Ontology-Based High-Level Context Inference for Human Behavior Identification
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 m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/10/1617 |
| _version_ | 1818036021914238976 |
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| author | Claudia Villalonga Muhammad Asif Razzaq Wajahat Ali Khan Hector Pomares Ignacio Rojas Sungyoung Lee Oresti Banos |
| author_facet | Claudia Villalonga Muhammad Asif Razzaq Wajahat Ali Khan Hector Pomares Ignacio Rojas Sungyoung Lee Oresti Banos |
| author_sort | Claudia Villalonga |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T07:04:20Z |
| format | Article |
| id | doaj.art-020efffbe7c542179c69a7f2251d1967 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:04:20Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-020efffbe7c542179c69a7f2251d19672022-12-22T01:58:12ZengMDPI AGSensors1424-82202016-09-011610161710.3390/s16101617s16101617Ontology-Based High-Level Context Inference for Human Behavior IdentificationClaudia Villalonga0Muhammad Asif Razzaq1Wajahat Ali Khan2Hector Pomares3Ignacio Rojas4Sungyoung Lee5Oresti Banos6Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, KoreaUbiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, KoreaUbiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, KoreaDepartment 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, SpainDepartment 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, SpainUbiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, KoreaUbiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, KoreaRecent 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.http://www.mdpi.com/1424-8220/16/10/1617context recognitioncontext inferenceontologiesontological reasoninghuman behavior identificationactivitieslocationsemotions |
| spellingShingle | Claudia Villalonga Muhammad Asif Razzaq Wajahat Ali Khan Hector Pomares Ignacio Rojas Sungyoung Lee Oresti Banos Ontology-Based High-Level Context Inference for Human Behavior Identification Sensors context recognition context inference ontologies ontological reasoning human behavior identification activities locations emotions |
| title | Ontology-Based High-Level Context Inference for Human Behavior Identification |
| title_full | Ontology-Based High-Level Context Inference for Human Behavior Identification |
| title_fullStr | Ontology-Based High-Level Context Inference for Human Behavior Identification |
| title_full_unstemmed | Ontology-Based High-Level Context Inference for Human Behavior Identification |
| title_short | Ontology-Based High-Level Context Inference for Human Behavior Identification |
| title_sort | ontology based high level context inference for human behavior identification |
| topic | context recognition context inference ontologies ontological reasoning human behavior identification activities locations emotions |
| url | http://www.mdpi.com/1424-8220/16/10/1617 |
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