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.

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.

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.

Knowledge Discovery on IoT-Enabled mHealth Applications.

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment.

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.

Utilizing a Homecare Platform for Remote Monitoring of Patients with Idiopathic Pulmonary Fibrosis.

Homecare and home telemonitoring are a focal point of emerging healthcare schemes, with proven benefits for both patients, caregivers and providers, including reduction of healthcare costs and improved patients' quality of life, especially in the case of chronic disease management. Studies have evaluated solutions for remote monitoring of chronic patients based on technologies that allow daily symptom and vital signs monitoring, tailored to the needs of specific diseases. In this work, we present an affordable home telemonitoring system for patients with idiopathic pulmonary fibrosis (IPF), based on an application for mobile devices and Bluetooth-enabled sensors for pulse oximetry and blood pressure measurements. Besides monitoring of vital signs, the system incorporates communication via videoconferencing and emergency response, with support from a helpdesk service. A pilot study was conducted, in order to verify the proposed solution's feasibility. The results support the utilization of the system for effective monitoring of patients with IPF.

AI4Work Project: Human-Centric Digital Twin Approaches to Trustworthy AI and Robotics for Improved Working Conditions in Healthcare and Education Sectors.

AI and robotics aim to transform workplace landscapes in a several sectors such as manufacturing, logistics, healthcare, construction, agriculture, and education. Central to this evolution is the innovative use of Digital Twin technology, which creates real-time updated virtual replicas of physical systems and entities. This technology is especially transformative in healthcare and education, promising customized and efficient experiences for all involved. This paper outlines the AI4Work project's approach to leveraging Digital Twin Technology to improve work environments in these sectors. The goal of AI4Work is to formulate a workplace where AI and robots seamlessly collaborate with humans, while explores how to best share tasks between humans and machines in six different domains. For healthcare, AI4Work will explore how Digital Twin technology can assist occupational doctors and psychologists in monitoring the physical and mental health of hospital personnel in order to predict burnout symptoms and to create a sustainable working environment. In education, AI4Work will investigate how to uphold the mental health of both educators and students while fostering a more supportive and enduring educational setting.

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.

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.

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".

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.

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.

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.

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.

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.

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.

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.

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.

A Smart Infotainment System Equiped with Emotional Intelligence.

Bedside infotainment technology has been gaining popularity, helping providers to address patient needs and improve hospitalization experience. Such systems have the potential to become valuable tools for medical and nursing personnel, with the integration of patient monitoring features, bolstering efficiency and coordination. Extending their utility beyond conventional monitoring, the incorporation of affective computing capabilities would allow for early detection of potentially dangerous situations, as an individual's emotional state has a direct effect on their health, cognitive status, behaviour and quality of life. Furthermore, the addition of a serious games module would provide additional value for patients with cognitive decline or mobility issues. This work presents a novel bedside infotainment system, equipped with the aforementioned capabilities and designed to address the needs of patients in long-term care facilities, such as recreation and rehabilitation centres.

CrowdHEALTH: Big Data Analytics and Holistic Health Records.

The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies.