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The field of Brain-Computer Interface (BCI) has been rapidly expanding in the last few years and it is applicable in several fields. This study introduces a BCI-controlled wheelchair that utilizes Motor Imagery (MI) mental commands for turning left and right and Electrooculogram (EOG) signals, raising the eyebrows, for starting and stopping. The wheelchair offers 4 Degrees of Freedom (DoF), allowing users to move forward, stop, turn left, and turn right. The Emotiv Epoc headset is used to record the raw EEG data, the Common Spatial Patterns (CSP) algorithm is utilized to extract features from the data, and the Support Vector Machine (SVM) is employed to classify the mental commands. A total of 28 subjects, with half of them being individuals with motor and brain disabilities such as brain paralysis, severe brain disability, epilepsy, and spastic tetraplegia, participated in 5 experiments to assess the proposed BCI system. The results show that all participants, including those with disabilities, successfully adapted to and operated the BCI-controlled wheelchair with high accuracy and precision.

The goal of this systematic review is to examine the use of Brain-Computer Interfaces (BCIs) for controlling unmanned aerial vehicles (UAVs) in real-time. A comprehensive search across various online databases, including IEEE Explore, ScienceDirect, MDPI and PubMed, using the PRISMA research method, was conducted. The total of 42 experimental studies were identified, analyzed, and included in the final report. The review highlights several important research directions and areas that require further investigation in the field. Additionally, it identifies potential future possibilities and trends in BCI-controlled UAVs. Lastly, the review outlines the future challenges that researchers are likely to encounter, aiming to provide valuable insights and guidance for future studies in this area. To the authors’ best knowledge, this systematic review represents the most extensive analysis in literature, both in terms of the number of studies included and the span of years considered.

The increasing integration of brain–computer interfaces (BCIs) with augmented reality (AR) presents new possibilities for immersive and interactive environments, particularly through the use of head-mounted displays (HMDs). Despite the growing interest, a comprehensive understanding of BCI-AR systems is still emerging. This systematic review aims to synthesize existing research on the use of BCIs for controlling AR environments via HMDs, highlighting the technological advancements and challenges in this domain. An extensive search across electronic databases, including IEEEXplore, PubMed, and Scopus, was conducted following the PRISMA guidelines, resulting in 41 studies eligible for analysis. This review identifies key areas for future research, potential limitations, and offers insights into the evolving trends in BCI-AR systems, contributing to the development of more robust and user-friendly applications.

This study endeavored to design and develop an innovative closed-loop diagnostic and therapeutic system with the following objectives: (a) the noninvasive detection of glucose concentration in sweat utilizing nanonengineered screen-printed biosensors; (b) the management of measured data through a specialized computer system comprising both hardware and software components, thereby enabling the precise control of therapeutic responses via a patch-based nanomedicine delivery system. This initiative addresses the significant challenges inherent in the management of diabetes mellitus, including the imperative need for glucose-level monitoring to optimize glycemic control. Leveraging chronoamperometric results as a foundational dataset and the in vivo hypoglycemic activity of nanoemulsion formulations, this research underscores the efficacy and accuracy of glucose concentration estimation, decision-making mechanism responses, and transdermal hypoglycemic treatment effects, within the proposed system.

The field of Unmanned Aerial Vehicles (UAVs), or drones, is encountering quick development in the areas of air transportation and computerization. Progress in innovation has prompted more noteworthy capacities and highlights in UAVs, which are currently broadly involved by the military and flying industry for an assortment of high-end generally safe errands. Highly advanced UAVs that can be controlled remotely via a controller, mobile phone, or ground station cockpit have been developed through the integration of automation technology and machine vision, which includes thermal imaging, cameras, sensors, and other sensors. The three primary characteristics of UAVs will be investigated in this study, namely power-source technology, deep-learning neural networks for computer vision, and some of the applications that are used the most. The goal is to thoroughly examine these characteristics and offer suggestions for addressing some of the difficulties of optimizing UAV performance and also exploring potential future trends.

