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Early illness recognition using frequent motif discovery.

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    Summary

    This study introduces an early illness recognition framework for older adults living alone. It uses sensor data to detect abnormal activity patterns, helping identify health declines and risks for adverse events.

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    Area of Science:

    • Gerontology
    • Biomedical Engineering
    • Health Informatics

    Background:

    • Older adults living alone often experience delayed assessment of health changes due to normalization of aging symptoms or reluctance to report issues.
    • Early identification of functional decline and adverse events in older adults is crucial for timely intervention and improved health outcomes.

    Purpose of the Study:

    • To describe an early illness recognition framework for older adults using sensor network technology.
    • To identify health trajectories of older adults by analyzing patterns in their day-to-day activities.

    Main Methods:

    • Developed the Abnormal Frequent Activity Pattern (AFAP) framework to detect deviations from normal behavior using sensor data.
    • Utilized the MEME bioinformatics algorithm to identify frequent activity patterns (FAP) from sensor data.
    • Validated the AFAP framework using data from an aging-in-place community equipped with sensor networks (motion, bed, depth sensors).

    Main Results:

    • The AFAP framework identifies abnormal days by detecting past frequent abnormal behavior patterns in current sensor data.
    • The approach does not require specific activity identification, only the labeling of past days as normal or abnormal.
    • Analysis of within-person variability in routine activities showed potential as a predictor for older adult health trajectories.

    Conclusions:

    • The proposed AFAP framework offers a novel method for early illness recognition in older adults living independently.
    • Sensor network technology and activity pattern analysis can provide valuable insights into the health status and risks of older adults.
    • Variability in daily routines detected through sensor data may serve as an important new predictor for monitoring the health trajectories of older adults.