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Human Activity Recognition Models in Ontology Networks.

Luca Buoncompagni, Syed Yusha Kareem, Fulvio Mastrogiovanni

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    Summary

    Arianna+ is a framework for designing ontology networks to enable smart home human activity recognition. It uses logic-based reasoning and contextualized data for efficient and intelligible knowledge representation.

    Area of Science:

    • Artificial Intelligence
    • Computer Science
    • Knowledge Representation

    Background:

    • Smart homes require effective human activity recognition.
    • Existing methods often lack flexibility in data contextualization and integration.
    • Representing complex knowledge for activity recognition is challenging.

    Purpose of the Study:

    • To present Arianna+, a novel framework for designing networks of ontologies for smart home activity recognition.
    • To enable flexible data contextualization and integration of heterogeneous data processing techniques.
    • To demonstrate the benefits of a network of small ontologies over a single large ontology.

    Main Methods:

    • Designing networks of ontologies where nodes represent ontologies and edges represent computational procedures.

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  • Employing logic-based reasoning for scheduling procedures based on events and statement classification.
  • Utilizing a modular network with spatial and temporal contexts for activity recognition.
  • Integrating logic-based and data-driven activity models in a context-oriented architecture.

    • Main Results:

    • Arianna+ facilitates the design of networks that encode data within multiple contexts.
    • A modular network demonstrated effective spatial and temporal contextualization for activity recognition.
    • Networks of small ontologies are more intelligible and computationally efficient than single large ontologies.
    • The framework accommodates heterogeneous data processing techniques within a unified architecture.

    Conclusions:

    • Arianna+ offers a flexible and efficient architecture for human activity recognition in smart homes.
    • Contextualization and reasoning are key to leveraging data for improved activity recognition.
    • The framework supports an iterative development process involving domain experts.
    • This approach enhances the intelligibility and reduces the computational load of knowledge representation for smart home applications.