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Sensors 2017, 17(10), 2433;

mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University (Global Campus), Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
School of Engineering and Technology, Universidad Internacional de La Rioja (UNIR), C/ Almansa 101, 28040 Madrid, Spain
Department of Electronic Engineering, Kwangwoon University 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
College of Technological Innovation, Zayed University, Abu Dhabi 144534, UAE
Department of Computer Science, Seoul Women’s University, Seoul 01797, Korea
Author to whom correspondence should be addressed.
Received: 20 September 2017 / Revised: 16 October 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
(This article belongs to the Special Issue Context Aware Environments and Applications)
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The 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. View Full-Text
Keywords: context-awareness; ontologies; reasoning; fusioning; human behavior identification context-awareness; ontologies; reasoning; fusioning; human behavior identification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Razzaq, M.A.; Villalonga, C.; Lee, S.; Akhtar, U.; Ali, M.; Kim, E.-S.; Khattak, A.M.; Seung, H.; Hur, T.; Bang, J.; Kim, D.; Ali Khan, W. mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification. Sensors 2017, 17, 2433.

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