Integrating IoT Wearable Devices in Telemonitoring Platforms for Continuous Assisted Living Services

Assisted living services have become increasingly important in recent years as the population ages and the demand for personalized care rises. In this paper, we present the integration of wearable IoT devices in a remote monitoring platform for elderly people that enables seamless data collection, analysis, and visualization while in parallel, alarms and notification functionalities are provided in the context of a personalized monitoring and care plan. The system has been implemented using state-of-the-art technologies and methods to facilitate robust operation, increased usability and real-time communication. The user has the ability to record and visualise their activity, health and alarm data using the tracking devices, and additionally settle an ecosystem of relatives and informal carers to provide assistance daily or support in cases of emergencies.

Enriching Remote Monitoring and Care Platforms with Personalized Recommendations to Enhance Gamification and Coaching

Patients' remote monitoring platforms can be enhanced with intelligent recommendations and gamification functionalities to support their adherence to care plans. The current paper aims to present a methodology for creating personalized recommendations, which can be used to improve patient remote monitoring and care platforms. The current pilot system design is aimed to support patients by providing recommendations for Sleep, Physical Activity, BMI, Blood sugar, Mental Health, Heart Health, and Chronic Obstructive Pulmonary Disease aspects. The users, through the application, can select the types of recommendations they are interested in. Thus, personalized recommendations based on data obtained by the patients' records anticipated to be a valuable and a safe approach for patient coaching. The paper discusses the main technical details and provides some initial results.

MedSecurance Project: Advanced Security-for-Safety Assurance for Medical Device IoT (IoMT)

The MedSecurance project focus on identifying new challenges in cyber security with focus on hardware and software medical devices in the context of emerging healthcare architectures. In addition, the project will review best practice and identify gaps in the guidance, particularly the guidance stipulated by the medical device regulation and directives. Finally, the project will develop comprehensive methodology and tooling for the engineering of trustworthy networks of inter-operating medical devices, that shall have security-for-safety by design, with a strategy for device certification and certifiable dynamic network composition, ensuring that patient safety is safeguarded from malicious cyber actors and technology "accidents".

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

Introduction

Monitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders.

Methods

We continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-min measures of total motor activity from an accelerometer (TMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, walking activity (WA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly.

Results

Our results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from demographic and clinical variables as well as changes in antipsychotic medication dose.

Discussion

Our findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

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.

Image Analysis in Digital Pathology Utilizing Machine Learning and Deep Neural Networks

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.

Exploiting Augmented Reality and Computer Vision for Healthcare Education: The Case of Pharmaceutical Substances Visualization and Information Retrieval

Augmented Reality (AR) is already used as the primary visualization and user interaction tool in several scientific and business areas. At the same time new AR technologies and frameworks considerably facilitate both the development of innovative applications and also their wide adoption in different domains of everyday life. In the area of healthcare AR solutions make use of mobile or wearable devices and glasses to support, among others, education and healthcare professionals training. The aim of this paper is to present a prototype mHealth app for education, which uses AR and computer vision technologies for pharmaceutical substances recognition on drug packaging. The conceptual design of the system includes three main components which are responsible for a) Text recognition, b) Drug identification and c) AR operations for interactivity. The prototype application is available in Android or iOS platforms and has been evaluated in real-world scenarios. Camera and screen of the mobile phones fulfill the text recognition and AR operations, which eliminates the need for special equipment, while PubChem and 3D Model databases provide assets required for the drug identification and AR visualizations. The results highlight the value of AR for educational purposes, especially when combined with advanced image recognition technologies to build interactive AR encyclopedias.

