Ontology-Based Context Modeling and Reasoning for U-HealthCare

IEICE Transactions on Information and Systems (Impact Factor: 0.21). 08/2007; 90-D(8):1262-1270. DOI: 10.1093/ietisy/e90-d.8.1262
Source: DBLP


In order to prepare the health care industry for an increasingly aging society, a ubiquitous health care infrastructure is certainly needed. In a ubiquitous computing environment, it is important that all applications and middleware should be executed on an embedded system. To provide personalized health care services to users anywhere and anytime, a context-aware framework should convert low-level context to high-level context. Therefore, ontology and rules were used in this research to convert low-level context to high-level context. In this paper, we propose context modeling and context reasoning in a context-aware framework which is executed on an embedded wearable system in a ubiquitous computing environment for U-HealthCare. The objective of this research is the development of the standard ontology foundation for health care services and context modeling. A system for knowledge inference technology and intelligent service deduction is also developed in order to recognize a situation and provide customized health care service. Additionally, the context-aware framework was tested experimentally.

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    • "To organize the u-healthcare infrastructure, it is necessary to establish a context-aware framework appropriate for the wearable computer or smallsized portable personal computer in ubiquitous environment [23]. The mobile health (m-health) a form of ubiquitous computing can be defined as mobile communications network technologies for healthcare [24]. "
    Dataset: Manuscript

    Full-text · Dataset · Sep 2014
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    • "By implementing a context-aware, ontology-based framework, care tasks could be automated to alleviate caregivers' workload. The first steps towards such frameworks can be found inPaganelli and Giuli (2011);Ongenae et al. (2011bOngenae et al. ( , 2013);Fook et al. (2006);Zhang et al. (2005) andKo et al. (2007). Ontologies are widely accepted within the eHealth domain, e.g., Galen Common Reference Model (Rector et al., 2003) or the Gene Ontology (Blake and Harris, 2008).Ongenae et al. (2011a)give an overview of the most relevant, well-known and well-developed eHealth ontologies. "
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    ABSTRACT: Ontology engineering methodologies tend to emphasize the role of the knowledge engineer or require a very active role of domain experts. In this paper, a participatory ontology engineering method is described that holds the middle ground between these two 'extremes'. After thorough ethnographic research, an interdisciplinary group of domain experts closely interacted with ontology engineers and social scientists in a series of workshops. Once a preliminary ontology was developed, a dynamic care request system was built using the ontology. Additional workshops were organized involving a broader group of domain experts to ensure the applicability of the ontology across continuous care settings. The proposed method successfully actively engaged domain experts in constructing the ontology, without overburdening them. Its applicability is illustrated by presenting the co-created continuous care ontology. The lessons learned during the design and execution of the approach are also presented.
    Preview · Article · Jan 2014 · Applied ontology
    • "Additionally, monitoring Figure 1. A feature model to design an application framework to develop context-aware MPMS TABLE I. COMMON AND VARIABLE FEATURES OF CONTEXT-AWARE MOBILE PATIENT MONITORING FRAMEWORKS Features Common/ Variable Literature Anywhere and anytime monitoring Common [1-11] Real-time continuous monitoring Common [1, 4, 5, 7, 9, 10, 12- 15] Unlimited number of sensors Common [2-9, 11-19] Unlimited number of monitoring applications Common [2-4, 9-11, 13, 15, 17, 19, 20] Context-aware monitoring query Common [2, 4, 6, 8-17, 19, 21] Query alarm Common [4, 9, 11] Instant alarm Variable [4, 9, 11] Duration alarm Variable [3, 4] Query reasoning approach using first-order logic Common [4, 9, 11, 12, 15] Query element Common [2-4, 9, 12] Measurable medical context Variable [2, 8-16] Nonmeasurable medical context Variable [3] Prescribed medications medical context Variable [3, 8, 10] Risk factors medical context Variable [2, 9-11] Physical activities context Variable [8, 10, 14, 16, 22] Environmental context Variable [2, 9-12, 15, 16] Wireless body sensors Variable [2, 8-16] Wireless environmental sensors Variable [2, 9-12, 15, 16] Patient profile Variable [2, 8-11] Mobile graphical user interface Variable [3] Hosting patient profile on mobile device Variable [9] patients anywhere and anytime can improve patient life styles by becoming more independent, more flexible and mobile while being monitored [23] "
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    ABSTRACT: The objective of this paper is to present a feature model as a main deliverable of a domain analysis for context-aware Mobile Patient Monitoring Systems (MPMS). This model is a part of ongoing work to design an application framework to develop context-aware MPMS. These systems will enable elderly populations and patients with chronic diseases to undertake monitoring of themselves during their daily life. Unfortunately, developing these systems is very complex. An application framework, as an ideal reuse technique, is one of the most suitable solutions to simplify the development of such systems and overcome their development complexity. The scope of this paper is limited to construction process for context-aware MPMS feature model. The expected benefits of the resulted model are twofold. First, it enhances the understanding of the domain of context-aware MPMS. Second, it supports designing frameworks that satisfy the main characteristics of application frameworks, which are framework extensibility and reusability.
    No preview · Conference Paper · Dec 2012
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