Background and objective: Electronic fetal heart monitoring is currently used during pregnancy throughout most of the developed world to detect risk conditions for both the mother and the fetus. Non-invasive fetal electrocardiogram (NI-fECG), recorded in the maternal abdomen, represents an alternative to cardiotocography, which could provide a more accurate estimate of fetal heart rate. Different methodologies, with varying advantages and disadvantages, have been developed for NI-fECG signal detection and processing. Methods: In this context, we propose a hybrid methodology, combining independent component analysis, signal quality indices, empirical mode decomposition, wavelet thresholding and correlation analysis for NI-fECG optimized signal extraction, denoising, enhancement and addressing the intrinsic mode function selection problem. Results: The methodology has been applied in four different datasets, and the obtained results indicate that our method can produce accurate fetal heart rate (FHR) estimations when tested against different datasets of variable quality and acquisition protocols, on the FECGDARHA dataset our method achieved average values of Sensitivity = 98.55%, Positive Predictive Value = 91.73%, F1 = 94.92%, Accuracy = 90.91%, while on the ARDNIFECG dataset it achieved average values of Sensitivity = 92.96%, Positive Predictive Value = 91.66%, F1 = 93.60%, Accuracy = 90.45%. Conclusions: The proposed methodology is completely unsupervised, has been proven robust in different signal-to-noise ratio scenarios and abdominal signals, and could potentially be applied to the development of real-time fetal monitoring systems.

Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.

Hemiplegia is a condition caused by brain injury and affects a significant percentage of the population. The effect of patients suffering from this condition is a varying degree of weakness, spasticity, and motor impairment to the left or right side of the body. This paper proposes an automatic feature selection and construction method based on grammatical evolution (GE) for radial basis function (RBF) networks that can classify the hemiplegia type between patients and healthy individuals. The proposed algorithm is tested in a dataset containing entries from the accelerometer sensors of the RehaGait mobile gait analysis system, which are placed in various patients’ body parts. The collected data were split into 2-second windows and underwent a manual pre-processing and feature extraction stage. Then, the extracted data are presented as input to the proposed GE-based method to create new, more efficient features, which are then introduced as input to an RBF network. The paper’s experimental part involved testing the proposed method with four classification methods: RBF network, multi-layer perceptron (MLP) trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm, support vector machine (SVM), and a GE-based parallel tool for data classification (GenClass). The test results revealed that the proposed solution had the highest classification accuracy (90.07%) compared to the other four methods.

The objective of this systematic review centers on cognitive assessment based on electroencephalography (EEG) analysis in Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) environments, projected on Head Mounted Displays (HMD), in healthy individuals. A range of electronic databases were searched (Scopus, ScienceDirect, IEEE Explore and PubMed), using PRISMA research method and 82 experimental studies were included in the final report. Specific aspects of cognitive function were evaluated, including cognitive load, immersion, spatial awareness, interaction with the digital environment and attention. These were analyzed based on various aspects of the analysis, including the number of participants, stimuli, frequency bands range, data preprocessing and data analysis. Based on the analysis conducted, significant findings have emerged both in terms of the experimental structure related to cognitive neuroscience and the key parameters considered in the research. Also, numerous significant avenues and domains requiring more extensive exploration have been identified within neuroscience and cognition research in digital environments. These encompass factors such as the experimental setup, including issues like narrow participant populations and the feasibility of using EEG equipment with a limited number of sensors to overcome the challenges posed by the time-consuming placement of a multi-electrode EEG cap. There is a clear need for more in-depth exploration in signal analysis, especially concerning the α, β, and γ sub-bands and their role in providing more precise insights for evaluating cognitive states. Finally, further research into augmented and mixed reality environments will enable the extraction of more accurate conclusions regarding their utility in cognitive neuroscience.

Brain-computer interfaces (BCIs) are becoming an increasingly popular technology, used in a variety of fields such as medical, gaming, and lifestyle. This paper describes a 3D non-invasive BCI game that uses a Muse 2 EEG headband to acquire electroencephalogram (EEG) data and OpenViBE platform for processing the signals and classifying them into three different mental states: left and right motor imagery and eye blink. The game is developed to assess user adjustment and improvement in BCI environment after training. The classification algorithm used is Multi-Layer Perceptron (MLP), with 96.94% accuracy. A total of 33 subjects participated in the experiment and successfully controlled an avatar using mental commands to collect coins. The online metrics employed for this BCI system are the average game score, the average number of clusters and average user improvement.

Recently, there has been a growing research interest in utilizing the electroencephalogram (EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article provides a detailed description of a resting-state EEG dataset of individuals with Alzheimer’s disease and frontotemporal dementia, and healthy controls. The dataset was collected using a clinical EEG system with 19 scalp electrodes while participants were in a resting state with their eyes closed. The data collection process included rigorous quality control measures to ensure data accuracy and consistency. The dataset contains recordings of 36 Alzheimer’s patients, 23 frontotemporal dementia patients, and 29 healthy age-matched subjects. For each subject, the Mini-Mental State Examination score is reported. A monopolar montage was used to collect the signals. A raw and preprocessed EEG is included in the standard BIDS format. For the preprocessed signals, established methods such as artifact subspace reconstruction and an independent component analysis have been employed for denoising. The dataset has significant reuse potential since Alzheimer’s EEG Machine Learning studies are increasing in popularity and there is a lack of publicly available EEG datasets. The resting-state EEG data can be used to explore alterations in brain activity and connectivity in these conditions, and to develop new diagnostic and treatment approaches. Additionally, the dataset can be used to compare EEG characteristics between different types of dementia, which could provide insights into the underlying mechanisms of these conditions.

The aim of this work is to highlight the possibilities of using VR applications in the informal learning process. This is attempted through the development of virtual reality cultural applications for historical monuments. For this purpose, the theoretical framework of virtual and augmented reality techniques is presented, developing as a showcase of the virtual environment of the historical bridge of Arta, in Greece. The bridge model is created through 3D software, which is then imported into virtual world environment by employing the Unity engine. The main objective of the research is the technical and empirical evaluation of the VR application by specialists, in comparison with the real environment of the monument. Accordingly, the use of the application in the learning process is evaluated by high school students. Using the conclusions of the evaluation, the environment will be enriched with multimedia elements and the application will be evaluated by secondary school students as a learning experience and process, using electroencephalography (EEG). The recording and analysis of research results can be generalized and lead to safe conclusions for the use of similar applications in the field of culture and learning.

Brain-computer interface (BCI) technology is a developing field of study with numerous applications. The purpose of this paper is to discuss the use of brain signals as a direct communication pathway to an external device. In this work, Zombie Jumper is developed, which consists of 2 brain commands, imagining moving forward and blinking. The goal of the game is to jump over static or moving “zombie” characters in order to complete the level. To record the raw EEG data, a Muse 2 headband is used, and the OpenViBE platform is employed to process and classify the brain signals. The Unity engine is used to build the game, and the lab streaming layer (LSL) protocol is the connective link between Muse 2, OpenViBE and the Unity engine for this BCI-controlled game. A total of 37 subjects tested the game and played it at least 20 times. The average classification accuracy was 98.74%, ranging from 97.06% to 99.72%. Finally, playing the game for longer periods of time resulted in greater control.

Non-alcoholic fatty liver disease (NAFLD) covers a range of chronic medical conditions varying from hepatocellular inflammation which characterizes nonalcoholic steatohepatitis (NASH) to steatosis, as the key element of a nonalcoholic fatty liver (NAFL). It is globally linked to the increasing prevalence of obesity and other components of metabolic syndrome and is expected to be the main indication for the existence of the liver disease in the coming years. Its eradication has become a major challenge due to the difficulties in clinical diagnosis, complex pathogenesis and the lack of approved therapies. In this study, an automated image analysis method is presented providing quantitative assessments of fat deposition in steatotic liver biopsy specimens. The methodology applies image processing, machine learning and evolutionary algorithm optimization techniques, producing a 1.93% mean classification error compared to the semiquantitative interpretations of specialized hepatologists.

Electroencephalography is one of the most commonly used methods for extracting information about the brain’s condition and can be used for diagnosing epilepsy. The EEG signal’s wave shape contains vital information about the brain’s state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals’ classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden–Fletcher–Goldfarb–Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.

Abstract Background The spectral information of the EEG signal with respect to epilepsy is examined in this study. Method In order to assess the impact of the alternative definitions of the frequency sub-bands that are analysed, a number of spectral thresholds are defined and the respective frequency sub-band combinations are generated. For each of these frequency sub-band combination, the EEG signal is analysed and a vector of spectral characteristics is defined. Based on this feature vector, a classification schema is used to measure the appropriateness of the specific frequency sub-band combination, in terms of epileptic EEG classification accuracy. Results The obtained results indicate that additional frequency band analysis is beneficial towards epilepsy detection. Conclusions This work includes the first systematic assessment of the impact of the frequency sub-bands to the epileptic EEG classification accuracy, and the obtained results revealed several frequency sub-band combinations that achieve high classification accuracy and have never been reported in the literature before.

