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Related Experiment Video

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Decoding Natural Behavior from Neuroethological Embedding
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mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification.

Muhammad Asif Razzaq1, Claudia Villalonga2, Sungyoung Lee3

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

Sensors (Basel, Switzerland)
|October 25, 2017
PubMed
Summary

This study introduces a multi-level Context-aware Framework (mlCAF) for fusing cross-domain user contexts. The framework achieves over 92% accuracy in recognizing abstract contexts for lifestyle and behavior modeling.

Keywords:
context-awarenessfusioninghuman behavior identificationontologiesreasoning

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Area of Science:

  • Ubiquitous and Pervasive Computing
  • Health Informatics
  • Artificial Intelligence

Background:

  • Automatic user context identification is crucial for context-aware applications, particularly for managing chronic diseases through lifestyle monitoring.
  • Integrating heterogeneous, cross-domain data presents significant challenges for extracting meaningful abstract information.
  • Previous work focused on single-domain context (physical activity), necessitating expansion to multiple domains.

Purpose of the Study:

  • To extend context-awareness from a single domain to multiple domains (physical activity, nutrition, clinical) by fusing cross-domain contexts.
  • To propose and evaluate the multi-level Context-aware Framework (mlCAF) for richer behavioral context arbitration.
  • To address challenges in multi-level context modeling, reasoning, and fusion using an open-source ontology.

Main Methods:

  • Developed the multi-level Context-aware Framework (mlCAF) integrating physical activity, nutrition, and clinical data.
  • Extended an existing open-source ontology to accommodate multi-domain context definitions, rules, and semantic queries.
  • Collected multi-level, cross-domain context data from 20 users for framework evaluation.

Main Results:

  • The mlCAF framework successfully fuses multi-level, cross-domain contexts to derive richer behavioral insights.
  • Ontology evolution included new domains, context definitions, rules, and semantic query capabilities.
  • Achieved an average context recognition accuracy of approximately 92.65% for the collected cross-domain contexts.

Conclusions:

  • The proposed mlCAF framework effectively integrates multi-domain contexts for advanced behavior and lifestyle modeling.
  • The framework demonstrates high accuracy in abstract context recognition, supporting applications for chronic disease management.
  • The open-source ontology and fusion approach provide a robust foundation for future context-aware computing research.