Context Aware Health Monitoring System

Sridevi, S.; Bhattacharya, Sayantani; Amutha, K. Pal; Mohan, CN; Pitchiah, R.

doi:10.1007/978-3-642-13923-9_27

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…In turn, the effectiveness of subsequent analytics tasks depends on the accuracy of the mapping. Finally, semantic analytics in the sense of SPARQL queries used on RDF graphs have seen use in health domain contexts [18]. The main difference with such work is that they start with the RDF graph representation as a knowledge base.…”

Section: A Related Workmentioning

confidence: 99%

Integrating existing large scale medical laboratory data into the semantic web framework

Haider

Abidi

Woensel

2014

2014 IEEE International Conference on Big Data (Big Data)

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Semantic Web technologies have shown to have great potential in many different domains, to facilitate knowledge representation, exchange and reasoning, in a formal and yet both human and machine understandable way. In particular, within the health domain, they enable knowledge integration and understanding by explicitly defining and linking concepts and relationships using ontologies to information within clinical knowledge bases. This additional metadata also allows for automated decision support and semantic based analytics to be implemented, that facilitate improved healthcare at a lower cost. Unfortunately many existing datasets in healthcare environments are still stored in relational databases, as opposed to using semantic technologies. Due to this, the link with explicit metadata is often lacking or non-existent. Furthermore, both the databases and the clinical terminologies can be considerably large, making the mapping and subsequent uses of the information a difficult process. In a full fledged decision support system the level and accuracy of the mapping can greatly influence the effectiveness of any subsequent analysis and decision support tasks. This is especially true in clinical scenarios, where very large and complex sets of terms need to be mapped to relational databases. In this paper we aim to provide a general approach for interlinking relational data with clinical ontology based metadata that allows for a fine grade evaluation, with respect to the mapping's impact on analytics. We evaluate our approach by mapping information from clinical terminologies, such as SNOMED CT, to a large laboratory dataset contained in a relational database, with the goal of creating a full fledged, semantically enabled, analytics and decision support system.

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Section: A Related Workmentioning

confidence: 99%

Integrating existing large scale medical laboratory data into the semantic web framework

Haider

Abidi

Woensel

2014

2014 IEEE International Conference on Big Data (Big Data)

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“…An important feature of remote monitoring applications is to identify the abnormal conditions of a patient accurately and so send appropriate alerts to the care givers. In traditional systems, situations are classified by generalized medical rules [11] or fuzzy rules [12] which are not always applicable for every kind of patient. These systems cannot sense the future at an early stage.…”

Section: Introductionmentioning

confidence: 99%

BDCaM: Big Data for Context-Aware Monitoring—A Personalized Knowledge Discovery Framework for Assisted Healthcare

Forkan

Khalil

Ibaida

et al. 2017

IEEE Trans. Cloud Comput.

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Context-aware monitoring is an emerging technology that provides real-time personalised health-care services and a rich area of big data application. In this paper, we propose a knowledge discovery-based approach that allows the context-aware system to adapt its behaviour in runtime by analysing large amounts of data generated in ambient assisted living (AAL) systems and stored in cloud repositories. The proposed BDCaM model facilitates analysis of big data inside a cloud environment. It first mines the trends and patterns in the data of an individual patient with associated probabilities and utilizes that knowledge to learn proper abnormal conditions. The outcomes of this learning method are then applied in context-aware decision-making processes for the patient. A use case is implemented to illustrate the applicability of the framework that discovers the knowledge of classification to identify the true abnormal conditions of patients having variations in blood pressure (BP) and heart rate (HR). The evaluation shows a much better estimate of detecting proper anomalous situations for different types of patients. The accuracy and efficiency obtained for the implemented case study demonstrate the effectiveness of the proposed model.

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“…While many context-based monitoring systems exist, most such systems lack interactivity [6,9]. The lack of online feedback can result in missed opportunities for users to help disambiguate the collected data.…”

Section: Introductionmentioning

confidence: 99%

“…The lack of online feedback can result in missed opportunities for users to help disambiguate the collected data. Other monitoring services either have limited extensibility for new sensors due to vendor-specific context formats [5,8], or they rely on thirdparty support for storage and processing and impose deployment restrictions [5][6][7]. For most systems, power management is not a first-order concern, and thus is either not incorporated into the design or not evaluated [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

ContextProvider: Context awareness for medical monitoring applications

Mitchell

Meyers

Wang

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Smartphones are sensor-rich and Internet-enabled.With their on-board sensors, web services, social media, and external biosensors, smartphones can provide contextual information about the device, user, and environment, thereby enabling the creation of rich, biologically driven applications.We introduce ContextProvider, a framework that offers a unified, query-able interface to contextual data on the device. Unlike other context-based frameworks, ContextProvider offers interactive user feedback, self-adaptive sensor polling, and minimal reliance on third-party infrastructure. ContextProvider also allows for rapid development of new context and bio-aware applications.Evaluation of ContextProvider shows the incorporation of an additional monitoring sensor into the framework with fewer than 100 lines of Java code. With adaptive sensor monitoring, power consumption per sensor can be reduced down to 1% overhead. Finally, through the use of context, accuracy of data interpretation can be improved by up to 80%.

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Context Aware Health Monitoring System

Cited by 14 publications

References 3 publications

Integrating existing large scale medical laboratory data into the semantic web framework

Integrating existing large scale medical laboratory data into the semantic web framework

BDCaM: Big Data for Context-Aware Monitoring—A Personalized Knowledge Discovery Framework for Assisted Healthcare

ContextProvider: Context awareness for medical monitoring applications

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