The OCarePlatform : a context-aware system to support independent living

Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Self-managed cells and their federation

Future e-Health systems will consist of low-power, on-body wireless sensors attached to mobile users that interact with a ubiquitous computing environment. This kind of system needs to be able to configure itself with little or no user input; more importantly, it is required to adapt autonomously to changes such as user movement, device failure, the addition or loss of services, and proximity to other such systems. This extended abstract describes the basic architecture of a Self-Managed Cell (SMC) to address these requirements, and discusses various forms of federation between/among SMCs. This structure is motivated by a typical e-Health scenario

Design of an ontology for decision support in VR exposure therapy

Virtual Reality (VR) is finding its way into many domains, including healthcare. Therapists greatly benefit from having any scenario in VR at their disposal for exposure therapy. However, adapting the VR environment to the needs of the patient is time-consuming. Therefore, an intelligent decision support system that takes context information into account would be a big improvement for personalised VR therapy. In this paper, a semantic ontology is presented for modelling relevant concepts and relations in the context of anxiety therapy in VR. The necessary knowledge was collected through workshops with therapists, this resulted in a layered ontology. Furthermore, semantic reasoning through logical rules enables deduction of interesting high-level knowledge from low-level data. The presented ontology is a starting point for further research on intelligent adaptation algorithms for personalised VR exposure therapy

Metadata and ontologies for organizing students’ memories and learning: standards and convergence models for context awareness

Este artículo trata de las ontologías que sirven para la comprensión en contexto y la Gestión de la Información Personal (PIM)y su aplicabilidad al proyecto Memex Metadata(M2). M2 es un proyecto de investigación de la Universidad de Carolina del Norte en Chapel Hill para mejorar la memoria digital de los alumnos utilizando tablet PC, la tecnología SenseCam de Microsoft y otras tecnologías móviles(p.ej. un dispositivo de GPS) para capturar el contexto del aprendizaje. Este artículo presenta el proyecto M2, dicute el concepto de los portafolios digitales en las actuales tendencias educativas, relacionándolos con las tecnologías emergentes, revisa las ontologías relevantes y su relación con el proyecto CAF (Context Awareness Framework), y concluye identificando las líneas de investigación futuras.This paper focuses on ontologies supporting context awareness and Personal Information Management (PIM) and their applicability in Memex Metadata (M2) project. M2 is a research project of the University of North Carolina at Chapel Hill to improve student digital memories using the tablet PC, Microsoft’s SenseCam technology, and other mobile technologies (e.g., a GPS device) to capture context. The M2 project offers new opportunities studying students’ learning with digital technologies. This paper introduces the M2 project; discusses E-portfolios and current educational trends related to pervasive computing; reviews relevant ontologies and their relationship to the projects’ CAF (context awareness framework), and concludes by identifying future research directions

Moving forward on u-healthcare: A framework for patient-centric

Delivering remote healthcare services without deteriorating the ‘patient experience’ requires building highly usable and adaptive applications. Efficient context data collection and management make possible to infer extra knowledge on the user’s situation, making easier the design of these advanced ubiquitous applications. This contribution, part of a work in progress which aims at building an operative AmI middleware, presents a generic architecture to provide u-healthcare services, to be delivered both in mobile and home environments. In particular, we address the design of the Context Management Component (CMC), the module that takes context data from the sensing layer and performs data fusion and reasoning to build an aggregated ‘context image’. We especially explain the requirements on data modelling and the functional features that are imposed to the CMC. The resulting logical multilayered architecture -composed by acquisition and fusion, inference and reasoning levels- is detailed, and the technologies needed to develop the Context Management Component are finally specifie