Unmanned aerial vehicles (UAVs) or simply drones are a fast growing technology with many applications in several sectors, one of them being the transportation of pharmaceuticals. In this work, we study the use of UAVs for transporting pharmaceuticals between hospitals, and we solve the specific problem of the placement of the stations where the UAVs will be based and controlled. The aim of this work is to calculate the minimum number of drone stations required, as well as their locations, to connect a set of hospitals. To achieve this, we consider both the range of the drones and the operational range of the stations, which is defined by the distance over which a drone can be controlled. We initially identify and exclude hospitals that cannot be served because they are beyond the range of drones or stations. Then, we apply a heuristic algorithm to generate an initial set of potential locations for the stations. This solution is given as input to a metaheuristic algorithm that is an adaptation of the well-known Tabu search algorithm. This algorithm improves the initial solution through an iterative process. We evaluated our algorithm through both numerical experiments and a realistic case study, confirming its efficiency and applicability to the task at hand.

This study presents the validation of the Greek translation of the Godspeed Questionnaire Series (GQS) through an interactive workshop. Data were collected from 94 participants who assessed seven different wearable robotic devices in person and online. The validation examined psychometric characteristics including internal consistency reliability and factor structure preservation. The Greek translation successfully adapted semantic differentials while achieving equivalent performance compared to the original English version. Cross-language equivalence was maintained across most dimensions. All subscales demonstrated significant discriminative ability between robotic devices, validating the Greek GQS as a reliable instrument for human-robot interaction research and user assessment in wearable robotics applications.

This study examines the ecological validity of a Living Lab (LL) experimentation protocol by comparing it with in-the-wild data collection from older adults, focusing on walking speed as a measure of indoor ambulation. Data were collected in two settings: a Living Lab testbed resembling a home space and real world environment (homes). Three older adult cases were included in the current study. A case-series exploratory approach was used to analyze walking speed patterns across the two environments - the living lab and the real-life one. Statistical features were calculated, and statistical tests were applied to assess differences and agreement between the experimental conditions. The results revealed higher variability and lower values in walking speed in the home environment compared to the Living Lab, suggesting that the suggested experimental setting still lacks ecological validity. Given the small sample size, results are considered as hypothesis generation rather than definitive conclusions.

The digitization of the healthcare domain has the potential to drastically improve healthcare services, reduce the time to diagnosis, and lower costs. However, digital applications for the healthcare domain need to be interoperable to maximize their potential. Additionally, with the rapid expansion of Artificial Intelligence (AI) and, specifically, Machine Learning (ML), large amounts of diverse types of data are being utilized. Thus, to achieve interoperability in such applications, the adoption of common semantic data models becomes imperative. In this paper, we describe the adoption of such a common semantic data model, using the well-known Health Level Seven Fast Health Interoperability Resources (HL7 FHIR) standard, in a platform that assists in the creation and storage of a plethora of AI-based applications for several medical conditions. The server’s efficiency is being showcased by using it in an application predicting coronary artery stenosis as well as for managing the platform’s key performance indicators.

Abstract Virtual reality has long existed, but its wider adoption in education is recent. Studies informed by theoretical underpinned co-creation frameworks and utilization of theoretical informed evaluations are scarce in literature. Thus, this study internationally evaluated the efficacy of three virtual reality reusable e-resources (VRReRs), co-created based on the ASPIRE framework, for teaching clinical skills to university students. The study followed a mixed-methods approach, combining SUS, SUS Presence Questionnaire, TAM, and UTAUT2 with a focus group discussion. Additionally, for one VRReR, a quantitative pre/post evaluation of knowledge and comparison with lecture notes followed. Results demonstrated moderately to highly usability, effectively facilitated a strong sense of presence, confidence while using them, and willingness to continue using VRReRs in the future, while increased knowledge of the learners, highlighted their effectiveness. Although some usability issues were identified, these were considered easy to address. This work evidence, in an international context, that co-created VR resources are highly acceptable and effective, similar to other types of digital or traditional resources developed through participatory inquiry paradigm. By leveraging the benefits of VR technology, VRReRs have the potential to transform and enhance the learning experience in the field of clinical skills, ultimately advancing the digitalization of higher education.

Cancer prevalence, particularly among older individuals, imposes a significant burden on healthcare systems. However, care for older cancer patients is often insufficient and fails to address their specific needs. The LifeChamps H2020 EU project aims to develop a patient-centered digital platform to improve monitoring, anticipation, and support for complications that can deteriorate the health-related quality of life (HRQoL) of older cancer survivors. By facilitating comprehensive clinical assessments and new integrated care models, it addresses the challenges and gaps in clinical practice, promoting patient-centered care through remote digital monitoring tools that collect HRQoL data not typically captured in routine clinical practice.

Synthetic data is becoming the way forward to manage legal and regulatory aspects of biomedical research involving personal and clinical data. As no matches are expected between artificial instances and real samples and/or subjects, external researchers performing secondary analyses could benefit significantly by having unlimited access to uncompromised information. In this context, one of the main objectives of the H2020 VITALISE project is to develop a platform for providing synthetic data generated from real data collected in Living Labs to those external researchers. In addition, while some time series specific synthetic data generation models exist, only a few of them consider metadata (e.g., patient demographics) as part of the time series generation process itself. Therefore, the objective of this research is to perform a comparative assessment of two synthetic data generation models that use and process the metadata of subjects differently: The Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). To achieve this goal making sure the analyses were data-independent, we selected two healthcare-related longitudinal datasets: (1) Treadmill Maximal Effort Test (TMET) measurements from the University of Málaga; and (2) a hypotension subset derived from the MIMIC-III v1.4 database. After synthetic data was generated, we assessed three pivotal aspects: resemblance to the original data, utility, and level of privacy. As a main conclusion, the importance of using metadata as context variables and the methodology to take them into account was proved to be significant and valuable, the DGAN model offering better results overall. A more extensive time series specific evaluation is left as the main avenue for future research.

