Abstract
This work presents the Mining Minds Context Ontology, an ontology for the identification of human behavior. This ontology comprehensively models high-level context based on low-level information, including the user activities, locations, and emotions. The Mining Minds Context Ontology is the means to infer high-level context from the low-level information. High-level contexts can be inferred from unclassified contexts by reasoning on the Mining Minds Context Ontology. The Mining Minds Context Ontology is shown to be flexible enough to operate in real life scenarios in which emotion recognition systems may not always be available. Furthermore, it is demonstrated that the activity and the location might not be enough to detect some of the high-level contexts, and that the emotion enables a more accurate high-level context identification. This work paves the path for the future implementation of the high-level context recognition system in the Mining Minds project.
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Acknowledgments
This work was supported by the Industrial Core Technology Development Program, funded by the Korean Ministry of Trade, Industry and Energy (MOTIE), under grant number #10049079.This work was also supported by the Junta de Andalucia Project P12-TIC-2082 and the grant “Movilidad Internacional de Jóvenes Investigadores de Programas de Doctorado Universidad de Granada y CEI BioTic”.
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Villalonga, C. et al. (2015). High-Level Context Inference for Human Behavior Identification. In: Cleland, I., Guerrero, L., Bravo, J. (eds) Ambient Assisted Living. ICT-based Solutions in Real Life Situations. IWAAL 2015. Lecture Notes in Computer Science(), vol 9455. Springer, Cham. https://doi.org/10.1007/978-3-319-26410-3_16
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DOI: https://doi.org/10.1007/978-3-319-26410-3_16
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