described are the respiratory rate (RR) and heart rate (HR) of the human body. The results were obtained in a laboratory environment in a group of 10 volunteers upon their written consent. The acquired results were compared by the Bland-Altman (B-A) method, which belongs to the group of statistical methods in biomedical research.

This paper focuses on current trends in the noninvasive biomedical monitoring of the human body using a fiber-optic interferometric sensor. An interferometric sensor encapsulated into a thin circular layer of material polyurethane (code PU430 - PH30). This used material is immune to electromagnetic interference (EMI). The sensor is primarily designed for use in electromagnetic interference environments (MRI). The monitored vital parameters of the human body are respiratory rate (RR) and heart rate (HR). The sensor was tested in a laboratory environment on the ten volunteers upon their written consent, when the subjects tested were monitored in the supine and sitting position of the body. Testing was performed towards the reference ECG and the piezoelectric respiratory belt. Based on the objective Bland-Altman analysis (for respiratory rate averaging 96.49 % and for heart rate averaging 95.64 %), the sensor may be acceptable to clinicians. (the sensor is primarily intended for monitoring not for the diagnosis).

This paper presents the design and implementation of a Heart Rate Monitor (HRM) based on photoplethysmography (PPG). This optical method is noninvasive and provides a convenient means of implementing a low-cost wearable HRM for eHealth applications. Our novel design consists of a light source that emits a modulated or unmodulated light signal through the finger tissue and measures the changes in the reflected light from the arterial blood. These reflections correspond well with the blood volume changes in synchrony with the heartbeat. Here we aim to provide a detailed description of how to design and implement an HRM device based on two approaches: 1) PPG using an unmodulated light source; 2) PPG using a modulated light source. Noise analysis in these devices enabled us to conceptualize and compare their performance and infer which one offers a better choice for optical heart rate monitoring. Based on the measurement results, the latter approach offers superior performance due to better noise cancellation and higher Signal-to-Noise Ratio (SNR) and is therefore robust against movement artefacts, power line noise, flicker noise of electronic components, as well as background environmental light interference.

To monitor and visualize the operational and technical functions in Smart Home Care (SHC), it is necessary to use a robust SW tool to provide user-friendly display of the measured quantities with the ability of well-arranged preparation of the acquired data for further processing and analysis. This article describes the use of the PI (Plant Information enterprise information system) System software tool for visualization and indirect monitoring of occupancy of SHC rooms from the measured operational and technical quantities. Within the further processing of the measured data, the method of predicting the CO2waveform using neural networks from the measured temperature indoor Ti(°C) and the relative humidity indoor rHi(%) with the subsequent use of trend signal detection based on wavelet transform is devised.

The Internet of Medical Things (IoMT) is a collection of medical devices and software applications used to interconnect local or remote healthcare systems. In this paper, we present a formal review of research related to the use of Semantic Web to solve IoMT interoperability problems. The use of IoMT promotes personalized health and wellness as well as higher standards of care using individualized data-driven treatments. It is estimated that there will be a significant increase in the utilization of IoMT platforms in the future. The large number of isolated platforms will pose an interoperability issue when a treatment requires the use of two or more IoMT platforms. One approach to address this problem is through the use of Semantic Web technologies that add semantics to IoMT using one-to-one ontology alignment and central ontology approaches.

Cardiovascular diseases are the leading cause of death around the world. It is estimated that 17.7 million people die each year from such health conditions. The World Health Organization promotes the development of specific strategies to reduce the mortality rate due to cardiovascular diseases. This paper proposes an ECG monitor and its implementation using three approaches: hardware, software, and cloud services. For the hardware approach, we used an e-Health platform for Arduino with three leads. However, the software- and cloud-based approaches were implemented using the NodeJS framework to create the network communication between a local server and a remote server hosted on the Amazon Web Services infrastructure. Additionally, a responsive web application was developed to display the ECG signal, its R-R intervals, and the heart rate. Finally, an assessment was conducted to measure the quality of the ECG signal using Dynamic Time Warping.