Gameful Design of an Application for Patients in Rehabilitation

The design process of any interactive application is an important part of its lifecycle, since it largely defines its structure, means of interaction with the users and its actual content. In the case of applications related to medical uses and self-help, it is even more important, given the aims of the application, the diversity of target users and the urgent need for increased retention. In this article, we present a gameful design process for a mobile application targeted toward patients in rehabilitation, implementing concepts related to increasing user rapport and motivation through gamification, and means to offer guidance and personalized services to improve user experience. Both gamification and personalization build on narrative concepts, by putting patients in the place of a “hero”, offering them the opportunity to overcome “challenges” and receive a clear view of their progress (a.k.a. a “hero's journey”), both in terms of physical and mental condition. Finally, we discuss measurable indicators used to evaluate the application in terms of the progress that patients showed, their motivation and interest, and degree of adherence to the exercise plans.

Intelligent Pervasive Monitoring Solution of COVID-19 Patients

The COVID-19 pandemic transforms the healthcare delivery models and accelerates the implementation and the adoption of telemedicine solutions at all levels of the healthcare system. Telehealth services ensure the continuity of care and treatment of both inpatients and outpatients during this pandemic, while reducing the spread of the virus through hospitals. The aim of this paper is to present an intelligent remote monitoring system with innovative data analytics features for COVID-19 patients. The i-COVID platform provides remote COVID-19 patients monitoring. The presented solution is addressed to patients with mild COVID-19 symptoms, as well as it can be used for post intensive-care monitoring. The platform offers advanced analytic capabilities using Proactive AI, to detect health condition deterioration, and automatically trigger personalized support workflows. Remote monitoring of COVID-19 patients using bio-sensors, seems to be an effective tool against the COVID-19 pandemic, as reduces the number of visits to patient screening centres and hospital admissions.

Using mHealth Technologies to Promote Public Health and Well-Being in Urban Areas with Blue-Green Solutions

European and International cities face crucial global geopolitical, economic, environmental, and other changes. All these intensify threats to and inequalities in citizens' health. The implementation of Blue-Green Solutions in urban and rural areas have been broadly used to tackle the above challenges. The Mobile health (mHealth) technologies contribution in people's well-being has found to be significant. In addition, several mHealth applications have been used to support patients with mental health or cardiovascular diseases with very promising results. The patients' remote monitoring can be a valuable asset in chronic diseases management for patients suffering from diabetes, hypertension or arrhythmia, depression, asthma, allergies and others. The scope of this paper is to present the specifications, the design and the development of a mobile application which collects health-related and location data of users visiting areas with Blue-Green Solutions. The mobile application has been developed to record the citizens' and patients' physical activity and vital signs using wearable devices. The proposed application can also monitor patients physical, physiological, and emotional status as well as motivate them to engage in social and self-caring activities. Additional features include the analysis of the patients' behavior to improve self-management. The "HEART by BioAsssist" application could be used as a health and other data collection tool as well as an "intelligent assistant" to monitor and promote patient's physical activity.

Gamification and Coaching in Remote Monitoring and Care Platforms

Nowadays, several e-health systems are equipped with advanced features for patients monitoring and care. Among these features, gamification and operations supporting the patients' adherence to therapeutic and care plans have been found to be quite useful and valuable. Among others, the introduction of intelligent patient coaching and the provisions of recommendations are very popular. The aim of this paper is to present specific gamification and coaching approaches that could be employed in the context of an existing eHealth system for remote monitoring and care for elders. The "Points, Badges and Leaderboards" gamification approach was followed. Specifically, parameters related to the application usage (daily points), the physical activity (number of daily steps), the sleep quality (sleep score) and other measurements (i.e. weight) were utilized to accommodate elders needs for motivation and engagement. Regarding the coaching, motivational messages and notification for the mobile devices were selected to deliver the relative information to the elders. A prototype health information system with a corresponding mobile application was adapted to include gamification and coaching features to motivate elders in order to achieve the maximum adherence on their monitoring and care health plans. The paper presents the design issues and summarizes the technical details.

