Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

Sensors

Natalia D໚z-Rodríguez¹  Olmo León Cadah໚²  Manuel Pegalajar Cuéllar²  Johan Lilius¹ 

[1] Åbo Akademi University, Department of Information Technologies, Turku Centre for Computer Science (TUCS) - Joukahainengatan, 3-5, Turku FIN-20520, Finland;University of Granada, Department of Computer Science and Artificial Intelligence, E.T.S.I. Informática y de Telecomunicación -C/. Periodista Daniel Saucedo Aranda s.n., Granada 18071, Spain

关键词: 3D depth sensors;    activity recognition;    fuzzy ontology;    context awareness;    ambient intelligence;    semantic web;    uncertainty;    vagueness;    hybrid systems;   

DOI  :  10.3390/s141018131

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.