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
Affiliations
Claudia Villalonga
Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
Muhammad Asif Razzaq
Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
Wajahat Ali Khan
Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
Hector Pomares
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
Ignacio Rojas
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
Sungyoung Lee
Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
Oresti Banos
Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea
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.
