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

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...

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Autores principales: Razzaq, Muhammad Asif, Villalonga, Claudia, Lee, Sungyoung, Akhtar, Usman, Ali, Maqbool, Kim, Eun-Soo, Khattak, Asad Masood, Seung, Hyonwoo, Hur, Taeho, Bang, Jaehun, Kim, Dohyeong, Ali Khan, Wajahat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677224/
https://www.ncbi.nlm.nih.gov/pubmed/29064459
http://dx.doi.org/10.3390/s17102433
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author Razzaq, Muhammad Asif
Villalonga, Claudia
Lee, Sungyoung
Akhtar, Usman
Ali, Maqbool
Kim, Eun-Soo
Khattak, Asad Masood
Seung, Hyonwoo
Hur, Taeho
Bang, Jaehun
Kim, Dohyeong
Ali Khan, Wajahat
author_facet Razzaq, Muhammad Asif
Villalonga, Claudia
Lee, Sungyoung
Akhtar, Usman
Ali, Maqbool
Kim, Eun-Soo
Khattak, Asad Masood
Seung, Hyonwoo
Hur, Taeho
Bang, Jaehun
Kim, Dohyeong
Ali Khan, Wajahat
author_sort Razzaq, Muhammad Asif
collection PubMed
description 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.
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spelling pubmed-56772242017-11-17 mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification Razzaq, Muhammad Asif Villalonga, Claudia Lee, Sungyoung Akhtar, Usman Ali, Maqbool Kim, Eun-Soo Khattak, Asad Masood Seung, Hyonwoo Hur, Taeho Bang, Jaehun Kim, Dohyeong Ali Khan, Wajahat Sensors (Basel) Article 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. MDPI 2017-10-24 /pmc/articles/PMC5677224/ /pubmed/29064459 http://dx.doi.org/10.3390/s17102433 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Razzaq, Muhammad Asif
Villalonga, Claudia
Lee, Sungyoung
Akhtar, Usman
Ali, Maqbool
Kim, Eun-Soo
Khattak, Asad Masood
Seung, Hyonwoo
Hur, Taeho
Bang, Jaehun
Kim, Dohyeong
Ali Khan, Wajahat
mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
title mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
title_full mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
title_fullStr mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
title_full_unstemmed mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
title_short mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
title_sort mlcaf: multi-level cross-domain semantic context fusioning for behavior identification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677224/
https://www.ncbi.nlm.nih.gov/pubmed/29064459
http://dx.doi.org/10.3390/s17102433
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