One of the most well-known laws of software evolution suggests that code quality deteriorates over time. Following this law, recent empirical studies have brought evidence that Technical Debt (TD) Principal tends to increase (in absolute value) as the system grows, since more technical debt issues are added than resolved over time. To shed light into how technical debt accumulation occurs in practice, in this paper we explore specific maintenance activities (i.e., feature addition, bug fixing, and refactoring) and explore the balance between the technical debt that they introduce or resolve. To achieve this goal, we rely on studying Pull Requests (PR), which are the most established way to contribute code to an open-source project. A Pull Request is usually comprised by more than one commits, corresponding to a specific development / maintenance activity. In our study, we categorized Pull Requests, based on their labels, to find the effect that the different maintenance activities have on the accumulation of technical debt across evolution. In particular, we have analysed more than 13.5K pull requests (mined from 10 OSS projects), by calculating the TD Principal (calculated through SonarQube) before and after the Pull Requests. The results of the study suggested that several labels are used for tagging Pull Requests, out of which the most prevalent ones are new features, bug fixing, and refactoring. The effect of these activities on TD Principal accumulation is statistically different, and: (a) the addition of features tends to increase TD Principal; (b) refactoring is having an almost consistent positive effect (reducing TD Principal); and (c) bug fixing activity has undecisive impact on TD Principal. These results are compared to existing studies, interpreted, and various useful implications for researchers and practitioners have been drawn.

There is growing need for intelligent digital support in healthcare, business, industry, indeed all domains. Learners in any domain need educators/experts to provide knowledge and guidance by relaying relevant and timely information. Artificial Intelligence (AI) systems such as chatbots or conversational agents, can support information delivery. However, user-centric frameworks for designing chatbot learning applications and collaborative user-centric design methodologies are lacking. This paper describes a chatbot design tool to promote co-creation through crowd-based data collection and design. This prototype has the capacity to be used as a structured method of design and data collection through cocreation for healthcare, education, and beyond.

There has been an increased interest in medical education and, specifically, in identifying appropriate methods to ensure efficiency in medical students’ learning process. The design and development of Immersive experiential technologies, including Virtual patients, chatbots, and Virtual, Augmented, or Mixed Reality (VR/AR/MR), through co-creative methods, find fertile ground to support medical educational material. The aim of the current paper was to evaluate the perceptions and gain insights on healthcare technologists’ and educators’ experience regarding a series of co-creation sessions conducted during the design and development of XR educational resources. Results of the qualitative research showed that co-creation can be an interactive and engaging method that involves individuals from multidisciplinary backgrounds for effectively designing XR educational resources. However, strict adherence to best practices, as described in this work, is required to avoid hindering the effectiveness of the process and the quality of the final resources.

Curriculum size constantly challenges healthcare education and training. Technology enhanced, immersive educational content like Virtual, Augmented, or Mixed Reality (VR/AR/MR collectively XR) constantly aims to facilitate knowledge retention and skills acquisition in the healthcare sector. Core challenge in this effort is the increasing costs, in time and resources, required for designing and developing XR immersive educational content. An approach to address this challenge is participatory design methods. Co-creation approaches distribute the burden of content development amongst the educators' community and facilitate decentralized bottoms-up content creation. This approach requires data modeling approaches that facilitate digital asset discoverability, reusability and consumption through visual authoring tools. This work describes the conceptualization and implementation of a UX taxonomy for annotating immersive AR/VR/MR content at the asset level for maximum repurposing capacity. A brainstorming session between educational and technology experts was conducted and conceptual details of the terms of the taxonomy were described. The Simple Knowledge Organization System (SKOS) vocabulary was utilized to organize this devised taxonomy and link it with existing medical ontology terms, formulating an RDF endpoint of the nominated ENTICE ontology. This ontology was able to link medical terms with UX and educational properties in a data structure that can annotate and contextually encapsulate any XR digital asset. An example such term is described and presented as proof of application. The semantic modelling implemented in this work is directly applicable to a previously proposed visual data structure and subsequent authoring environment that could facilitate XR resource design and authoring from non-technical experts.

Virtual Reality (VR), blanket term used for all the reality-virtuality spectrum, is exploding in proliferation, especially in healthcare. The core challenge of this explosion is the timely and cost-effective provision of bespoke content. Co-creative approaches can facilitate this endeavor by making the educators active participants in the development process reducing development overheads and democratizing healthcare digital content creation. This panel session aims at health educators, VR technologists, developers and technology enthusiasts. It will present, and actively engage its audience with co-creative methods and approaches, providing them with the gateway experience that could become useful for introducing such methods in their institutions and workflows. The panelists are cross-disciplined educational technologists/medical educators with extensive experience in the field of technology enhanced learning (TEL) who have implemented such methodologies in practice. The workshop will follow a mixed format. A brief “observations from the field” introduction to the topics of VR TEL and co-creation will be followed by an introduction to real world healthcare VR projects and resources. In these, realistic examples, the participants will engage in hands-on storyboarding and non-technical design and development tasks, in order to acclimate with the co-creative process and become able to explore it for their own use cases.

Abstract Research demonstrates that positive emotions contribute to students’ greater engagement with the learning experience, while negative emotions may detract from the learning experience. The purpose of this study is to evaluate the effect of a computer-based training program on the emotional status and its effect on the performance of two students with learning difficulties: a second-grade student of a primary school with Simpson-Golabi-Behmel syndrome and a fourth-grade student of a primary school with learning difficulties. For the purpose of this study, the “BrainHQ” web-based cognitive training software and the mobile app “AffectLecture” were used. The former was used for measuring the affective state of the students before and after each intervention. The latter was used for improving students’ cognitive development, in order to evaluate the possible improvement of their initial emotional status after the intervention with “BrainHQ” program, the possible effect of positive/negative emotional status on their performance, as well as the possible effect of high/poor performance on their emotional status. The results of the study demonstrate that there is a positive effect of emotion on performance and vice versa and the positive effect of performance on the emotional status and vice versa. These findings suggest that the affective state of students should be taken into account by educators, scholars and policymakers.

The primary manifestations of Parkinson Disease (PD) concern abnormalities of movement associated with the constant deterioration of motor skills. Such motor impairment affects patients' movement accuracy and coordination, disrupting their daily life. Taking into account recent studies stating that computer-based physical therapy games can be used as a PD rehabilitation option, we propose a novel Exergame, the iPrognosis Warming up Game (http://www.i-prognosis.eu/), as a user-friendly tool that could both serve as a computer-based physical therapy game, as well as a means of accurately and automatically identifying the severity of PD motor symptoms. To this regard, we propose a novel deep learning methodology for motor impairment stage prediction that relies solely on human body motion data extracted from the recorded game sessions. Experimental results using a dataset of both early and advanced PD patients reveal a good classification performance of the proposed methodology, predicting the motor impairment stage of PD patients and paving the way for additional research in the field.

T e-Coaches are promising intelligent systems that aims at supporting human everyday life, dispatching advices through different interfaces, such as apps, conversational interfaces and augmented reality interfaces. This workshop aims at exploring how e-coaches might benefit from spatially and time-multiplexed interfaces and from different communication modalities (e.g., text, visual, audio, etc.) according to the context of the interaction.

Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physical well-being and pathological conditions. A prerequisite for further analysis is the identification of the sleep macroarchitecture through manual sleep staging. Several computer-based approaches have been proposed to extract time and/or frequency-domain features with accuracy ranging from 80% to 95% compared with the golden standard of manual staging. However, their acceptability by the medical community is still suboptimal. Recently, utilizing deep learning methodologies increased the research interest in computer-assisted recognition of sleep stages. Aiming to enhance the arsenal of automatic sleep staging, we propose a novel classification framework based on convolutional neural networks. These receive as input synchronizations features derived from cortical interactions within various electroencephalographic rhythms (delta, theta, alpha, and beta) for specific cortical regions which are critical for the sleep deepening. These functional connectivity metrics are then processed as multidimensional images. We also propose to augment the small portion of sleep onset (N1 stage) through the Synthetic Minority Oversampling Technique in order to deal with the great difference in its duration when compared with the remaining sleep stages. Our results (99.85%) indicate the flexibility of deep learning techniques to learn sleep-related neurophysiological patterns.

The papers in this special section focus on the challenges faced in the digital healthcare market. Recent advances in information and communication technologies (ICT), as well as biomedical engineering, sensor technology and data science, have acted as catalysts for significant developments in the sector of health care, strongly affecting medical diagnosis, patient and healthcare management, disease treatment and health education. In fact, small wearable, disposable sensors, implantable devices or medical devices, as well as elementary services are being featured as keys for monitoring health and facilitating well-being.

Cancer is one of the most prevalent diseases in Europe and the world. Significant correlations between dietary habits and cancer incidence and mortality have been confirmed by the literature. Physical activity habits are also directly implicated in the incidence of cancer. Lifestyle behaviour change may be benefited by using mobile technology to deliver health behaviour interventions. M-Health offers a promising cost-efficient approach to deliver en-masse interventions. Smartphone apps with constructs such as gamification and personalized have shown potential for helping individuals lose weight and maintain healthy lifestyle habits. However, evidence-based content and theory-based strategies have not been incorporated by those apps systematically yet. The aim of the current work is to put the foundations for a methodologically rigorous exploration of wellness/health intervention literature/app landscape towards detailed design specifications for connected health m-apps. In this context, both the overall work plan is described as well as the details for the significant steps of application space and literature space review. Both strategies for research and initial outcomes of it are presented. The expected evidence based design process for patient centered health and wellness interventions is going to be the primary input in the implementation process of upcoming patient centered health/wellness m-health interventions.

As adults get older, the risks are increasing on their health, such as chronic diseases, functional decline and geriatric syndromes which threaten their well-being. Technology has many features to support aging wellness enabling older people maintaining healthy and sociable as they grow. However, the needs of the elderly of the population are not always the same. Stealth assessment, coming from the educational domain, can assess such needs. Virtual scenarios, interactive simulation technologies that recreate realistic scenarios combined with novel and effective interaction modalities such as dialogue integrations are contemporary means for such endeavors. Thus, coalescing all these axes and disciplines this work aims to explore the applicability of a dialogue based virtual scenario stealth assessment approach for evaluating the perceptions of elderly towards technology based health interventions. Utilizing a simple dialogue based authoring and deployment platform a simple scenario was devised. 21 elderly, aged 60-80 (6 males, 15 females) run the scenario and filled out a validator questionnaire for their perceptions towards technology. Correlation was discovered between the devised instrument and the elderlys desire to use technology in everyday life. These results provide the necessary foundations for refining future iteration of such stealth assessment instruments with Evidence Centered Design Principles.

Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms. Two BCI-naïve female subjects, a SCI patient and a healthy control subject participated in the proof-ofconcept implementation. They were instructed to perform motor imagery (MI) while watching multiple movements of either arms or legs during walking, while under 128-channel EEG recording. They were, subsequently, asked to control two robotic arms (Mercury v2.0) using a commercial class EEGBCI. They both achieved comparable performance levels of robotic control, 52.5% for the SCI patient and 56.9% for the healthy control. We performed a feasibility analysis of functional networks on the EEG-BCI recordings. Visual MI allows training on multiple imagined movements and shows promise in investigating differences in functional cortical networks associated with different motor tasks. This approach could allow the implementation of functional network-based BCIs in the future for complex movement control.

This paper focuses on developing a novel approach to automatic sleep stage classification based on electroencephalographic (EEG) data. The proposed methodology employs contemporary mathematical tools such as the synchronization likelihood and graph theory metrics applied on sleep EEG data. The derived features are then fitted into three different machine learning techniques, namely k-nearest neighbors, support vector machines and neural networks. The evaluation of their comparative performance is investigated according to their accuracy. Interestingly, the support vector machine achieves the maximum possible accuracy, i.e., 89.07%, which renders it as a suitable method for sleep stage classification.

The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games). From the methodological point of view, to facilitate the visualization of 14 game-scenarios, the system interface and the PD contexts, the storyboarding technique was adopted here. Overall, the realization of the PGS sets the basis for establishing a holistic framework that could aim at improving motor and non-motor symptoms, in order to inform health care providers and policy makers for its inclusion in routine management for PD.

Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards to their suitability for BCI applications has been made. The choice of MI approach could affect individual performance during BCI control, especially for off-the-shelf BCI systems, where ease of use and fast reliable results is the target. Whether for healthy individuals or clinical applications, if such systems are expected to reach consumer maturity, best practices for their use should be investigated. We designed a study to compare VMI and KMI as control modalities of an off-the-shelf EEG-BCI system. 30 healthy individuals (18 male, 12 female) participated in the study, operating two house-developed robotic arms (Mercury 2.0) using an Emotiv EPOC EEG-BCI. They were asked to use first VMI and then KMI to achieve BCI control and we compared the training and success rates. In our study, KMI achieved higher skill percentages during imagery training but VMI achieved higher success rates during BCI control of both robotic arms. Nonetheless, observed differences did not exceed significance thresholds. Individual differences could play a major role in MI performance and should be taken into account when choosing which modality to train for the use of a BCI system.

Mathematical Anxiety is the feeling of fear or dislike when dealing with mathematical rich situations. Although math anxiety seems to be innocent it can seriously affect so the learning procedure, as the future carrier directions. The accurate recognition of math anxiety is very important so for diagnostic purposes as for e-learning systems. This work comes to present an automatic system for the detection of math anxiety based on electroencephalographic (EEG) signals, that are supposed to be more subjective, compared to self-report and psychometric questionnaires, since they cannot be intentionally modulated. For this reason we have gathered multichannel EEG recordings from two groups with different levels of math anxiety (Low and High). From these EEG signals we have extracted 466 features and then using a feature selection algorithm we ended to only one feature that was able to recognize math anxiety with 93.75% accuracy using a Naïve Bayesian Tree with 10-fold cross validation.

The inclusion of information and communication technologies in Healthcare and Medical Education is a fact nowadays. Furthermore numerous virtual learning environments have been established in order to host both educational material and learners online activities. Online modules in a VLE can be designed in very different ways being part of different types of courses, while different models can be used to design the course based on what the creator aims to achieve. Thus, the types and the importance of the different elements of the online course may vary a lot. At the same time the need of a global approach to gather big educational data in order to provide valid meaning to the data through learning analytics and educational data mining is urgent. In order this to be achievable we propose a Learner Activity Taxonomy in which the different elements of the learners activity data can be categorised and a Learner Engagement Framework in which the importance of the different elements is vital in order for an analysis of the big educational data to provide a meaningful result. The initial application to practice of the Taxonomy and the Framework are presented based on data from 3 modules at 2 Universities, while the impact of them along with its limitations are discussed.

Bot Assistants can be an efficient and low-cost solution to Patient Care. One important aspect of Assistant Bots is successful Communication and Socialization with the patient. A new Conditional Entropy Retrieval Based model is proposed and also an Attitude Modeling based on Popitz Powers. The algorithm successfully retrieves the suitable answer with a high success rate in the patient-Bot Assistant dialogue interaction. Moreover, the Conditional Entropy Model and the Popitz Attitude Model are combined in order to identify Attitude Changes in Dialogue Interactions between patients and doctors.

This pilot study was conducted in the framework of SmokeFreeBrain project and it aimed at assessing the subjective emotional impact of skin temperature training and neurofeedback training on smokers by means of the AffectLecture application. The current paper constitutes a proof-of-concept, exploring the case of a single participant. The intervention consists of 5 sessions of biofeedback followed by 20 sessions of neurofeedback. Both pre- and post- biofeedback and neurofeedback training subjective scores of the participants mood were collected through the application. Based on our results, biofeedback training seems to promote alterations in mood, which are then maintained in the baseline mood scoring before neurofeedback training. Additionally, mood seems to be preserved after neurofeedback training. However, significant correlations between scoring and training performance have not been indicated.

Elementary school students experience significant personal changes; their bodies change, as well as their inner selves. Their emotional status can be affected by both intrinsic and extrinsic factors; therefore, it is vital to highlight the emotional factors that influence the learning process, as a negative emotional status may lead to reduced motivation and low school performance, whereas a positive one may bring the opposite results. The aim of this study is to evaluate the effects of the teaching process onto the students emotional status, and how it affects their academic performance. The study was conducted on 15 3rd-grade elementary school students in Greece. The students scores on weekly reviewing tests were used, in the subjects of Language, Math, History, and Environmental Study. The affective state of the students was measured by the AffectLecture app of the AUTh Medical Physics lab. The results indicate that after attending the Environmental Study class, students have a more positive emotional status; on the other hand, this wasnt the case with the other subjects. Additionally, the students emotions and their test marks, exhibit a strong positive correlation. These findings suggest that the affective state of students and how it alters should be taken into account by education professionals, researchers and policy makers.

A plethora of studies has shown that working memory, processing speed and fluid intelligence are diminished with aging. However, emotional processing remains relatively stable even though emotional processing alters through aging. Neurophysiological studies have employed emotional stimuli to investigate age differences through Event Related Potentials (ERPs). The present approach used affective visual word stimuli derived from the Greek language. Healthy young and elderly volunteers passively viewed the stimuli which were divided into pleasant, unpleasant and neutral. The study shows differential processing of emotional stimuli in comparison to the neutral in terms of temporal resolution (latency) and activation of neuronal assembles (amplitude). The age factor interacts with emotional dimension through a complex pattern while laterality differences also occur. Our results suggest a difference in the way emotional stimuli are processed during aging through functional compensation.

The attractiveness and robustness of graph theory has stimulated an unprecedented increase in studies aiming to understand the functional organization and dynamics of the brain. The investigation of brain connectomics produces a tremendous amount of data which may be examined at both a macroscopic and microscopic level. However, the interpretation of findings is still challenging. Novel methodological approaches should enhance the arsenal of the tools employed towards the understanding of the interaction of the distinct brain components. So, in this paper we propose a multi-parametric approach to identify cortical components that co-ordinate the activity of specific sub-networks and converge the information integration to a global network level. It also provides a quantification of the hubs strength, which may be useful to investigate how hubs develop and change across the life-span. Thus, it is hypothesized the proposed methodology could serve as a robust outcome measure for identifying the benefits of traditional dance intervention in terms of functional re-organization and neuroplasticity.

Premature detection of malignant melanoma remains the primary factor in reducing mortality from this form of cancer. During the last decade diagnostic sensitivity and specificity have improved through the utilization of new computer-based technologies, which help improve lesion selection for pathology review and biopsy. Despite these advances in melanoma diagnosis, initial detection, timely recognition and quick treatment of melanoma remain crucial. Despite the fact that the gold standard for diagnosis remains pathologic examination, this form of cancer has the potential to be diagnosed through non-invasive and radiation-free techniques that are based on electrical impedance spectroscopy (EIS). For this reason, the aim of this study was to design a prototype device (now in its 2nd generation) which will be able to assist clinicians detect early stage melanoma. The DermaSense electrical impedance spectroscopy system (EIS), a novel portable dermatological scanner, offers non-invasive electrical impedance spectroscopy data from multiple dry electrodes placed on a patients skin. A proof of concept prototype of the DermaSense device has been constructed and is currently being evaluated, while clinical pilot measurements related to melanoma are being planned. Initial testing results of the device are promising, enriching the objective physiological evidence available to dermatologists at the point of care. The purpose of this paper is to provide initial results from evaluating the prototype device and to propose statistical processes which can reveal diagnostically meaningful patterns in the data.

Exergames have been the subject of research and technology innovations for a number of years. Different devices and technologies have been utilized to train the body and the mind of senior people or different patient groups. In the past, we presented FitForAll, the protocol efficacy of which was proven through widely taken (controlled) pilots with more than 116 seniors for a period of two months. The current piece of work expands this and presents the first truly web exergaming platform, which is solely based on HTML5 and JavaScript without any browser plugin requirements. The adopted architecture (controller application communication framework) combines a unified solution for input devices such as MS Kinect and Wii Balance Βoard which may seamlessly be exploited through standard physical exercise protocols (American College of Sports Medicine guidelines) and accommodate high detail logging; this allows for proper pilot testing and usability evaluations in ecologically valid Living Lab environments. The latter type of setups is also used herein for evaluating the web application with more than a dozen of real elderly users following quantitative approaches.

Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Multi-DoF robotics control is possible by patients through commercial wireless BCI. Multiple sessions and tailored BCI algorithms are needed to improve performance.

Many platforms have emerged as response to the call for technology supporting active and healthy aging. Key requirements for any such e-health systems and any subsequent business exploitation are tailor-made design and proper evaluation. This paper presents the design, implementation, wide deployment, and evaluation of the low cost, physical exercise, and gaming (exergaming) FitForAll (FFA) platform system usability, user adherence to exercise, and efficacy are explored. The design of FFA is tailored to elderly populations, distilling literature guidelines and recommendations. The FFA architecture introduces standard physical exercise protocols in exergaming software engineering, as well as, standard physical assessment tests for augmented adaptability through adjustable exercise intensity. This opens up the way to next generation exergaming software, which may be more automatically/smartly adaptive. 116 elderly users piloted FFA five times/week, during an eight-week controlled intervention. Usability evaluation was formally conducted (SUS, SUMI questionnaires). Control group consisted of a size-matched elderly group following cognitive training. Efficacy was assessed objectively through the senior fitness (Fullerton) test, and subjectively, through WHOQoL-BREF comparisons of pre-postintervention between groups. Adherence to schedule was measured by attendance logs. The global SUMI score was 68.33±5.85%, while SUS was 77.7. Good usability perception is reflected in relatively high adherence of 82% for a daily two months pilot schedule. Compared to control group, elderly using FFA improved significantly strength, flexibility, endurance, and balance while presenting a significant trend in quality of life improvements. This is the first elderly focused exergaming platform intensively evaluated with more than 100 participants. The use of formal tools makes the findings comparable to other studies and forms an elderly exergaming corpus.

Gait analysis is nowadays considered, as a promising contributor towards early detection of cognitive and physical status deterioration when it comes to elderly people. However, the majority of recent efforts on indoor gait analysis methodologies are limited as they only exploit the average walking speed. Applying density based clustering algorithms on indoor location datasets could accelerate context awareness on gait analysis and consequently augment information quality with regard to underlying gait disorders. This work presents the application of DBScan, a well-known algorithm for knowledge discovery, on indoor Kinect location datasets collected in the Active and Healthy Aging Living Lab in the Lab of Medical Physics of the Aristotle University of Thessaloniki. The aim of the paper is to provide evidence that such an approach could effectively discriminate indoor activity High Density Regions which may subsequently be transferred to datasets originated from seniors' real homes in the light of context aware gait analysis.

Artificial intelligence and decision support systems offer a plethora of health monitoring capabilities in ambient assisted living environment. Continuous assessment of health indicators for elderly people living on their own is of utmost importance, so as to prolong their independence and quality of life. Slow varying, long-term deteriorating health trends are not easily identifiable in seniors. Thus, early sign detection of a specific condition, as well as, any likely transition from a healthy state to a pathological one are key problems that the herein proposed framework aims at resolving. Statistical process control concepts offer a personalized approach toward identification of trends that are away from the atypical behavior or state of the seniors, while fuzzy cognitive maps knowledge representation and inference schema have proved to be efficient in terms of disease classification. Geriatric depression is used as a case study throughout the paper, so to prove the validity of the framework, which is planned to be pilot tested with a series of lone-living seniors in their own homes.

This paper presents a novel method for tracking the position of a medical instrument's tip. The system is based on phase locking a high frequency signal transmitted from the medical instrument's tip to a reference signal. Displacement measurement is established having the loop open, in order to get a low frequency voltage representing the medical instrument's movement; therefore, positioning is established by means of conventional measuring techniques. The voltage-controlled oscillator stage of the phase-locked loop (PLL), combined to an appropriate antenna, comprises the associated transmitter located inside the medical instrument tip. All the other low frequency PLL components, low noise amplifier and mixer, are located outside the human body, forming the receiver part of the system. The operating details of the proposed system were coded in Verilog-AMS. Simulation results indicate robust medical instrument tracking in 1-D. Experimental evaluation of the proposed position tracking system is also presented. The experiments described in this paper are based on a transmitter moving opposite a stationary receiver performing either constant velocity or uniformly accelerated movement, and also together with two stationary receivers performing constant velocity movement again. This latter setup is implemented in order to demonstrate the prototype's accuracy for planar (2-D) motion measurements. Error analysis and time-domain analysis are presented for system performance characterization. Furthermore, preliminary experimental assessment using a saline solution container to more closely approximate the human body as a radio frequency wave transmission medium has proved the system's capability of operating underneath the skin.

To tackle the problem of training the careers of senior citizens numerous educational options may be attempted. However, the emerging complex puzzle of the diversity of training needs for the aforementioned trainee group, consisting of a mixture of formal and informal careers, calls for a careful and perhaps more radical than the usually attempted approach. To this extent, this paper describes elements of the innovative efforts followed upon the training design of the DISCOVER EU project. Specifically, simulations of virtual patients in Second Life are exploited in conjunction with educational components and objects in semantically extended e-learning environments, in an attempt to make more realistic everyday training cases and diverse topic, content rich approaches. The whole endeavor is presented in view of the existing limitations to elderly careers' training and is soon to lend itself to a rigorous pilot phase in Greece. Planned evaluation sessions are envisaged to certify successes, to inform for any required improvements but also to identify pitfalls.

Highly proficient eHealth health IT professionals assure health care and public health, while healthcare professional workforces should have the eSkills needed to make optimum use of their available eHealth health information technology. Addressing IT skills for healthcare workforce is seen as an important element of achieving greater social inclusion. Thus, in this paper we propose a framework for a social network based registry where the users (healthcare workforce), the institutions and the policy makers could, collaboratively shape the required IT skills and competences for each healthcare profession. At the same time, all knowledge created at the registry could be shared through the semantic web and linked with existing silos of knowledge.

Recent studies and workshops have stressed the fundamental importance of providing harmonised health and social care services that meet the extended needs of the individual, taking into account diversity in need, preferences, ability and support. For a real caring society, the notion of "information sharing" becomes clearly crucial when communication and interaction in health care is needed. This paper revisits recent funded projects undertaken by our team, in view of the so-called coordination of planned activities for elderly/disabled care and independent living support. Three development pillars are taken under consideration herein, namely, affective exergaming services for seniors/disabled, social media and the semantic web. Outputs from a multitude of projects are then used to provide evidence of how issues recently encountered under the so-called blue line dimensions are tackled; these are technological interoperability, semantic integration, modern interfaces, people needs/expectations and societal incentives. As the latter synthesize a con-temporary puzzle of personalised health care approach, this paper provides an innovative perspective of interlinking developments and outputs of research in view of improving healthcare.

Attention deficit-hyperactivity disorder (ADHD) is a neurobehavioral disorder characterized by either significant difficulties of attention or hyperactivity and impulsiveness or a combination of the two. Behavioral interventions seem to be a good treatment plan for ADHD, although it requires continuous monitoring of the ADHD condition in order to adapt the intervention over time. There have only been rather few computerized monitoring systems so far, mostly based on pre-existing classic forms for ADHD assessment. This study reviews the most popular computerized monitoring systems for ADHD, in an effort to suggest new ways for enhancing education for related healthcare professionals thereby indicating some interesting directions for future work.

Dramatic changes in the demographic situation across western countries suggest that new policies towards elderly healthcare should be followed. Among these, timely detection and forecasting of early signs of pathological physical, cognitive or emotional health is of paramount importance, since it will allow for certain medicative strategies to be applied. The USEFIL project as such, aims at developing services that would contribute to the prolongation of seniors' independent living and support them during their daily life activities. An unobtrusive sensor network along with several intelligent processing algorithms will result to a better understanding of the senior population health course. In this paper the modeling and preliminary analysis (first evaluation) of a Decision Support Tool-Subsystem (DSS) for geriatric depression scenario is presented. The decision support tool is based on the construction of a Fuzzy Cognitive Map (FCM) model that describes the geriatric depression scenario. First results are outlined to show the applicability of the proposed methodology. More specifically, a number of virtual senior cases (both healthy and with depressive symptoms) were built with the help of a neuropsychologist. Results of preliminary analysis are promising; however, further expansion of the model with more signs and several other pathologic conditions of seniors such as cognitive impairment and frailty shall be investigated.

Alzheimer's disease (AD) is the most common type of dementia, greatly affecting cognitive functioning and independent living of elderly population. The lack of an available drug therapy that could prevent disease progression shifted the research interest towards the early detection of the neurodegeneration symptoms that affect the mature brain and impair the interaction between brain regions, thus partially causing functional disconnection. The notion of electroencephalographic complexity is a valid and reliable method of quantifying the degree of isolation of brain regions due to AD pathology. Recently permutation entropy, which is a methodology of transforming the signal data into symbolic sequences and then computing the frequency distribution of symbolic patterns, gained great attention and was applied in seizure detection and computation of consciousness. The current study aims to investigate whether this complexity marker would be suitable to be applied in dementia research towards the quantification of the degree of cognitive deterioration due to disconnection of brain regions. The promising results indicate that permutation entropy on posterior regions (parieto-occipital areas) abnormally increases during mild dementia and is negatively correlated with the level of cognitive dysfunction, as estimated by the Mini Mental State Examination. Therefore, it may be a fast, accurate and simple tool for screening elderly population prone in Alzheimer.

Gender differences in mathematical thinking is a common concern of scientists from different research fields. Both parents and teachers report that males seem to perform better in complex mathematics compared to females. This study comes to shed light in the different organization of the underlying functional networks, in order to investigate the aforementioned observation, without supporting or rejecting this statement. In this sense, it is generally accepted that females use their both hemispheres to accomplish a certain task, while males use mostly the hemisphere which is properly suited. For the purposes of the current analysis, electroencephalographic recordings were collected from 11 males and 11 females, during a difficult mathematical task. Then a previously proposed model was used in order to pass from the sensor level to the cortical one, in order to examine the networks formed among the cortical dipoles. Mutual information was employed to form the graphs represeting the functional connectivity among the different dipoles, while the density, the global and the local efficiencies were further examined. The results suggest that females use their both hemisphere to solve the complex mathematical task while males use mostly their left hemisphere which is the responsible one for the mathematical thinking.

Semantic wikis have been widely adopted to support a variety of collaborative activities within the health domain [6], [7], [9]. In this paper, relevant existing tools that may be taken into account for the development of a Wiki-based tool are revisited. The paper then proposes a collaborative Wiki-based tool to be used for semantic management and classification of unstructured and semi-structured medical interventions [12] spread across the Web. The architecture of the tool and its functionality are described in the light of some evidence and a discussion on how this tool may become useful in the semantic Web description of elderly care interventions in the ageing society.

