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In recent years, there has been an interest in visual sports analytics, and especially in player and ball detection, action recognition, and camera pose estimation in various sports. The greatest interest is associated with football or soccer. The purpose of this work is the design and implementation of a low-cost methodology that extracts football analytics from simple TV broadcasts, using solely computer vision techniques. In the paper, we discuss the state of the art in this field and propose an integrated pipeline that solves the problem of player localization in the court and extracts useful insights. Technical details concerning the proposed methods that enhance track player–ball movement and camera pose, along with post-analytic results, are presented. The paper concludes with some future expansions of the proposed pipeline. [ABSTRACT FROM AUTHOR]

Detection of regions of interest (ROIs) in whole slide images (WSIs) in a clinical setting is a highly subjective and a labor-intensive task. In this work, recent developments in machine learning and computer vision algorithms are presented to assess their possible usage and performance to enhance and accelerate clinical pathology procedures, such as ROI detection in WSIs. In this context, a state-of-the-art deep learning framework (Detectron2) was trained on two cases linked to the TUPAC16 dataset for object detection and on the JPATHOL dataset for instance segmentation. The predictions were evaluated against competing models and further possible improvements are discussed. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion. [ABSTRACT FROM AUTHOR]

Explaining the predictions of neural networks to comprehend which region of an image influences the most its decision has become an imperative prerequisite when classifying medical images. In the case of convolutional neural networks, gradient-weighted class activation mapping is an explainability scheme that is more than often utilized for the unveiling of connections between stimuli and predictions especially in classification tasks that address the determination of the class between distinct objects in an image. However, certain categories of medical imaging such as confocal and histopathology images contain rich and dense information that differs from the cat versus dog paradigm. To further improve the performance of the gradient-weighted class activation mapping technique and the generated visualizations, we propose a segmentation-based explainability scheme that focuses on the common visual characteristics of each segment in an image to provide enhanced visualizations instead of highlighting rectangular regions. The explainability performance was quantified by applying random noise perturbations on microscopy images. The area over perturbation curve is utilized to demonstrate the improvement of the proposed methodology when utilizing the Slic superpixel algorithm against the Grad-CAM technique by an average of 4% for the confocal dataset and 9% for histopathology dataset. The results show that the generated visualizations are more comprehensible to humans than the initial heatmaps and demonstrate improved performance against the original Grad-CAM technique. [ABSTRACT FROM AUTHOR]

Human motion data captured from wearable devices such as smart watches can be utilized for activity recognition and emergency event detection, especially in the case of elderly or disabled people living independently in their homes. The output of such sensors is streams of physical activity data that require real-time recognition, especially in emergency situations. This paper presents a novel application that utilizes the low-cost Pebble Smart Watch together with an Android device (i.e. a smart phone) and allows the efficient capturing, transmission, storage and processing of such motion data. The paper includes the technical details of the stream data capturing and processing methodology, along with a comparison of the major algorithms used for the classification of physical activity type (i.e. Mild, Moderate, Intense and Sleep). An initial evaluation of the achieved accuracy in recognizing activity type, calculating the energy consumption and detecting falls, is also included and the corresponding results are discussed. The reported results are quite promising and can enable the development of intelligent systems, capable of analyzing human behavior and triggering alarms related to human activity in addition to fall detection. [ABSTRACT FROM AUTHOR]

The exchange of medical images over the Internet has evoked significant interest over the past few years due to the introduction of web and cloud based medical information systems. The protection of sensitive data has always been a key indicator in the performance of such systems. In this context, this work presents an algorithm developed for Digital Imaging and Communications in Medicine (DICOM) medical images, which applies secret-sharing steganography methods for ensuring the integrity of sensitive patient data as well as the important parts of the image. In the proposed algorithm, images are divided into two parts: the region of interest (ROI) and the region of non interest (RONI). Patient data and integrity hashes are positioned inside the ROI while the information (map) needed to recover the ROI before insertion is positioned in the RONI. Security of the extraction process is assured through the use of cryptography. The experimental results prove that the original (cover) images and the stego images provide an excellent visual equality result in terms of PSNR. Furthermore, they prove that the proposed scheme can be efficiently used as a steganography scheme in DICOM images with limited smooth areas. [ABSTRACT FROM AUTHOR]

The paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images. The proposed tool utilizes adaptive thresholding and a Support Vector Machines classifier. The segmentation results are enhanced through a Majority Voting and a Watershed technique, while an object labeling algorithm has been developed for the fast and accurate validation of the recognized cells. Expert pathologists evaluated the tool and the reported results are satisfying and reproducible. [ABSTRACT FROM AUTHOR]

The interest in image dermoscopy has been significantly increased recently and skin lesion images are nowadays routinely acquired for a number of skin disorders. An important finding in the assessment of a skin lesion severity is the existence of dark dots and globules, which are hard to locate and count using existing image software tools. In this work we present a novel methodology for detecting/segmenting and count dark dots and globules from dermoscopy images. Segmentation is performed using a multi-resolution approach based on inverse non-linear diffusion. Subsequently, a number of features are extracted from the segmented dots/globules and their diagnostic value in automatic classification of dermoscopy images of skin lesions into melanoma and non-malignant nevus is evaluated. The proposed algorithm is applied to a number of images with skin lesions with known histo-pathology. Results show that the proposed algorithm is very effective in automatically segmenting dark dots and globules. Furthermore, it was found that the features extracted from the segmented dots/globules can enhance the performance of classification algorithms that discriminate between malignant and benign skin lesions, when they are combined with other region-based descriptors. [ABSTRACT FROM AUTHOR]

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising. [ABSTRACT FROM AUTHOR]

The ever-increasing use of ontologies in modern biological analysis and interpretation facilitates the understanding of the cellular procedures, their hierarchical organization, and their potential interactions at a system's level. Currently, the gene ontology serves as a paradigm, where through the annotation of whole genomes of certain organisms, genes subsets selected, either from high-throughput experiments or with an established pivotal role regarding the probed disease, can act as a starting point for the exploration of their underlying functional interconnections. This may also aid the elucidation of hidden regulatory mechanisms among genes. Reverse engineering the functional relevance of genes to specific cellular pathways and vice versa, through the exploitation of the inner structure of the ontological vocabularies, may help impart insight regarding the identification and prioritization of the critical role of specific genes. The proposed graph-theoretical method is showcased in a pancreatic cancer and a T-cell acute lymphoblastic leukemia gene set, incorporating edge and Resnik semantic similarity metrics, and systematically evaluated regarding its performance. [ABSTRACT FROM AUTHOR]

The monitoring of human physiological data, in both normal and abnormal situations of activity, is interesting for the purpose of emergency event detection, especially in the case of elderly people living on their own. Several techniques have been proposed for identifying such distress situations using either motion, audio or video data from the monitored subject and the surrounding environment. This paper aims to present an integrated patient fall detection system that may be used for patient activity recognition and emergency treatment. Visual data captured from the user's environment, using overhead cameras among with motion and physiological data collected from the subject's body are utilized. Appropriate tracking techniques are applied to the aforementioned visual perceptual component enabling the trajectory tracking of the subjects, while acceleration data from the sensors can indicate a fall incident. Trajectory information and subject's visual location can verify fall and indicate an emergency event, whereas the interpretation of biosignals like electrocardiogram (ECG) and blood oxygen saturation (SPO2) can indicate the severity of the incident with the help of rules-based evaluation. The paper includes also the assessment of several classifiers and meta-classifiers in terms of accuracy in detecting falls and a user based evaluation. [ABSTRACT FROM AUTHOR]

