From Computer Innovation to Human Integration: Current Trends and Challenges for Pervasive HealthTechnologies

Röcker, Carsten; Ziefle, Martina; Holzinger, Andreas

doi:10.1007/978-1-4471-6413-5_1

Cited by 49 publications

(35 citation statements)

References 87 publications

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“…Artificial intelligence and desired properties of healthcare systems (predictive, preventive and personalised) are applied [21] to produce smart healthcare environments in order to improve patients' conditions. Novel interactive technologies and sensors are integrated into these environments and medical support systems are developed.…”

Section: 1smart Healthcare Environmentsmentioning

confidence: 99%

Future Healthcare: Will Digital Data Lead to Better Care?

Göçeri

2017

GJPAAS

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Currently, datasets used in bioinformatics and computational biology are high-dimensional, complex and multivariate. Analysis and processing of data is vital in medicine; however, manual analysis and pattern recognition with big data is difficult, and processing of large and weakly connected datasets is challenging. The increasing complexity of healthcare systems causes high health cost. To provide better healthcare services at reduced prices, computer-aided tools using smart approaches and context-aware computations are of great importance. Advancements in wireless network technology, mobile devices and pattern recognition applications help solve the cost problem of healthcare systems. In the future, patients will be able to participate in healthcare as their own health manager and observe important parameters like body fat amount and blood pressure. However, open issues related to this topic exist. In this paper, we present a survey of smart healthcare environments and smart hospitals and discuss some questions and challenges in this area.

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Section: 1smart Healthcare Environmentsmentioning

confidence: 99%

Future Healthcare: Will Digital Data Lead to Better Care?

Göçeri

2017

GJPAAS

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“…Healthcare stands out as a key sector where these novel technologies and associated enhanced services can have a significant impact by improving the quality of life of patients, elderly people, but also the general population through real-time monitoring and intervention which enables proactive and more effective health management, justifying the intensive research efforts in the field [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Policy-Controlled Authenticated Access to LLN-Connected Healthcare Resources

Rantos

Fysarakis

Manifavas

et al. 2018

IEEE Systems Journal

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“…Nesta perspectiva, uma alternativa é a utilização de casas inteligentes que proporcionem um ambiente assistido de vivência no contexto da saúde. Nestas residências devem ser contemplados serviços computacionais que possam auxiliar as pessoas nas suas práticas diárias, da forma mais transparente possível [Röcker et al 2014]. Busca-se assim integrar a tecnologia ao cotidiano do usuário, caracterizando uma infraestrutura computacional de natureza ubíqua que deve exigir o menor envolvimento possível das pessoas no seu gerenciamento, bem como deve possibilitar que as atividades sejam reconhecidas a partir de dados de contexto coletados por sensores, os quais podem estar presentes em objetos carregados pelas pessoas ou incorporados ao ambiente em que as mesmas interagem [Perera et al 2015].…”

Section: Introductionunclassified

Uma Abordagem não Intrusiva para Reconhecimento de Atividades em Casas Inteligentes Explorando Processamento Semântico*

Abreu¹,

Cavalheiro²,

Pernas³

et al. 2018

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP)

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The purpose of this work, named EXEHDA-AR (Execution Environment for Highly Distributed Applications-Activity Recognition), is to provide architectural features that allow the recognition of activities in the EXEHDA middleware, exploiting a semantic processing approach. For this purpose, architectural components were designed and integrated in the Subsystem of Adaptation and Context Recognition of EXEHDA. A case study about smart homes was developed to evaluate the functionalities proposed for the EXEHDA-AR, obtaining an average accuracy of 94.36% in the recognition of activities. These results indicate that methods based on semantic processing constitute a viable alternative, with low level of intrusion.Resumo. O objetivo deste trabalho, denominado EXEHDA-AR (Execution Environment for Highly Distributed Applications-Activity Recognition), é prover recursos arquiteturais que permitam o reconhecimento de atividades no middleware EXEHDA, explorando uma abordagem baseada em processamento semântico. Para tanto foram concebidos componentes arquiteturais, os quais foram integrados ao Subsistema de Reconhecimento de Contexto e Adaptação do EXE-HDA. Um estudo de caso sobre casas inteligentes foi desenvolvido para avaliar as funcionalidades propostas para o EXEHDA-AR, sendo obtida uma acurácia média de 94,36% no reconhecimento de atividades. Estes resultados apontam que métodos baseados em processamento semântico constituem uma alternativa viável, com baixo nível de intrusão.

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From Computer Innovation to Human Integration: Current Trends and Challenges for Pervasive HealthTechnologies

Cited by 49 publications

References 87 publications

Future Healthcare: Will Digital Data Lead to Better Care?

Future Healthcare: Will Digital Data Lead to Better Care?

Policy-Controlled Authenticated Access to LLN-Connected Healthcare Resources

Uma Abordagem não Intrusiva para Reconhecimento de Atividades em Casas Inteligentes Explorando Processamento Semântico*

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