In the current explosion of internet technologies and exponentially increasing usage of the web there exists a need for effective searching and retrieval of information and resources. This is particularly true in medical and health care education. In the mEducator EUC funded project (www.mEducator.net), the collbaorators are attempting to develop technologies that will enable the above notion by means of multiple technological approaches, including one based on Web 2.0 principles and mashup technologies, and another based on web 3.0 and semantic (or linked) services. In this paper we view this problem through the recent notions of persuasive technologies and we explain how these are taken into account in the problem of metadata completion, a necessary action for linking resources in open educational repositories or learning management systems. The basic principles of persuasive technologies taken into account together with their application in the above field are explained and demonstrated with examples.

Social Software and particularly semantic wikis have been increasingly adopted by many online health-related professional and educational services. Because of their ease of use and rapidity of deployment, they offer the opportunity for powerful information sharing and ease of collaboration. Semantic wikis are Web sites that can be edited by anyone who has access to them. However, within medical intervention domain, certain important fundamental issues around development and evaluation have yet to be resolved. Thus, this paper proposes a Wikipedia-like Web-based tool to be used for describing and classifying medical interventions in order to plan and document patient care at a distance. Finally, this paper provides an overview of the ontology to be taken into account for the support of the Web-based tool.

This paper presents a semantic rule-based system for the composition of successful algorithmic pathways capable of solving medical computational problems (MCPs). A subset of medical algorithms referring to MCP solving concerns well-known medical problems and their computational algorithmic solutions. These solutions result from computations within mathematical models aiming to enhance healthcare quality via support for diagnosis and treatment automation, especially useful for educational purposes. Currently, there is a plethora of computational algorithms on the web, which pertain to MCPs and provide all computational facilities required to solve a medical problem. An inherent requirement for the successful construction of algorithmic pathways for managing real medical cases is the composition of a sequence of computational algorithms. The aim of this paper is to approach the composition of such pathways via the design of appropriate finite-state machines (FSMs), the use of ontologies, and SWRL semantic rules. The goal of semantic rules is to automatically associate different algorithms that are represented as different states of the FSM in order to result in a successful pathway. The rule-based approach is herein implemented on top of Knowledge-Based System for Intelligent Computational Search in Medicine (KnowBaSICS-M), an ontology-based system for MCP semantic management. Preliminary results have shown that the proposed system adequately produces algorithmic pathways in agreement with current international medical guidelines.

Educational content is often shared among different educators and is enriched, adapted, and, in general, repurposed so that it can be reused in different contexts. This paper discusses educational content and content repurposing in medical education, presenting different repurposing contexts. Finally, it proposes a novel approach to content repurposing via Web 2.0 social networking of learning resources. The proposed social network is augmented by a graphical representation module in order to capture and depict the relationships among different repurposed medical educational resources, based on educational resource "families" and inheritance. The ultimate goal is to provide a conceptually different approach to educational resource organization and retrieval via "social" associations among learning resources.

The 20 papers in this special issue were originally presented in the International Special Topic Conference on Information Technology in Biomedicine, held in October 2009, in Larnaka, Cyprus.

Digital resource sharing is a contemporary trend in Medical Education. State-of-the-art digital resources are incrementally appearing in the majority of curriculums worldwide. Institutional repositories have been initiated and metadata descriptions of digital resources have been turned into standards, however sharing, retrieval and re-use across different institutions are still at infant stage, due to the lack of appropriate tools and the cost of metadata creation. To this extend the aim of this paper is to propose a user-friendly editor for the creation of Healthcare Learning Object Metadata (H-LOM), an ANSI standard by MedBiquitous, to enhance the educational sharing and retrieval of digital health resources.

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.

This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels. The first classification level involves the arousal discrimination. The valence discrimination is then performed. The Mahalanobis (MD) distance-based classifier and support vector machines (SVMs) were used for the discrimination of emotions. The achieved overall classification rates were 79.5% and 81.3% for the MD and SVM, respectively, significantly higher than in previous studies. The robust classification of objective emotional measures is the first step toward numerous applications within the sphere of human-computer interaction.

Medical education requires a learning environment that enables medical students to acquire knowledge in a ldquohands onrdquo and organized way. This, in turn, requires that content can be accessed, evaluated, organized and reused with ease by the students. Social Software (i.e. Weblogs, Wikis, ePortfolios, Instant Messaging) and Semantic Web technology could play an important role in such learning environments. Where Social Software gives users freedom to choose their own processes and supports the collaboration of people anytime, anywhere, Semantic Web technology gives the possibility to structure information for easy retrieval, reuse, and exchange between different systems and tools. In this article a very specific technology that combines Social Software and the Semantic Web, that is Semantic Wikis are presented, together with their possible role in medical education Moreover the first Medical Semantic Wiki in Greek Language and its use in medical education are illustrated.

During the last two decades, the scene in education is rapidly changing by the deployment of Information and Communication Technologies (ICT). Smart classes, virtual classrooms, online collaborative educational experiences and emerging WEB 2.0 applications are increasingly used, either as stand alone or blended with conventional education. Additionally, as emphasis is shifting from dasiateachingpsila to dasialearningpsila, technologies that promote active, participative learning, such as computer based audience response systems, are employed in order to enhance students' participation and explore their degree of understanding. This paper presents our approach in combining online active learning and active learning in class in the case of medical education.

Education of people with special needs has recently been considered as a key element in the field of medical education. Recent development in the area of information and communication technologies may enable development of collaborative interactive environments which facilitate early stage education and provide specialists with robust tools indicating the person's autism spectrum disorder level. Towards the goal of establishing an enhanced learning environment for children with autism this paper attempts to provide a framework of a semi-controlled real-world environment used for the daily education of an autistic person according to the scenarios selected by the specialists. The proposed framework employs both real-world objects and virtual environments equipped with humanoids able to provide emotional feedback and to demonstrate empathy. Potential examples and usage scenarios for such environments are also described.

Emotion identification has recently been considered as a key element in contemporary studies for advanced human-computer interaction. The achievement of this goal is usually attempted via methods incorporating facial expression and speech recognition, as well as, human motion analysis. In this paper it is attempted to fuse multi-modal physiological signals of the autonomic (skin conductance) and central nervous systems (EEG), through the use of appropriate feature extraction procedures discriminating emotional arousal modulations, to a neural network classifier. Thus, skin conductivity responses, evoked-related potential peaks, and delta frequency oscillatory patterns are analyzed for a comparatively large number of subjects exposed to different emotions, evoked by pictures selected from the International Affective Picture System. The achieved neural network classifications were encouraging. It was found that fear was successfully differentiated (100%), pleasant emotions differing in their arousal level were well distinguished (80%), but the discrimination of low arousing negative feelings such as melancholy was more difficult (70%). It is argued that physiological patterning of multimodal recordings may successfully contribute to the enhancement of human computer interaction and emotion aware computing.