Abstract: Taking into account that heart auscultation remains the dominant method for heart examination in the small health centers of the rural areas and generally in primary healthcare set-ups, the enhancement of this technique would aid significantly in the diagnosis of heart diseases. In this context, the present paper initially surveys the research that has been conducted concerning the exploitation of heart sound signals for automated and semi-automated detection of pathological heart conditions. Then it proposes an automated diagnosis system for the identification of heart valve diseases based on the Support Vector Machines (SVM) classification of heart sounds. This system performs a highly difficult diagnostic task (even for experienced physicians), much more difficult than the basic diagnosis of the existence or not of a heart valve disease (i.e. the classification of a heart sound as ‘healthy’ or ‘having a heart valve disease’): it identifies the particular heart valve disease. The system was applied in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases suffering from the four most usual heart valve diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and mitral regurgitation (MR). Initially the heart sounds were successfully categorized using a SVM classifier as normal or disease-related and then the corresponding murmurs in the unhealthy cases were classified as systolic or diastolic. For the heart sounds diagnosed as having systolic murmur we used a SVM classifier for performing a more detailed classification of them as having aortic stenosis or mitral regurgitation. Similarly for the heart sounds diagnosed as having diastolic murmur we used a SVM classifier for classifying them as having aortic regurgitation or mitral stenosis. Alternative classifiers have been applied to the same data for comparison (i.e. back-propagation neural networks, k-nearest-neighbour and naïve Bayes classifiers), however their performance for the same diagnostic problems was lower than the SVM classifiers proposed in this work. [Copyright &y& Elsevier]

This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation and skin detection. A set of several colored images with human faces have been considered as the training set. A second module is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring the gradient of eyes’ and mouth’s region figure. The paper provides results from the system application, along with proposals for further research. [ABSTRACT FROM AUTHOR]

Two-way satellite broadband communication technologies, such as the Digital Video Broadcasting with Return Channel via Satellite (DVB-RCS) technology, endeavour to offer attractive wide-area broadband connectivity for telemedicine applications, taking into consideration the available data rates, Quality of Service (QoS) provision, survivability, flexibility and operational costs, even in remote areas and isolated regions where the terrestrial technologies suffer. This paper describes a wide-area tele-medicine platform, specially suited for homecare services, based on the DVB-RCS and Wi-Fi communication technologies. The presented platform combines medical data acquisition and transfer, patient remote monitoring and teleconference services. Possible operational scenarios concerning this platform and experimental results regarding tele-monitoring, videoconference and medical data transfer are also provided and discussed in the paper. [ABSTRACT FROM AUTHOR]

Nowadays, web search services are considered as a valuable tool for information exploration. However, due to the extremely rapid growth of the web, search engines cannot index the new information at the same time or with the same priority and thus their performance is hampered in some extend. Besides, web search services index the disseminated information with different algorithms, having as result different response time in updating their directories. In this paper, we propose a meta-search algorithm, which is capable of self-adapting over the continuous changes that occur on the indexed web, using a web evolution adaptation mechanism. This mechanism is put in the context of real capture–recapture experiments conducted in wildlife biological studies. The meta-search algorithm also supports a monitor mechanism, which records the user's browsing behavior during his search sessions. The paper provides the implementation details of the proposed meta-search ranking algorithm along with its initial assessment over health-related information using three medical web search services. We prove in our approach, that when user's browsing behavior is jointly used with a dynamic survey mechanism, which scores the ability of each search service to adapt in the incessant evolution of the web, a more effective meta-search is provided. Experimental results showed that the precision of the third-party results, were increased in several recall levels, for a tested period between September of 2006 and April of 2007. [ABSTRACT FROM AUTHOR]

This paper deals with breast cancer diagnostic and prognostic estimations employing neural networks over the Wisconsin Breast Cancer datasets, which consist of measurements taken from breast cancer microscopic instances. A probabilistic approach is dedicated to solve the diagnosis problem, detecting malignancy among instances derived from the Fine Needle Aspirate test, while regression algorithms estimate the time interval that possibly correspond to the right end-point of the patients' disease-free survival time or the time where the tumour recurs (time-to-recur). For the diagnosis problem, the accuracy of the neural network in terms of sensitivity and specificity was measured at 98.6 and 97.5% respectively, using the leave-one-out test method. As far as the prognosis problem is concerned, the accuracy of the neural network was measured through a stratified tenfold cross-validation approach. Sensitivity ranged between 80.5 and 91.8%, while specificity ranged between 91.9 and 97.9%, depending on the tested fold and the partition of the predicted period. The prognostic recurrence predictions were then further evaluated using survival analysis and compared with other techniques found in literature. [ABSTRACT FROM AUTHOR]

