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Ontology-Based High-Level Context Inference for Human Behavior Identification.

Claudia Villalonga1,2, Muhammad Asif Razzaq3, Wajahat Ali Khan4

  • 1Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea. cvillalonga@oslab.khu.ac.kr.

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Summary
This summary is machine-generated.

This study introduces an ontology-based method to combine human behavior primitives like activity, location, and emotion for deriving high-level context. The approach proves robust in identifying context, even with imperfect low-level data.

Keywords:
activitiescontext inferencecontext recognitionemotionshuman behavior identificationlocationsontological reasoningontologies

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Area of Science:

  • Artificial Intelligence
  • Human Behavior Analysis
  • Ontology Engineering

Background:

  • Significant progress in identifying basic human behavior primitives (activities, locations).
  • Need for abstract, contextual information to analyze complex human behavior.
  • Existing methods lack integration of diverse behavioral primitives for higher-level context.

Purpose of the Study:

  • To develop an ontology-based method for deriving high-level context from low-level behavior primitives.
  • To create a novel open ontology encompassing low-level (activity, location, emotion) and high-level context information and their relationships.
  • To present and evaluate a framework utilizing the ontology and reasoning models for intelligent context derivation.

Main Methods:

  • Development of a new open ontology to model relationships between low-level and high-level context.
  • Integration of behavior primitives: activity, location, and emotions.
  • Application of reasoning models within a framework built upon the ontology.

Main Results:

  • The proposed method successfully derives meaningful high-level context information.
  • The system demonstrates robustness in identifying high-level contexts, even with erroneous low-level data.
  • Reasonable inference times were achieved for a specific set of users and instances.

Conclusions:

  • The ontology-based approach effectively combines diverse behavior primitives for advanced context understanding.
  • The method offers robustness against inaccuracies in low-level behavior detection.
  • Scalability to long-term, large-scale scenarios requires further research and optimization.