Ontology-based high-level context inference for human behavior identification

Claudia Villalonga, Muhammad Asif Razzaq, Wajahat Ali Khan, Hector Pomares, Ignacio Rojas, Sungyoung Lee, Oresti Banos Legran

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    22 Citations (Scopus)
    108 Downloads (Pure)


    Recent years have witnessed a huge progress in the utomatic identification of individual primitives of human behavior, such as activities or locations. However, the complex nature of human behavior demands more abstract contextual information for its analysis. This work presents an ontology-based method that combines low-level primitives of behavior, namely activity, locations and emotions, unprecedented to date, to intelligently derive more meaningful high-level context information. The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts. Despite reasonable inference times being obtained for a relevant set of users and instances, additional work is required to scale to long-term scenarios with a large number of users.
    Original languageUndefined
    Pages (from-to)1-26
    Number of pages26
    JournalSensors (Switzerland)
    Issue number10
    Publication statusPublished - 2016


    • Emotions
    • activities
    • ontological reasoning
    • Ontologies
    • locations
    • IR-101559
    • context recognition
    • human behavioridentification
    • EWI-27261
    • METIS-318538
    • context inference

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