Pervasive Monitoring of Public Health and Well-Being in Urban Areas with Blue-Green Solutions

The urban environment seems to affect the citizens' health. The implementation of Blue-Green Solutions (BGS) in urban areas have been used to promote public health and citizens well-being. The aim of this paper is to present the development of an mHealth app for monitoring patients and citizens health status in areas where BGS will be applied. The "HEART by BioAsssist" application could be used as a health and other data collection tool as well as an "intelligent assistant" to monitor and promote patient's physical activity in areas with Blue-Green Solutions.

An IoT-Enabled Platform for the Assessment of Physical and Mental Activities Utilizing Augmented Reality Exergaming

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.

Quantifying Citizens' Well-Being in Areas with Natural Based Solutions Using Mobile Computing

Urban planners, architects and civil engineers are integrating Nature-Based Solutions (NBS) to address contemporary environmental, social, health and economic challenges. Many studies claim that NBS are poised to improve citizens' well-being in urban areas. NBS can also benefit Public Health, as they can contribute to optimising environmental parameters (such as urban heat island effects, floods, etc.), as well as to the reduction of diseases, as for example cardiovascular ones and the overall mortality rate. In addition, the usage of mobile health (mHealth) solutions has been broadly applied to support citizens' well-being as they can offer monitoring of their physical and physiological status and promote a healthier lifestyle. The aim of this paper is to present the specifications, the design and the development of a mobile app for monitoring citizens' well-being in areas where NBS have been applied. The users' physical activity and vital signs are recorded by wearable devices and the users' locations are recorded by the proposed mobile application. All collected data are transferred to the cloud platform where data management mechanisms aggregate data from different sources for combined analysis. The mobile application is currently available for Android and iOS devices and it is compatible with most smart devices and wearables. The "euPOLIS by BioAssist" application can be used as a health and other data collection tool to investigate citizen's well-being improvement in areas with NBS.

Introducing Gamification in eHealth Platforms for Promoting Wellbeing

Gamification techniques are adopted by IT systems and applications in order to facilitate their adoption and motivate users to take advantage of specific application features. The current work presents a modern approach for the effective implementation of gamification features in a prototype eHealth application which encourages the daily use of the application, endorses the users to continuously monitor their health and promotes a healthier lifestyle. The implementation of this approach is modular and flexible in order to be easily applied in any similar system and tailor the provided features for user activity monitoring, analysis, feedback, and interactivity, to the specific requirements of the different usage scenarios.

Speech Based Affective Analysis of Patients Embedded in Telemedicine Platforms

Speech is a basic means of human expression, not only due to the combination of words that exits our mouth, but also because of the different way we express these words. Apart from the main objective of speech, which is the communication of information, emotions flow in human speech as various vocal characteristics (prosodic, spectral, tonal). By processing these characteristics, Speech Emotion Recognition aims to analyze and assess the human emotional status to complement medical data captured during telemedicine sessions. Driven by the latest developments in Computer Vision concerning Deep Learning techniques, EfficientNets are exploited to extract features and classify imagery representations of human speech into emotions as a web service along with an interpretation scheme. The developed web service will be consumed during video conferences between medical staff and patients for the near real-time assessment of emotional status of patients during video teleconsultations.

Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke

Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We aimed to develop a machine-learning-derived prognostic model for the prediction of cardiovascular risk in ischemic stroke patients.

MATERIALS AND METHODS

Two prospective stroke registries with consecutive acute ischemic stroke patients were used as training/validation and test datasets. The outcome assessed was major adverse cardiovascular event, defined as non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death during 2-year follow-up. The variables selection was performed with the LASSO technique. The algorithms XGBoost (Extreme Gradient Boosting), Random Forest and Support Vector Machines were selected according to their performance. The evaluation of the classifier was performed by bootstrapping the dataset 1000 times and performing cross-validation by splitting in 60% for the training samples and 40% for the validation samples.

RESULTS

The model included age, gender, atrial fibrillation, heart failure, peripheral artery disease, arterial hypertension, statin treatment before stroke onset, prior anticoagulant treatment (in case of atrial fibrillation), creatinine, cervical artery stenosis, anticoagulant treatment at discharge (in case of atrial fibrillation), and statin treatment at discharge. The best accuracy was measured by the XGBoost classifier. In the validation dataset, the area under the curve was 0.648 (95%CI:0.619-0.675) and the balanced accuracy was 0.58 ± 0.14. In the test dataset, the corresponding values were 0.59 and 0.576.

CONCLUSIONS

We propose an externally validated machine-learning-derived model which includes readily available parameters and can be used for the estimation of cardiovascular risk in ischemic stroke patients.

A Fully Open-Source Approach to Intelligent Edge Computing: AGILE's Lesson

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.

Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies

Introduction.

According to WHO, “health policy refers to decisions, plans, and actions that are undertaken to achieve specific health care goals within a society”. Although policymaking is important to be based on scientific evidence, in many countries, evidence-informed decision-making remains the exception rather than the rule.

Aim:

This work presents a cloud-based Decision Support System for public health decision-making.

Methods:

In CrowdHEALTH, the concept of a Public Health Policy (PHP) is directly connected with one or more Key Performance Indexes (KPIs). The design and technical details of the system implementations are reported, along with use case scenarios.

Results:

The Policy Development Toolkit presents a unique interface and point of reference for policymakers, allowing them to create policy models and obtain analytical results for evidence-based decisions and evaluations.

Conclusions:

The hierarchical structure of the Public Health Policy Model offers versatility in the creation and handling of the policies, resulting in Health Analytics Tools Results Objects which offer quantitative policy support and provide the basis for meta-analytic operations.

An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management

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.

Creating Visual Vocabularies for The Retrieval And Classification of Histopathology Images

State-of-the-art technologies in the fields of computer vision and machine learning led the automatic recognition of malignant structures in histopathology images. More than often, such structures are reported to be found in glands, where different morphological characteristics indicate the existence of a variety of adenocarcinomas, including prostate, breast, lung and colon cancer. Classification of images containing glandular representations in different cancer types can be performed in the whole image by the utilization of a combination of local and global features. The proposed methodology involves the exploitation of a notion utilized often in text mining called Bag of Words and employed in the service of Computer Vision with the name of Bag of Visual Words (BOVW) for the development of a retrieval and classification system for pathology images. The paper discusses the technical details of implementation, the enhancement of the BOVW technique, while some initial results using public datasets are presented.

Multi-omics Data and Analytics Integration in Ovarian Cancer

Cancer, which involves the dysregulation of genes via multiple mechanisms, is unlikely to be fully explained by a single data type. By combining different “omes”, researchers can increase the discovery of novel bio-molecular associations with disease-related phenotypes. Investigation of functional relations among genes associated with the same disease condition may further help to develop more accurate disease-relevant prediction models. In this work, we present an integrative framework called Data & Analytic Integrator (DAI), to explore the relationship between different omics via different mathematical formulations and algorithms. In particular, we investigate the combinatorial use of molecular knowledge identified from omics integration methods netDx, iDRW and SSL, by fusing the derived aggregated similarity matrices and by exploiting these in a semi-supervised learner. The analysis workflows were applied to real-life data for ovarian cancer and underlined the benefits of joint data and analytic integration.

Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs’ clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.

Recognizing Occlusal Caries in Dental Intraoral Images Using Deep Learning

Based on an image dataset of 88 in-vivo dental images taken with an intra-oral camera, we show that a Deep Learning model (Mask R-CNN) can detect and classify dental caries on occlusal surfaces across the whole 7-class ICDAS (International Caries Detection and Assessment System) scale. This is accomplished without any image pre-processing method and by utilizing superpixels segmentation for the experts' annotations and the evaluation of the classifier. In the proposed methodology, transfer learning and data augmentation are employed during the training of the model. The paper discusses technical details, provides initial results and denotes points for further improvement by fine-tuning the classifier along with an extended dataset.

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies

Introduction:

With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet.

Aim:

The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms.

Methods:

HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a “health in all policies” approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed.

Results:

CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies.

