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

Natalia Díaz Rodríguez, Olmo León Cadahía, Manuel P. Cuéllar, Johan Lilius, Miguel Delgado-Calvo-Flores, 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://dx.doi.org/10.3390/s141018131

Abstract:

Human activity recognition is a key task in Ambient Intelligence applications to achieve proper Ambient Assisted Living. There have been remarkable progresses 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 modelling 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 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 outperforms substantially the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 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.

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BibTeX entry:

@ARTICLE{jDxLeCuLiDe14a,
  title = {Handling Real-World Context-Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method},
  author = {Díaz Rodríguez, Natalia and León Cadahía, Olmo and Cuéllar, Manuel P. and Lilius, Johan and Delgado-Calvo-Flores, Miguel},
  journal = {Sensors},
  volume = {14},
  number = {10},
  publisher = {MDPI},
  pages = {18131–18171},
  year = {2014},
  keywords = {3D depth sensors; Activity Recognition; Fuzzy Ontology; Context-Awareness; Ambient Intelligence; Semantic Web; Uncertainty; Vagueness; Hybrid systems },
  ISSN = {1424-8220},
}

Belongs to TUCS Research Unit(s): Embedded Systems Laboratory (ESLAB)

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