The double-inverted pendulum (DIP) constitutes a classical problem in mechanics, whereas the control methods for stabilizing around the equilibrium positions represent the classic standards of control system theory and various control methods in robotics. For instance, it functions as a typical model for the calculation and stability of walking robots. The present study depicts the controlling of a double-inverted pendulum (DIP) on a cart using a fuzzy logic controller (FLC). A linear-quadratic controller (LQR) was used as a benchmark to assess the effectiveness of our method, and the results showed that the proposed FLC can perform significantly better than the LQR under a variety of initial system conditions. This performance is considered very important when the reduction of the peak system output is concerned. The proposed controller equilibration and velocity tracking performance were explored through simulation, and the results obtained point to the validity of the control method.

Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be utilized in research on stress system mobilization. Until recently, electroencephalography (EEG)-related research was focused on mental stress prompted by social or mathematical challenges, with only a few studies employing HMD VR techniques to induce stress. In this study, we combine a state-of-the-art EEG wearable device and an electrocardiography (ECG) sensor with a VR headset to provoke stress in a high-altitude scenarios while monitoring EEG and ECG biomarkers in real time. A robust pipeline for signal clearing is implemented to preprocess the noise-infiltrated (due to movement) EEG data. Statistical and correlation analysis is employed to explore the relationship between these biomarkers with stress. The participant pool is divided into two groups based on their heart rate increase, where statistically important EEG biomarker differences emerged between them. Finally, the occipital-region band power changes and occipital asymmetry alterations were found to be associated with height-related stress and brain activation in beta and gamma bands, which correlates with the results of the self-reported Perceived Stress Scale questionnaire.

Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians.

Hemiplegia affects a significant portion of the human population. It is a condition that causes motor impairment and severely reduces the patient’s quality of life. This paper presents an automatic system for identifying the hemiplegia type (right or left part of the body is affected). The proposed system utilizes the data taken from patients and healthy subjects using the accelerometer sensor from the RehaGait mobile gait analysis system. The collected data undergo a pre-processing procedure followed by a feature extraction stage. The extracted features are then sent to a neural network trained by the Levenberg-Marquardt backpropagation (LM-BP) algorithm. The experimental part of this research involved creating a custom-created dataset containing entries taken from ten healthy and twenty non-healthy subjects. The data were taken from seven different sensors placed in specific areas of the subjects’ bodies. These sensors can capture a three-dimensional (3D) signal using the accelerometer, magnetometer, and gyroscope device types. The proposed system used the signals taken from the accelerometers, which were split into 2-sec windows. The proposed system achieved a classification accuracy of 95.12% and was compared with fourteen commonly used machine learning approaches.

Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer’s disease (AD) is the most common neurogenerative disorder, making up 50–70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests.

In the context of glucocorticoid (GC) therapeutics, recent studies have utilised a subcutaneous hydrocortisone (HC) infusion pump programmed to deliver multiple HC pulses throughout the day, with the purpose of restoring normal circadian and ultradian GC rhythmicity. A key challenge for the advancement of novel HC replacement therapies is the calibration of infusion pumps against cortisol levels measured in blood. However, repeated blood sampling sessions are enormously labour-intensive for both examiners and examinees. These sessions also have a cost, are time consuming and are occasionally unfeasible. To address this, we developed a pharmacokinetic model approximating the values of plasma cortisol levels at any point of the day from a limited number of plasma cortisol measurements. The model was validated using the plasma cortisol profiles of 9 subjects with disrupted endogenous GC synthetic capacity. The model accurately predicted plasma cortisol levels (mean absolute percentage error of 14%) when only four plasma cortisol measurements were provided. Although our model did not predict GC dynamics when HC was administered in a way other than subcutaneously or in individuals whose endogenous capacity to produce GCs is intact, it was found to successfully be used to support clinical trials (or practice) involving subcutaneous HC delivery in patients with reduced endogenous capacity to synthesize GCs.