Conclusions:

A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.

Modelling and Evaluation of Policies

INTRODUCTION

NCDs (non-communicable diseases) are considered an important social issue and a financial burden to the health care systems in the EU which can be decreased if cost-effective policies are implemented, along with proactive interventions. The CrowdHEALTH project recognizes that NCD poses a burden for the healthcare sector and society and aims at focusing on NCDs' public health policies.

AIM

The aim of this paper is to present the concept of Public Health Policy (PHP), elaborate on the state-of-the-art of PHPs development, and propose a first approach to the modeling and evaluation of PHPs used in a toolkit that is going to support decision making, the Policy Development Toolkit (PDT).

METHODS

The policy creation module is a part of the PDT aiming to integrate the results of the rest of the health analytics and policy components. It is the module that selects, filters, and aggregates all relevant information to help policy-makers with the decision making process. The policies creation component is connected to the visualization component to provide the final users with data visualization on different PHPs, including outcomes from data-driven models, such as risk stratification, clinical pathways mining, forecasting or causal analysis models, outcomes from cost-benefit analysis, and suggestions and recommendations from the results of different measured KPIs, using data from the Holistic Health Records (HHRs).

RESULTS

In the context of CrowdHEALTH project, PHP can be defined as the decisions taken for actions by those responsible in the public sector that covers a set of actions or inactions that affect a group of public and private actors of the health care system. In the CrowdHEALTH project, the Policy Development Toolkit works as the main interface between the final users and the whole system in the CrowdHEALTH platform. The three components related to policy creation are: (i) the policy modeling component, (ii) the population identification component and (iii) the policy evaluation component. In policy evaluation, KPIs are used as measurable indicators to help prevent ambiguity problems in the interpretation of the model and the structure.

CONCLUSIONS

This initial Policy creation component design might be modified during the project life circle according to the concept complexity.

Real-time integration of emotion analysis into homecare platforms

The scientific and technological advancements in the area of affective computing enable the development of services in various domains that facilitate the interaction between humans and computers, and can considerably improve decision-making. This work presents the integration and operation of an emotion analysis service in a homecare/mHealth application. In the described approach, the developed emotion analysis service follows the IoT paradigm and it is combined with features for connectivity such as biosignal sensors and wearables, while it is fully integrated in the WebRTC video communication functionality offered by the homecare platform. Thus, it supports the medical experts to perform real-time analysis of their patients' emotional status during interactive video-conference sessions. The paper discusses the technical details of the implementation and the integration of the proposed service and provides initial results from its operation in practice.

Deepbots: A Webots-Based Deep Reinforcement Learning Framework for Robotics

Deep Reinforcement Learning (DRL) is increasingly used to train robots to perform complex and delicate tasks, while the development of realistic simulators contributes to the acceleration of research on DRL for robotics. However, it is still not straightforward to employ such simulators in the typical DRL pipeline, since their steep learning curve and the enormous amount of development required to interface with DRL methods significantly restrict their use by researchers. To overcome these limitations, in this work we present an open-source framework that combines an established interface used by DRL researchers, the OpenAI Gym interface, with the state-of-the-art Webots robot simulator in order to provide a standardized way to employ DRL in various robotics scenarios. Deepbots aims to enable researchers to easily develop DRL methods in Webots by handling all the low-level details and reducing the required development effort. The effectiveness of the proposed framework is demonstrated through code examples, as well as using three use cases of varying difficulty.

Real-Time Surf Manoeuvres’ Detection Using Smartphones’ Inertial Sensors

Surfing is currently one of the most popular water sports in the world, both for recreational and competitive level surfers. Surf session analysis is often performed with commercially available devices. However, most of them seem insufficient considering the surfers’ needs, by displaying a low number of features, being inaccurate, invasive or not adequate for all surfer levels. Despite the fact that performing manoeuvres is the ultimate goal of surfing, there are no available solutions that enable the identification and characterization of such events. In this work, we propose a novel method to detect manoeuvre events during wave riding periods resorting solely to the inertial sensors embedded in smartphones. The proposed method was able to correctly identify over 95% of all the manoeuvres in the dataset (172 annotated manoeuvres), while achieving a precision of up to 80%, using a session-independent validation approach. These findings demonstrate the suitability and validity of the proposed solution for identification of manoeuvre events in real-world conditions, evidencing a high market potential.