As technologies of information and communication are integrated incrementally with all facets of everyday life, it is reasonable to expect a penetration into educational procedures as well. This is also true for the case of medical/health informatics. In this paper, we describe our approach to facilitate the provision of online medical informatics modules with all those tools (Moodle) and standards (SCORM, HealthcareLOM) required, so as to allow for a proper electronic provision of our modules. The effort is made against the traditional and ineffective concept of simply converting the classical learning material into its corresponding digital form; it is rather attempted here to fully follow the whole educational process by trying to achieve the educational objectives and learning outcomes in parallel to creating and structuring the digital material. Web 2.0 technologies are also incorporated into this process.

During the last few decades, medical education is shifting is increasingly embracing active learning approaches. This shift from teaching to learning is also strongly related to an involvement of information and communication technology, and especially the Internet and the Web. The emergence of Internet 2.0 is indeed being stressed as a promising tool for advanced support of medicine and medical education. Although Web 2.0 emphasizes on participation, in its early days is still used in the majority of cases to hold and provide content (albeit created dynamically and via peer participation and collaboration) and then systematically deliver it to students. In this paper, we propose the use of wikis and blogs not just for creation and promotion of information, but as active tools to support problem based learning in medicine. In this approach, students and instructors use the web as a virtual place to collaborate and create new knowledge and new educational experiences.

Cervical cancer is the second most common cause of cancer deaths in women around the world. The primary underlying cause is infection with high risk type(s) of the human papillomavirus (HPV). In this paper, a framework of developing an e-learning platform with standardized educational material about prevention of uterine cervical cancer and management of precancerous lesions is provided. As technological evolutions during the last decades along with the appearance of international educational standards affect incrementally every facet of education, numerous medical courses will have to be implemented online, and in compliance with SCORM. This paper outlines the efforts undertaken within the Medical School of AUTH to provide such e-courses to GPs. The main conclusion drawn from the first pilot evaluations indicate that learners are capable and ready to use such e-learning technologies.

A reliability model for a health care domain based on requirement analysis at the early stage of design of regional health network (RHN) is introduced. RHNs are considered as systems supporting the services provided by health units, hospitals, and the regional authority. Reliability assessment in health care domain constitutes a field-of-quality assessment for RHN. A novel approach for predicting system reliability in the early stage of designing RHN systems is presented in this paper. The uppermost scope is to identify the critical processes of an RHN system prior to its implementation. In the methodology, Unified Modeling Language activity diagrams are used to identify megaprocesses at regional level and the customer behavior model graph (CBMG) to describe the states transitions of the processes. CBMG is annotated with: 1) the reliability of each component state and 2) the transition probabilities between states within the scope of the life cycle of the process. A stochastic reliability model (Markov model) is applied to predict the reliability of the business process as well as to identify the critical states and compare them with other processes to reveal the most critical ones. The ultimate benefit of the applied methodology is the design of more reliable components in an RHN system. The innovation of the approach of reliability modeling lies with the analysis of severity classes of failures and the application of stochastic modeling using discrete-time Markov chain in RHNs.

Continuing medical education tends to be considered compulsory in most countries although it is described by the Union of Euro pean Medical Specialists as an ethical duty. Accreditation of CME and allocation of credits is not yet mandatory in the EU but it is important for providers of educational activities to meet certain quality criteria so as to be prepared for future requirements. Many software platforms and learning management systems (LMS) can be used to support Web-based courses for online CME. Certain standards like SCORM, define the set of specifications that enables cross-system workflows. Updates and extensions of SCORM specifications enable further enhancements. The current research proposes a framework for the accreditation of online CME courses through the enhancement of the SCORM model and the use of metadata, using a SCORM-compliant open source LMS platform, namely, moodle. The model assumes the definition of learning objects and learning outcomes for a specific target audience of the course and the design of an instructional module incorporating several types of learning material.

Recently, a great interest has emerged in e-learning approaches for medical education. In particular, problem/case based learning constitutes a significant initiative in the domain. In this paper, we propose an ontology-based approach to constructing medical computational problems (MCPs) to be used in electronic medical education. Specifically, we elaborate on a novel ontological schema for MCP description that semantically annotates problems and their associated solutions (in terms of algorithms and implementations), linked with the IEEE metadata standard for learning objects (LOM) and its healthcare extension (Healthcare LOM). We present the overall procedure involved in the construction of the educational framework on MCPs, the involved actors, and the semantic schema of the corresponding knowledge model developed. The applicability and virtue of the proposed approach is illustrated via an example test case lesson on pulmonary embolism.

Communication, collaboration and exchange of information between health units via the internet have been thoroughly discussed and many web systems have been proposed. However, little work has been done at patients' transportation using web-based technologies. We propose a web-based system which provides an e-platform for patient transportation between health units in the same country as well as in different countries, taking into consideration the cross-country legal framework for patient transportation. To this extend, the system prototype is tested for the case of cross-border patient transfer between Greece and Bulgaria.

This paper introduces initial attempts towards an emotion specification method for physiological signals. The core elements of a complete emotion record are identified and their structure is represented. The XML annotation is proposed as the most appropriate since it provides platform independency and easier intercommunication. The emotion record is described in both quantitative statistical and qualitative analytical manners. The former is a standard way of representing signals by specifying their true numerical values. The second, though is additional description that is characteristic for each signal and describes the main features of the signal in a more free and informal annotation.

This commentary is a response to Interacting with Computers (Vol 14)—[Interacting Comput. 14 (2002) 119], [Interacting with Comput. 14 (2002) 141], [Interacting Comput. 14 (2002) 93]. Its aim is to discuss the role that neurophysiological measurements, such as EEG and MEG, may play in affective computing. The discussion is drawn upon the light of current experience and practice, as well as, advances envisaged in the fields of health informatics, telecommunications and biomedical engineering. It is explained why HCI research into interface evaluation and affective computing may be greatly enhanced by exploiting the underlying information of neurophysiological recordings.