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This paper uses Chaos Game Representation (CGR) to visualize and characterize daily habits. Household monitoring data are analyzed with Dissimilarity Structural Similarity Index (DSSIM) and Earth Mover’s Distance (EMD). The method classifies days into three types—standard, apathy, and wandering—and detects specific habits within standard days, offering insights into older adults’ routines. Experiments with both generated and real datasets demonstrate the feasibility of accurately clustering day types and identifying habits.

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Non-intrusive Monitoring of Daily Habits in Older Adults: A Chaos Game Representation-Based Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Fabio Salice,
  • Matteo Romilio Pasqual,
  • Ferdinando Onori,
  • Sara Comai

摘要

This paper uses Chaos Game Representation (CGR) to visualize and characterize daily habits. Household monitoring data are analyzed with Dissimilarity Structural Similarity Index (DSSIM) and Earth Mover’s Distance (EMD). The method classifies days into three types—standard, apathy, and wandering—and detects specific habits within standard days, offering insights into older adults’ routines. Experiments with both generated and real datasets demonstrate the feasibility of accurately clustering day types and identifying habits.