A Graph-Based Extension for the Set-Based Model Implementing Algorithms Based on Important Nodes

The purpose of this paper is the expansion of the set-based model, namely an information retrieval model, with the use of graphs. The indexing process implements a graphical representation, while the querying and document representation are based on the classical set-based model. The root of the set-based model corresponds to the use of term sets to complete the querying process based on the terms of the query. However, in the weighting process, this paper presents a wholly different approach elaborating on algorithms that may clearly benefit the process based on the k-core decomposition of each single graph. The main focus will finally be the estimation and presentation of the most important nodes belonging to each graph. These intend to be regarded as keywords presenting the evaluation of their major influence.

Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques.

An Innovative Graph-Based Approach to Advance Feature Selection from Multiple Textual Documents

This paper introduces a novel graph-based approach to select features from multiple textual documents. The proposed solution enables the investigation of the importance of a term into a whole corpus of documents by utilizing contemporary graph theory methods, such as community detection algorithms and node centrality measures. Compared to well-tried existing solutions, evaluation results show that the proposed approach increases the accuracy of most text classifiers employed and decreases the number of features required to achieve ‘state-of-the-art’ accuracy. Well-known datasets used for the experimentations reported in this paper include 20Newsgroups, LingSpam, Amazon Reviews and Reuters.

Deep Echo State Networks in Industrial Applications

This paper analyzes the impact of reservoir computing, and, in particular, of Deep Echo State Networks, to the modeling of highly non-linear dynamical systems that can be commonly found in the industry. Several applications are presented focusing on forecasting models related to energy content of steelwork byproduct gasses. Deep Echo State Network models are trained, validated and tested by exploiting datasets coming from a real industrial context, with good results in terms of accuracy of the predictions.

Acoustic Resonance Testing of Glass IV Bottles

In this paper, acoustic resonance testing on glass intravenous (IV) bottles is presented. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods.

The results from the binary classification experiments are presented and compared in terms of Balanced Accuracy Rate, F1-score, Area Under the Receiver Operating Characteristic Curve and Matthews Correlation Coefficient metrics.

The presented feature analysis and the employed classifiers achieved solid results, despite the rather small and imbalanced dataset with a highly inconsistent class population.

Application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window

Since the discovery of motifs in molecular sequences for real genomic data, research into this phenomenon has attracted increased attention. Motifs are relatively short sequences that are biologically significant. This paper utilises the bioinformatics application of the algorithm outlined in [5], testing it using real genomic data from large sequences. It intends to implement bioinformatics application for real genomic data, in order to discover interesting regions for all maximal motifs, in a sliding window of length ℓ, on a sequence x of length n.

Hong Kong Protests: Using Natural Language Processing for Fake News Detection on Twitter

The automation of fake news detection is the focus of a great deal of scientific research. With the rise of social media over the years, there has been a strong preference for users to be informed using their social media account, leading to a proliferation of fake news through them. This paper evaluates the veracity of politically-oriented news and in particular the tweets about the recent event of Hong Kong protests, with the aid of a dataset recently published by Twitter. From this dataset, Chinese tweets are translated into English, which are kept along with originally English tweets. By utilizing a language-independent filtering process, relevant tweets are identified. To complete the dataset, tweets originating from valid sources are used as the real portion, with journalists rather than news agencies being considered, which constitutes a novel aspect of the methodology. Well-known Machine Learning algorithms are used to classify tweets, which are represented by a feature value vector that is extracted, selected and preprocessed from the datasets and mainly revolves around language use, with word entropy being a novel feature. The results derived from these algorithms highlight morphological, lexical and vocabulary differences between tweets spreading fake and real news, which are for the most part in accordance with past related work.

Harnessing Social Interactions on Twitter for Smart Transportation Using Machine Learning

Twitter is generating a large amount of real-time data in the form of microblogs that has potential knowledge for various applications like traffic incident analysis and urban planning. Social media data represents the unbiased actual insights of citizens’ concerns that may be mined in making cities smarter. In this study, a computational framework has been proposed using word embedding and machine learning model to detect traffic incidents using social media data. The study includes the feasibility of using machine learning algorithms with different feature extraction and representation models for the identification of traffic incidents from the Twitter interactions. The comprehensive proposed approach is the combination of following four steps. In the first phase, a dictionary of traffic-related keywords is formed. Secondly, real-time Twitter data has been collected using the dictionary of identified traffic related keywords. In the third step, collected tweets have been pre-processed, and the feature generation model is applied to convert the dataset eligible for a machine learning classifier. Further, a machine learning model is trained and tested to identify the tweets containing traffic incidents. The results of the study show that machine learning models built on top of right feature extraction strategy is very promising to identify the tweets containing traffic incidents from micro-blogs.

Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Automated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interesting predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.

The Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics

How online cultural content is chosen based on conscious or subconscious criteria is an central question across a broad spectrum of sciences and for the entertainment industry, including content providers and distributors. To this end, a number of tailored analytics forming the backbone of recommendation engines specialized for retrieving cultural content are proposed. Their strength derives directly from well-established principles of cognitive science and behavioral economics, both scientific fields exploring aspects of human decision making. Another novel contribution of this conference paper is that these analytics are implemented in Neo4j expressed as Cypher queries. Various aspects of the cultural content and digital consumers can be naturally represented by appropriately configured vertices, whereas edges represent various connections indicating content delivery preferences. Early experiments conducted over a synthetic dataset mimicking the distributions of preferences and ratings of well-known movie datasets are encouraging as the proposed analytics outperformed the baseline of a multilayer feedforward neural network of various configurations. The synthetic dataset contains enriched preferences of mobile digital consumers of cultural content regarding literature of the Greek region of Ionian Islands.

Combined 5G-Based Video Production and Distribution in a Crowded Stadium Event

Based upon the scope of the original 5G ESSENCE research effort and by considering the related fundamental architecture, we develop a dedicated scenario for the implementation and demonstration of a setup for a 5G edge network acceleration in the context of a sport event, taking place in a stadium. Specifically, we demonstrate a combined 5G-based video production and video distribution scenario towards delivering benefits to both media producers/content providers and mobile operators, with those being able to offer enriched event experience to their subscribers. The production/distribution of locally generated content through the respective platform, coupled with value-added services and rich user context, enables secure, high-quality and resilient transmission in real-time, thus ensuring minimal latency. In the selected scenario, massive data traffic does not affect nor overload the backhaul connection as it is produced, processed and consumed just locally, aligned to the 5G 3GPP specifications. We also discuss details about the proposed services and functionalities of the use case, together with an approach for a potential projection to the market.

Exploring NLP and Information Extraction to Jointly Address Question Generation and Answering

Question Answering (QA) and Question Generation (QG) have been subjects of an intensive study in recent years and much progress has been made in both areas. However, works on combining these two topics mainly focus on how QG can be used to improve QA results. Through existing Natural Language Processing (NLP) techniques, we have implemented a tool that addresses these two topics separately. We further use them jointly in a pipeline. Thus, our goal is to understand how these modules can help each other. For QG, our methodology employs a detailed analysis of the relevant content of a sentence through Part-of-speech (POS) tagging and Named Entity Recognition (NER). Ensuring loose coupling with the QA task, in the latter we use Information Retrieval to rank sentences that might contain relevant information regarding a certain question, together with Open Information Retrieval to analyse the sentences. In its current version, the QG tool takes a sentence to formulate a simple question. By connecting QG with the QA component, we provide a means to effortlessly generate a test set for QA. While our current QA approach shows promising results, when enhancing the QG component we will, in the future, provide questions for which a more elaborated QA will be needed. The generated QA datasets contribute to QA evaluation, while QA proves to be an important technique for assessing the ambiguity of the questions.

