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User behavior shift detection in ambient assisted living environments.

Asier Aztiria1, Golnaz Farhadi, Hamid Aghajan

  • 1University of Mondragon, Mondragon, Spain. aaztiria@mondragon.edu.

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Summary
This summary is machine-generated.

This study introduces an algorithm to detect changes in user behavior patterns for intelligent environments. These detected behavioral shifts can help adapt systems and may signal early disease indicators like Alzheimer's.

Keywords:
disease detectionintelligent environmentsshift detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Intelligent environments require understanding user behavior patterns.
  • Frequent behavior identification is crucial for personalized support.
  • Behavioral changes can indicate health issues like depression or Alzheimer's.

Purpose of the Study:

  • To develop and validate an algorithm for detecting shifts in user behavior.
  • To enable adaptation of intelligent environments based on behavioral changes.
  • To explore the potential of behavioral shift detection for early disease diagnosis.

Main Methods:

  • Developed an algorithm to compare current user behaviors with established frequent behaviors.
  • Implemented the algorithm in smart apartment datasets.
  • Validated the algorithm's ability to identify behavioral shifts and necessary modifications.

Main Results:

  • The algorithm successfully identified all shifts from frequent behaviors.
  • The system accurately determined necessary modifications in all tested cases.
  • Validation was performed using datasets recognizing five Activities of Daily Living (ADLs).

Conclusions:

  • The developed algorithm effectively detects behavioral deviations in intelligent environments.
  • Behavioral shift detection offers potential for both system adaptation and health monitoring.
  • This approach supports the development of more responsive and health-aware intelligent systems.