Universidad de Granada Digibug

Repositorio Institucional de la Universidad de Granada >
1.-Investigación >
Departamentos, Grupos de Investigación e Institutos >
Departamento de Ciencias de la Computación e Inteligencia Artificial >
DCCIA - Artículos >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/35463

Title: Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method
Authors: Díaz-Rodríguez, Natalia
León Cadahía, Olmo
Pegalajar Cuéllar, Manuel
Lilius, Johan
Delgado Calvo-Flores, Miguel
Issue Date: 2014
Abstract: Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.
Sponsorship: This work was funded by TUCS (Turku Centre for Computer Science), Finnish Cultural Foundation, Nokia Foundation, Google Anita Borg Scholarship, CEI BioTIC Project CEI2013-P-3, Contrato-Programa of Faculty of Education, Economy and Technology of Ceuta and Project TIN2012-30939 from National I+D Research Program (Spain). We also thank Fernando Bobillo for his support with FuzzyOWL and FuzzyDL tools.
Publisher: MDPI
Keywords: 3D depth sensors
Activity recognition
Fuzzy ontology
Context awareness
Ambient intelligence
Semantic web
Hybrid systems
URI: http://hdl.handle.net/10481/35463
ISSN: 1424-8220
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Díaz-Rodríguez, N.; et al. Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method. Sensors, 14(10): 18131-18171 (2014). [http://hdl.handle.net/10481/35463]
Appears in Collections:DCCIA - Artículos

Files in This Item:

File Description SizeFormat
DiazRodriguez_Recognition.pdf486.25 kBAdobe PDFView/Open
Recommend this item

This item is licensed under a Creative Commons License
Creative Commons

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! OpenAire compliant DSpace Software Copyright © 2002-2007 MIT and Hewlett-Packard - Feedback

© Universidad de Granada