Document Type
Article
Source of Publication
Sensors (Switzerland)
Publication Date
10-24-2017
Abstract
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. 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.
DOI Link
ISSN
Publisher
MDPI AG
Volume
17
Issue
10
First Page
2433
Disciplines
Computer Sciences
Keywords
Context-awareness, Fusioning, Human behavior identification, Ontologies, Reasoning
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
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; and Khan, Wajahat Ali, "mlCAF: Multi-level cross-domain semantic context fusioning for behavior identification" (2017). All Works. 2407.
https://zuscholars.zu.ac.ae/works/2407
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series