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Human Behavior Drift Detection in a Smart Home Environment.

Andrea Masciadri1, Anna A Trofimova1, Matteo Matteucci1

  • 1Department of Electronics, Information and Bioengineering, Politecnico di Milano - via Anzani 42, 22100, Como, Italy.

Studies in Health Technology and Informatics
|September 7, 2017
PubMed
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This study introduces a system using sensor data and Hidden Markov Models to detect changes in elderly individuals' daily habits, potentially indicating early disease diagnosis for independent living.

Area of Science:

  • Gerontology and Health Informatics
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Maintaining independent living for the elderly is a significant societal challenge.
  • Early detection of health issues is crucial for timely intervention and improved outcomes.
  • Behavioral changes can be subtle indicators of underlying health conditions.

Purpose of the Study:

  • To develop a system for monitoring elderly individuals' daily habits to support independent living.
  • To provide an early indicator of habit changes that may signal potential disease onset.
  • To leverage sensor data and advanced statistical methods for behavioral analysis.

Main Methods:

  • Utilizing sensor data to capture real-time behavioral patterns.
Keywords:
Behavioral drift detectionHidden Markov model (HMM)Likelihood ratio testsmart home

Related Experiment Videos

  • Employing Hidden Markov Models (HMMs) to represent and analyze behavioral sequences.
  • Applying the Likelihood Ratio Test (LRT) to identify significant variations in behavior over time.

    • Main Results:

    • The system successfully identifies deviations from established daily routines.
    • Habit changes indicative of potential health declines can be flagged.
    • The Hidden Markov Model effectively characterizes normal and altered behavioral patterns.

    Conclusions:

    • The proposed system offers a promising approach for supporting elderly independent living through early detection of health changes.
    • Behavioral monitoring using sensor data and HMMs can serve as a valuable tool in geriatric care.
    • Further research can refine the system for specific disease prediction and personalized health management.