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dc.contributor.authorAsif Razzaq, Muhammad
dc.contributor.authorVillalonga, Claudia (1)
dc.contributor.authorSungyoung, Lee
dc.contributor.authorAkhtar, Usman
dc.contributor.authorAli, Maqbool
dc.contributor.authorKim, Eun-Soo
dc.contributor.authorMasood Khattak, Asad
dc.contributor.authorSeung, Hyonwoo
dc.contributor.authorHur, Taeho
dc.contributor.authorBang, Jaehun
dc.contributor.authorKim, Dohyeong
dc.contributor.authorAli Khan, Wajahat
dc.date2017-10
dc.date.accessioned2018-03-07T15:51:19Z
dc.date.available2018-03-07T15:51:19Z
dc.identifier.issn1424-8220
dc.identifier.urihttps://reunir.unir.net/handle/123456789/6322
dc.description.abstractThe emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.es_ES
dc.language.isoenges_ES
dc.publisherSensorses_ES
dc.relation.ispartofseries;vol. 17, nº 10
dc.relation.urihttp://www.mdpi.com/1424-8220/17/10/2433es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectcontext-awarenesses_ES
dc.subjectontologieses_ES
dc.subjectreasoninges_ES
dc.subjectfusioninges_ES
dc.subjecthuman behavior identificationes_ES
dc.subjectScopuses_ES
dc.subjectJCRes_ES
dc.titlemlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identificationes_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.3390/s17102433


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