Federation of AAL & AHA systems through semantically interoperable framework

[EN] Ambient Assisted Living (AAL) and Active and Healthy Ageing (AHA) immensely benefit from IoT application. The federation of IoT platforms can multiply the benefits obtained by the operation of those systems in an isolated way, as it enables important synergies (e.g., intelligent information sharing, system cooperation, service enhancement). This federation requires the enablement of interoperability between the IoT systems, which represents a major challenge, as systems typically follow very different standards, data formats, semantic models and manners of representing the information. We have provided a technical solution in the frame of ACTIVAGE, a project that aims to federate multiple heterogeneous IoT platforms and systems associated to clusters of AHA Smart Homes in 12 regions across Europe, with the goal to improve the AHA service provided and create the first European AHA ecosystem. Our technical solution allows the enablement of full semantic interoperability across heterogeneous platforms and it has been validated in a test scenario. It enables significant AHA service enhancement within the ACTIVAGE ecosystem, as native applications from one platform could be used indistinctly by all federated platforms. Our solution allows good scalability federating new platforms, with linear and relatively low effort.This research work has been partially funded by LSP H2020 ACTIVAGE project under Grant Agreement Nº 732679.González-Usach, R.; Julián, M.; Esteve Domingo, M.; Palau Salvador, CE. (2021). Federation of AAL & AHA systems through semantically interoperable framework. 1-6. https://doi.org/10.1109/ICCWorkshops50388.2021.94735031

Internet of things in health: Requirements, issues, and gaps

Background and objectives: The Internet of Things (IoT) paradigm has been extensively applied to several sectors in the last years, ranging from industry to smart cities. In the health domain, IoT makes possible new scenarios of healthcare delivery as well as collecting and processing health data in real time from sensors in order to make informed decisions. However, this domain is complex and presents several tech- nological challenges. Despite the extensive literature about this topic, the application of IoT in healthcare scarcely covers requirements of this sector. Methods: A literature review from January 2010 to February 2021 was performed resulting in 12,108 articles. After filtering by title, abstract, and content, 86 were eligible and examined according to three requirement themes: data lifecycle; trust, security, and privacy; and human-related issues. Results: The analysis of the reviewed literature shows that most approaches consider IoT application in healthcare merely as in any other domain (industry, smart cities…), with no regard of the specific requirements of this domain. Conclusions: Future effort s in this matter should be aligned with the specific requirements and needs of the health domain, so that exploiting the capabilities of the IoT paradigm may represent a meaningful step forward in the application of this technology in healthcare.Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía P18-TPJ - 307

O3 – prizadevanja na področju medicinske informatike za e-zdravstveno regijo

The Open Three (O3) Consortium is promoting the adoption of Open Source in e-health on regional, European and World-wide levels. This project aims to contribute to the development of e-health through the study of Healthcare Information Systems and the contemporary proposal of new concepts, designs and solutions for the management of health data in an integrated environment of hospitals, Regional Health Information Organizations and citizens (home-care, mobile-care and ambient assisted living). Some concrete technical solutions in the field of medical informatics are presented in this paper. The applications presented are the heart of the Radiology information system, which is open to other health institutions, thus forming a basis for the realization of e-health integration. The formation of a genuine e-health region is just one step forward.Konzorcij Odprti trije (O3) si prizadeva za sprejetje odprtega vira v e-zdravstvu na regionalnem, evropskem in svetovnem nivoju. Projekt si prizadeva prispevati k razvoju e-zdravstva s pomočjo preučevanja informacijskega sistema zdravstvenega varstva ter sodobnih predlogov novih zasnov, načrtov in rešitev za upravljanje z zdravstvenimi podatki v integriranem okolju bolnišnic, regionalnih organizacijah zdravstvene informatike in pri državljanih (v domači negi, mobilni negi in v primeru bivanja z asistenco v okolju). V prispevku so prikazane nekatere konkretne tehnične rešitve na področju medicinske informatike. Prikazane aplikacije so bistvo radiološkega informacijskega sistema, ki je odprt drugim zdravstvenim institucijam in tako predstavlja temelj za realizacijo e-zdravstvene integracije. Tako je oblikovanje prave e-zdravstvene regije oddaljeno le še za korak

Facilitating Inter-Domain Synergies in Ambient Assisted Living Environments

Current Ambient Assisted Living (AAL) environments lack integration of sensors and actuators of other sub-domains. Creating technical and organizational integration is addressed by the BASIS project (Build Automation by a Scalable and Intelligent System), which aims to build a cross-domain home bus system. The main objective of this paper is to present an overview of design, architecture and state of realization of BASIS by describing the requirements development process, underlying hardware design and software architecture. We built a distributed system of one independent building manager with several redundantly meshed segment controllers, each controlling a bus segment with any number of bus nodes. The software system layer is divided into logical partitions representing each sub-domain. Structured data storage is possible with a special FHIR based home centered data warehouse. The system has been implemented in six apartments running under daily living conditions. BASIS integrates a broad range of sub-domains, which poses challenges to all project partners in terms of a common terminology, and project management methods, but enables development of inter-domain synergies like using the same sensor and actuator hardware for a broad range of services and use cases

A Home E-Health System for Dependent People Based on OSGI

This chapter presents a e-health system for dependent people installed in a home environment. After reviewing the state of art in e-health applications and technologies several limitations have been detected because many solutions are proprietary and lack interoperability. The developed home e-health system provides an architecture capable to integrate different telecare services in a smart home gateway hardware independent from the application layer. We propose a rule system to define users’ behavior and monitor relevant events. Two example systems have been implemented to monitor patients. A data model for the e-health platform is described as well.Ministerio de Educación y Ciencia TSI2006-13390-C02-0

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana