Ontology-Based High-Level Context Inference for Human Behavior Identification
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Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
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Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies—University of Granada (CITIC-UGR), C/Periodista Rafael Gomez Montero 2, Granada 18071, Spain
3
Telemedicine Group, Center for Telematics and Information Technology, University of Twente, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Vladimir Villarreal and Carmelo R. García
Sensors 2016, 16(10), 1617; https://doi.org/10.3390/s16101617
Received: 30 April 2016
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Revised: 21 September 2016
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Accepted: 22 September 2016
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Published: 29 September 2016
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
Recent years have witnessed a huge progress in the automatic 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.
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Keywords:
context recognition; context inference; ontologies; ontological reasoning; human behavior identification; activities; locations; emotions
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MDPI and ACS Style
Villalonga, C.; Razzaq, M.A.; Khan, W.A.; Pomares, H.; Rojas, I.; Lee, S.; Banos, O. Ontology-Based High-Level Context Inference for Human Behavior Identification. Sensors 2016, 16, 1617. https://doi.org/10.3390/s16101617
AMA Style
Villalonga C, Razzaq MA, Khan WA, Pomares H, Rojas I, Lee S, Banos O. Ontology-Based High-Level Context Inference for Human Behavior Identification. Sensors. 2016; 16(10):1617. https://doi.org/10.3390/s16101617
Chicago/Turabian StyleVillalonga, Claudia, Muhammad A. Razzaq, Wajahat A. Khan, Hector Pomares, Ignacio Rojas, Sungyoung Lee, and Oresti Banos. 2016. "Ontology-Based High-Level Context Inference for Human Behavior Identification" Sensors 16, no. 10: 1617. https://doi.org/10.3390/s16101617
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