Recent developments in networking and computing technologies and the expansion of the electronic health record system have enabled the possibility of online collaboration between geographically distributed medical personnel. In this context, the paper presents a Web-based application, which implements a collaborative working environment for physicians by enabling the peer-to-peer exchange of electronic health records. The paper treats technological issues such as Video, Audio and Message Communication, Workspace Management, Distributed Medical Data Management and exchange, while it emphasizes on the Security issues arisen, due to the sensitive and private nature of the medical information. In the paper, we present initial results from the system in practice and measurements regarding transmission times and bandwidth requirements. A wavelet based image compression scheme is also introduced for reducing network delays. A number of physicians were asked to use the platform for testing purposes and for measuring user acceptance. The system was considered by them to be very useful, as they found that the platform simulated very well the personal contact between them and their colleagues during medical meetings. [ABSTRACT FROM AUTHOR]

The paper studies the factors influencing the consistent acquisition and recognition of object's color and border features in digital imaging. The proposed image acquisition process is utilized by a computer supported imaging system implementing the acquisition and analysis of skin lesion images supporting medical diagnosis. In addition the same approach may be used for several problems requiting reliable color measurement and object identification. Two methodologies are adopted: The Bayesian Networks, which provide an efficient way of reasoning under uncertainty and are used to incorporate the expert judgement into the estimation of the probability of successful operation, and a Markov chain approach, which is generally used for the dynamic modeling of the system behavior. The Markov chain model requires asymptotically the solution of sparse linear systems. Explicit preconditioned methods are used for the efficient solution of the derived sparse linear system, and the parallel implementation of the dominant computational part is exploited. [ABSTRACT FROM AUTHOR]

The paper presents a computer-based imaging system aiming to support telemedicine examination sessions in dermatology. Many studies have proved the inadequacy of general practitioners to diagnose successfully common dermatological diseases; some of them may prove fatal if not diagnosed at their early stages (e.g., melanoma). Thus the need for telemedicine systems customized for dermatology becomes obvious for distant rural areas, where dermatological care is usually provided by general doctors. We treat technological issues such as image acquisition, camera calibration, illumination, data transmission, and data compression, and propose a store and forward architecture for image transmission. We also include a study of the effect that image compression quality factor has in the diagnostic value of the skin digital images, along with some initial results and conclusions from the pilot use of the system. [ABSTRACT FROM AUTHOR]

Background: In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study. Methods: The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared. Results: The SVM (Support Vector Machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same. Conclusion: The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis. [ABSTRACT FROM AUTHOR]

A major issue concerning the design and implementation of an image acquisition system for skin lesions is its ability to capture reproducible images. The reproducibility is considered essential for image analysis and for the comparison of sequential images during follow-up studies. This paper describes a prototype image acquisition system that includes a standardized illumination and capturing geometry with polarizing filters and a series of software corrections: Calibration to Black, White and Color for color constancy, Internal camera Parameters adjustment and Pose extraction for stereo vision, Shading correction and Noise Filtering for color quality. The validity of the calibration procedure and the images' reproducibility were tested by capturing sample images in three different lighting conditions: dark, medium and intense lighting. For each case the average values of the three color planes RGB and their standard deviations were calculated; the measured error differences ranged between 0.7 and 12.9 (in the 0–255 scale). Preliminary experiments for stereo measurements provided repeatability of about 0.3~mm. The above results demonstrate the reproducibility of the captured images at a satisfactory level. The developed prototype was also evaluated clinically, for its ability to support the construction of knowledge-based decision systems and for telemedicine, thus to support telemedical sessions in dermatology. [ABSTRACT FROM AUTHOR]