In this paper we investigate the essential minimum functionality of the autonomous blockchain, and the minimum hardware and software required to support it in the micro-scale in the IoT world. The application of deep-blockchain operation in the lower-level activity of the IoT ecosystem, is expected to bring profound clarity and constitutes a unique challenge. Setting up and operating bit-level blockchain mechanisms on minimal IoT elements like smart switches and active sensors, mandates pushing blockchain engineering to the limits. “How deep can blockchain actually go?” “Which is the minimum Thing of the IoT world that can actually deliver autonomous blockchain functionality?” To answer, an experiment based on IoT micro-controllers was set. The “Witness Protocol” was defined to set the minimum essential micro-blockchain functionality. The protocol was developed and installed on a peer, ad-hoc, autonomous network of casual, real-life IoT micro-devices. The setup was tested, benchmarked, and evaluated in terms of computational needs, efficiency, and collective resistance against malicious attacks. The leading considerations are highlighted, and the results of the experiment are presented. Findings are intriguing and prove that fully autonomous, private micro-blockchain networks are absolutely feasible in the smart dust world, utilizing the capacities of the existing low-end IoT devices.

Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic.

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson’s disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed—always under medical supervision—and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients’ physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient’s motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient’s status on previous days and define the optimal therapeutical treatment.

Wearable technologies for health monitoring have become a reality in the last few years. So far, most research studies have focused on assessments of the technical performance of these systems, as well as the validation of the clinical outcomes. Nevertheless, the success in the acceptance of these solutions depends not only on the technical and clinical effectiveness, but on the final user acceptance. In this work the compliance of a telehealth system for the remote monitoring of Parkinson’s disease (PD) patients is presented with testing in 32 PD patients. This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view. Diverse methodologies (REBA, Borg and CRS scales in combination with a body map) are employed to study the comfort, biomechanical and physiological effects of the system. The test results allow us to conclude that the acceptance of this system is satisfactory with all the levels of effect on each component scoring in the lowest ranges. This study also provided useful insights and guidelines to lead to redesign of the system to improve patient compliance.

Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

Alcohol consumption affects the function of the brain and long-term excessive alcohol intake can lead to severe brain disorders. Wearable electroencephalogram (EEG) recording devices combined with Brain Computer Interface (BCI) software may serve as a tool for alcohol-related brain wave assessment. In this paper, a method for mental state assessment from alcohol-related EEG recordings is proposed. EEG recordings are acquired with the Emotiv EPOC+, after consumption of three separate doses of alcohol. Data from the four stages (alcohol-free and three levels of doses) are processed using the OpenViBE platform. Spectral and statistical features are calculated, and Grammatical Evolution is employed for discrimination across four classes. Obtained results in terms of accuracy reached high levels (89.95%), which renders the proposed approach suitable for direct assessment of the driver’s mental state for road safety and accident avoidance in a potential in-vehicle smart system.

This paper presents a solution for monitoring of solar ultraviolet (UV) exposure and alerting about risks in real time. The novel system provides smart personalized indications for solar radiation protection. The system consists of a sensing device and a mobile application. The sensing device monitors solar radiation in real time and transmits the values wirelessly to a smart device, in which the mobile application is installed. Then, the mobile application processes the values from the sensory apparatus, based on a fuzzy expert system (FES) created from personal information (hair and eye color, tanning and burning frequency), which are entered by the user answering a short questionnaire. The FES provides an estimation of the recommended time of safe exposure in direct sunlight. The proposed system is designed to be portable (a wearable sensing device and smartphone) and low cost, while supporting multiple users.

Hepatic steatosis is the accumulation of fat in the hepatic cells and the liver. Triglycerides and other kinds of molecules are included in the lipids. When there is some defect in the process, hepatic steatosis arise, during which the free fatty acids are taken by the liver and exuded as lipoproteins. Alcohol is the main cause of steatosis when excessive amounts are consumed for a long period of time. In many cases, steatosis can lead to inflammation that is mentioned as steatohepatitis or non-alcoholic steatohepatitis (NASH), which can later lead to fibrosis and finally cirrhosis. For automated detection and quantification of hepatic steatosis, a novel two-stage methodology is developed in this study. Initially, the image is processed in order to become more suitable for the detection of fat regions and steatosis quantification. In the second stage, initial candidate image regions are detected, and then they are either validated or discarded based on a series of criteria. The methodology is based on liver biopsy image analysis, and has been tested using 40 liver biopsy images obtained from patients who suffer from hepatitis C. The obtained results indicate that the proposed methodology can accurately assess liver steatosis.