Towards a Smart Port: The Role of the Telecom Industry

Transformation is not only today’s trend but also a reality. Ports could not be excluded from that change. A transformation process has been initiated in order to change their operational structure, and the services they offer. Artificial Intelligent and Data oriented services push the services’s landscape beyond the traditional ones that are currently used. The scope of this paper is to analyze and scrutinize the opportunities that are risen for Telecommunications/Information and Communication Technology (ICT) providers at ports. These opportunities are the stepping stone towards the transformation of ports for the future. This work in progress is under the DataPorts project that is funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 871493.

Applying an Intelligent Personal Agent on a Smart Home Using a Novel Dialogue Generator

Nowadays, Intelligent Personal Agents include Natural Language Understanding (NLU) modules, that utilize Machine Learning (ML), which can be included in different kind of applications in order to enable the translation of users’ input into different kinds of actions, as well as ML modules that handle dialogue. This translation is attained by the matching of a user’s sentence with an intent contained in an Agent. This paper introduces the first generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA (https://rasa.com/) framework and utilizes two machine learning models for NLU and dialogue flow classification. Besides the architecture of CIPA—Generation A, a novel dialogue-story generator that is based on the idea of adjacency pairs is introduced. By utilizing on this novel-generator, the agent is able to create all the possible dialog trees in order to handle conversations without training on existing data in contrast with the majority of the current alternative solutions. CIPA supports multiple intents and it is capable of classifying complex sentences consisting of two user’s intents into two automatic operations from the part of the agent. The introduced CIPA—Generation A has been deployed and tested in a real-world scenario at Centre’s of Research & Technology Hellas (CERTH) nZEB Smart Home (https://smarthome.iti.gr/) in two different domains, energy and health domain.

The Ethos of Artificial Intelligence as a Legal Personality in a Globalized Space: Examining the Overhaul of the Post-liberal Technological Order

The categorical ethos of artificial intelligence is influenced by its basic structure, which defines its due purpose as a legal personality, challenging the conventional standards of law and justice in a globalized world. Recent developments show a precedential growth in the need-perspective of the AI industry, thereby influencing governance and corporate operations and their legal side in cross-cultural avenues. The determinant outlining of artificial intelligence as a legal personality rests on its probabilistic nature, which yet can be limited to the jurisprudential scope of AI-based on the ethos of the utilitarian approach involving the anthropocentric innovations for artificial intelligence. The dynamic nature of AI, however, in the proposition, is capable of a full-fledged and anthropomorphic legal representation and interpretation, which is hard to find in D9 and certain developing countries, which poses special risks to the generic legal infrastructure of a democratic polity to understand the dynamic and self-transformative nature of artificial intelligence in the age of globalization.

The paper is thus based on the proposition that the ethos involving the legal infrastructure and persona of artificial intelligence is traceable and easier in deterministic mechanisms by regarding and extending stable & constitutive approaches to dissect the legal challenges connected with the redemptions implicated with the lack of a full-fledged regard and scope of the legal personality of AI. The approaches in due proposition are (a) anthropomorphisation; (b) naturalization; (c) techno-socialization; and (d) enculturation. Further, the paper analyses on the challenges to determine the problematic implications awaited by the influence of populism, protectionism, data-centred digital colonialism and technology distancing and proposes suggestions based on the four approaches to counter the minimal effects of the implications. The conclusions of the paper rest on the argument that in the case of a post-liberal order, the ethos of AI can be protected and diversified by adapting with the appreciation of the ethos of globalization, giving adequate, constitutive and reasonable space to the identity-led implications of national identity & diluting the monopolistic influence of the utilitarian approach to artificial intelligence.