Clinical characteristics of COVID-19 patients have been mostly described in hospitalised patients, yet most are managed in an outpatient setting. The COVID-19 pandemic transformed healthcare delivery models and accelerated the implementation and adoption of telemedicine solutions. We employed a modular remote monitoring system with multi-modal data collection, aggregation, and analytics features to monitor mild COVID-19 patients and report their characteristics and symptoms. At enrolment, the patients were equipped with wearables, which were associated with their accounts, provided the respective in-system consents, and, in parallel, reported the demographics and patient characteristics. The patients monitored their vitals and symptoms daily during a 14-day monitoring period. Vital signs were entered either manually or automatically through wearables. We enrolled 162 patients from February to May 2022. The median age was 51 (42–60) years; 44% were male, 22% had at least one comorbidity, and 73.5% were fully vaccinated. The vitals of the patients were within normal range throughout the monitoring period. Thirteen patients were asymptomatic, while the rest had at least one symptom for a median of 11 (7–16) days. Fatigue was the most common symptom, followed by fever and cough. Loss of taste and smell was the longest-lasting symptom. Age positively correlated with the duration of fatigue, anorexia, and low-grade fever. Comorbidities, the number of administered doses, the days since the last dose, and the days since the positive test did not seem to affect the number of sick days or symptomatology. The i-COVID platform allowed us to provide remote monitoring and reporting of COVID-19 outpatients. We were able to report their clinical characteristics while simultaneously helping reduce the spread of the virus through hospitals by minimising hospital visits. The monitoring platform also offered advanced knowledge extraction and analytic capabilities to detect health condition deterioration and automatically trigger personalised support workflows. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) and Internet of Things (IoT) are among the core technological elements of modern information systems and applications in which advanced features for user interactivity and monitoring are required. These technologies are continuously improving and are available nowadays in all popular programming environments and platforms, allowing for their wide adoption in many different business and research applications. In the fields of healthcare and assisted living, AR is extensively applied in the development of exergames, facilitating the implementation of innovative gamification techniques, while IoT can effectively support the users' health monitoring aspects. In this work, we present a prototype platform for exergames that combines AR and IoT on commodity mobile devices for the development of serious games in the healthcare domain. The main objective of the solution was to promote the utilization of gamification techniques to boost the users' physical activities and to assist the regular assessment of their health and cognitive statuses through challenges and quests in the virtual and real world. With the integration of sensors and wearable devices by design, the platform has the capability of real-time monitoring the users' biosignals and activities during the game, collecting data for each session, which can be analyzed afterwards by healthcare professionals. The solution was validated in real world scenarios and the results were analyzed in order to further improve the performance and usability of the prototype. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various articles on assistive environments which include empowering daily life of old persons with use of technology, use of pervasive technologies for assistive environments, and monitoring of sleep patterns.

Evaluating the interpretability of Deep Learning models is crucial for building trust and gaining insights into their decision-making processes. In this work, we employ class activation map based attribution methods in a setting where only High-Resolution Class Activation Mapping (HiResCAM) is known to produce faithful explanations. The objective is to evaluate the quality of the attribution maps using quantitative metrics and investigate whether faithfulness aligns with the metrics results. We fine-tune pre-trained deep learning architectures over four medical image datasets in order to calculate attribution maps. The maps are evaluated on a threefold metrics basis utilizing well-established evaluation scores. Our experimental findings suggest that the Area Over Perturbation Curve (AOPC) and Max-Sensitivity scores favor the HiResCAM maps. On the other hand, the Heatmap Assisted Accuracy Score (HAAS) does not provide insights to our comparison as it evaluates almost all maps as inaccurate. To this purpose we further compare our calculated values against values obtained over a diverse group of models which are trained on non-medical benchmark datasets, to eventually achieve more responsive results. This study develops a series of experiments to discuss the connection between faithfulness and quantitative metrics over medical attribution maps. HiResCAM preserves the gradient effect on a pixel level ultimately producing high-resolution, informative and resilient mappings. In turn, this is depicted in the results of AOPC and Max-Sensitivity metrics, successfully identifying the faithful algorithm. In regards to HAAS, our experiments yield that it is sensitive over complex medical patterns, commonly characterized by strong color dependency and multiple attention areas. • A framework for evaluating interpretability methods, under faithfulness, is proposed. • The proposed algorithm is applied to four medical imaging datasets. • Area over Perturbation Curve and Max-Sensitivity favor the faithful method • Heatmap Assisted Accuracy Score fails to identify the faithful method • Heatmap Assisted Accuracy Score is sensitive over complex patterns characterizing medical images. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Bamis and colleagues on the scalable framework for the interpretation of elder behavior, one by Li and colleagues on the use of wireless camera-based surveillance system for cognitive impairment patient monitoring, and one by Koufi and colleagues on the MASPortal used for assisting people that need medical advice in their homes.

This work describes a novel methodology for creating exergames on an edge-native platform with the integration of multiple deep neural networks. A prototype of the platform, which includes capabilities for innovative gameplay and advanced user interactivity, has been implemented and deployed in a real-world scenario. At core of the proposed methodology is the ad hoc training of classifiers for posture classification which can be dynamically adapted to the specific requirements of the usage scenario, operational and environmental conditions allowing for real-time identification of events and advanced game control. The proposed solution is ideal for individual consumers in a home environment since is supports by-design edge platforms minimizing the cost of the system and enabling in parallel the communication with state-of-the-art hardware (i.e., GPUs, TPUs, computer boards) for real-time operation. The proposed system allows the collection and analysis of game data, which can be exploited by specialized personnel in rehabilitation centers or for other purposes in the areas of healthcare and assisted living. [ABSTRACT FROM AUTHOR]

Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses. [ABSTRACT FROM AUTHOR]

In this work we propose a novel algorithm for foreground segmentation in video sequences that achieves better accuracy, while maintaining low complexity and allows real time execution. The proposed algorithm combines selected features from a number of well-established and state of the art algorithms, such as the Gaussian mixture models, the Self Organizing Maps and the illumination sensitive method. The presented methodology is capable of efficiently handling sudden light changes, both from natural and multiple artificial light sources, which may have caused erroneous segmentation for other algorithms. Comparative results are presented utilizing user-defined ground truth segmentation for two different types of indoor video sequences, one of which was obtained by a hemispheric omnidirectional dioptric (fish-eye) camera, with and without illumination changes and the second by a plain camera. The behavior of the algorithm with respect to its controlling parameters is investigated and its computational burden is studied. [ABSTRACT FROM AUTHOR]

A core problem in robotics is the determination of the location and pose of a mobile robot in its environment. The localization is a basic operation, which must be successfully carried out in complex environments using imprecise and/or contaminated data and is essential for a broad range of mobile robot tasks, since the robot behavior depends on its position. In this work, we propose the use of a stationary fisheye camera for real time robot localization in indoor environments. We employ a model for the formation of the image by the fisheye camera, which can be used for accelerating the segmentation of the robot's top surface, as well as for calculating the robot's true position in the real world frame of reference. The proposed algorithm for robot localization exploits the calibrated fisheye camera model and the known dimensions of the robot, whereas it does not depend on any information from the robot's sensors and does not require visual landmarks in the indoor environment. Furthermore, the pose (orientation) of the robot is determined using a triangular shape placed on top of the robot's flat top surface, using Hu's moment invariants, appropriately modified using the calibrated fisheye camera model. Initial results are presented from video sequences and are compared to the ground truth position, obtained by the robot's sensors. The dependence of the average positional error with the distance from the camera is also measured. [ABSTRACT FROM AUTHOR]

The extended utilization of digitized Whole Slide Images is transforming the workflow of traditional clinical histopathology to the digital era. The ongoing transformation has demonstrated major potentials towards the exploitation of Machine Learning and Deep Learning techniques as assistive tools for specialized medical personnel. While the performance of the implemented algorithms is continually boosted by the mass production of generated Whole Slide Images and the development of state-of the-art deep convolutional architectures, ensemble models provide an additional methodology towards the improvement of the prediction accuracy. Despite the earlier belief related to deep convolutional networks being treated as black boxes, important steps for the interpretation of such predictive models have also been proposed recently. However, this trend is not fully unveiled for the ensemble models. The paper investigates the application of an explanation scheme for ensemble classifiers, while providing satisfactory classification results of histopathology breast and colon cancer images in terms of accuracy. The results can be interpreted by the hidden layers' activation of the included subnetworks and provide more accurate results than single network implementations. [ABSTRACT FROM AUTHOR]

The present paper studies the prospective and the performance of a forthcoming high-speed third generation (3.5G) networking technology, called enhanced uplink, for delivering mobile health (m-health) applications. The performance of 3.5G networks is a critical factor for successful development of m-health services perceived by end users. In this paper, we propose a methodology for performance assessment based on the joint uplink transmission of voice, real-time video, biological data (such as electrocardiogram, vital signals, and heart sounds), and healthcare records file transfer. Various scenarios were concerned in terms of real-time, nonreal-time, and emergency applications in random locations, where no other system but 3.5G is available. The accomplishment of quality of service (QoS) was explored through a step-by-step improvement of enhanced uplink system's parameters, attributing the network system for the best performance in the context of the desired m-health services. [ABSTRACT FROM AUTHOR]

Accurate fall detection is a crucial research challenge since the time delay from fall to first aid is a key factor that determines the consequences of a fall. Wearable sensors allow a reliable way for motion tracking, allowing immediate detection of high-risk falls via a machine learning framework. Toward this direction, accelerometer devices are widely used for the assessment of fall risk. Although there exist a plethora of studies under this perspective, several challenges still remain, such as dealing simultaneously with extremely demanding data management, power consumption and prediction accuracy. In this work, we propose a complete methodology based on the cooperation of deep learning for signal classification along with a lightweight control chart method for change detection. Our basic assumption is that it is possible to control computational resources by selectively allowing the operation of a relatively heavyweight, but very efficient classifier, when it is truly required. The proposed methodology was applied to real experimental data providing the reliable results that justify the original hypothesis. [ABSTRACT FROM AUTHOR]

Melanoma is a lethal type of skin cancer, unless it is diagnosed early. Formalin-fixed, paraffin-embedded (FFPE) tissue is a valuable source for molecular assays after diagnostic examination, but isolated nucleic acids often suffer from degradation. Here, for the first time, we examine primary melanomas from Greek patients, using whole exome sequencing, so as to derive their mutational profile. Application of a bioinformatic framework revealed a total of 10,030 somatic mutations. Regarding the genes containing putative protein-altering mutations, 73 were common in at least three patients. Sixty-five of these 73 top common genes have been previously identified in melanoma cases. Biological processes related to melanoma were affected by varied genes in each patient, suggesting differences in the components of a pathway possibly contributing to pathogenesis. We performed a multi-level analysis highlighting a short list of candidate genes with a probable causative role in melanoma. [ABSTRACT FROM AUTHOR]

Driven by technological advances in low-power network systems and medical sensors, we have witnessed during the recent years the adoption of wireless sensor networks (WSNs) in electronic healthcare. Improving the quality of electronic healthcare and the prospects of 'ageing in place' through WSNs requires solving difficult problems in scale, energy management, and data acquisition. Medical and pervasive healthcare application (or mobile healthcare application) based on WSNs is influenced by many factors such as transmission errors and power consumption. We propose a multivariate context forwarding model that achieves energy-efficient WSN operation. A node adopts multivariate autoregression for forecasting contextual information (bio-signals or vital parameters) and locally decides whether context retransmission is required or not. This scheme is applied in patient telemonitoring systems where accurate yet energy-aware transmission of bio-signals to a remote control unit is crucial. Simulation results are reported indicating the capability of the proposed model in minimizing energy consumption in WSNs having as application domain the electronic healthcare systems. [ABSTRACT FROM AUTHOR]

The aim of this paper is, in accordance with efficient web search service operation objectives, to propose enhancements to the sophistication of the functionality that can be offered by search engine services. A meta-search third-party result ranking mechanism is proposed, which is capable of adapting over the continuous changes that occur on the web, providing in parallel personalized information acquisition considering the user's navigation behaviour. Transparency is achieved for both personalization and web evolution adaptation mechanisms, requiring virtually none effort from the user's part. In essence, the proposed meta search engine rates, re-organises and combines the results acquired from search services for a specific user information resource request in accordance with a weighted combination of a performance related factor (tightly related to the ranking of the web result as given by the search engine service) and a reliability related factor (corresponding to the user satisfaction stemming from the specific web result that he/she browses), while the performance of each search engine with respect to adequately adapting to the web evolution is taken into account. For the evaluation of the web results reliability, a collaborative reputation mechanism is utilized, which helps estimating their quality and predicting their future usability, taking into account their past performance in consistently satisfying user expectations. A set of results indicative of the efficiency of our proposed scheme is provided. The Internet search services used were Google, MSN and Yahoo! [ABSTRACT FROM AUTHOR]

The article focuses on data rate standards for Wireless Local Area Networks. The initial Institute of Electrical and Electronics Engineers Inc. (IEEE) 802.11 standard specifies data rates for three different physical layers based on direct sequence spread spectrum (DSSS), frequency hopping spread spectrum, and infrared techniques. The convergence of the IEEE 802.11a and IEEE 802.11b standards came with the publication of the IEEE 802.11g standard. The latter provides the data rates of IEEE 802.11a at the 2.4 giga hertz band, thus allowing interoperability with older IEEE 802.11 and IEEE 802.11b devices. While IEEE 802.11b uses only DSSS technology, IEEE 802.11g uses DSSS, orthogonal frequency division multiplexing (OFDM), or both at the 2.4 giga hertz band to provide high data rates. Combined use of both DSSS and OFDM is achieved through the provision of four different physical layers. These layers, defined in the standard as extended rate physicals, coexist during a frame exchange, so the sender and receiver have the option to select and use one of the four layers as long as they both support it.

In this paper, we describe the main outcomes of AGILE (acronym for "Adaptive Gateways for dIverse muLtiple Environments"), an EU-funded project that recently delivered a modular hardware and software framework conceived to address the fragmented market of embedded, multi-service, adaptive gateways for the Internet of Things (IoT). Its main goal is to provide a low-cost solution capable of supporting proof-of-concept implementations and rapid prototyping methodologies for both consumer and industrial IoT markets. AGILE allows developers to implement and deliver a complete (software and hardware) IoT solution for managing non-IP IoT devices through a multi-service gateway. Moreover, it simplifies the access of startups to the IoT market, not only providing an efficient and cost-effective solution for industries but also allowing end-users to customize and extend it according to their specific requirements. This flexibility is the result of the joint experience of established organizations in the project consortium already promoting the principles of openness, both at the software and hardware levels. We illustrate how the AGILE framework can provide a cost-effective yet solid and highly customizable, technological foundation supporting the configuration, deployment, and assessment of two distinct showcases, namely a quantified self application for individual consumers, and an air pollution monitoring station for industrial settings. [ABSTRACT FROM AUTHOR]

Current healthcare services promise improved life-quality and care. Nevertheless, most of these entities operate independently due to the ingested data' diversity, volume, and distribution, maximizing the challenge of data processing and exchange. Multi-site clinical healthcare organizations today, request for healthcare data to be transformed into a common format and through standardized terminologies to enable data exchange. Consequently, interoperability constraints highlight the need of a holistic solution, as current techniques are tailored to specific scenarios, without meeting the corresponding standards' requirements. This manuscript focuses on a data transformation mechanism that can take full advantage of a data intensive environment without losing the realistic complexity of health, confronting the challenges of heterogeneous data. The developed mechanism involves running ontology alignment and transformation operations in healthcare datasets, stored into a triple-based data store, and restructuring it according to specified criteria, discovering the correspondence and possible transformations between the ingested data and specific Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) through semantic and ontology alignment techniques. The evaluation of this mechanism results into the fact that it should be used in scenarios where real-time healthcare data streams emerge, and thus their exploitation is critical in real-time, since it performs better and more efficient in comparison with a different data transformation mechanism. [ABSTRACT FROM AUTHOR]