Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers

Matthew A. Brodie*, Stephen R. Lord, Milou J. Coppens, Janneke Annegarn, Kim Delbaere

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    55 Citations (Scopus)

    Abstract

    Objectives: Develop algorithms to detect gait impairments remotely using data from freely worn devices during long-term monitoring. Identify statistical models that describe how gait performances are distributed over several weeks. Determine the data window required to reliably assess an increased propensity for falling. Methods: 1085 days of walking data were collected from eighteen independent-living older people (mean age 83 years) using a freely worn pendant sensor (housing a triaxial accelerometer and pressure sensor). Statistical distributions from several accelerometer-derived gait features (encompassing quantity, exposure, intensity, and quality) were compared for those with and without a history of falling. Results: Participants completed more short walks relative to long walks, as approximated by a power law. Walks less than 13.1 s comprised 50% of exposure to walking-related falls. Daily-life cadence was bimodal and step-time variability followed a log-normal distribution. Fallers took significantly fewer steps per walk and had relatively more exposure from short walks and greatermode of step-time variability. Conclusions: Using a freely worn device and wavelet-based analysis tools allowed long-term monitoring of walks greater than or equal to three steps. In older people, short walks constitute a large proportion of exposure to falls. To identify fallers, mode of variability may be a better measure of central tendency than mean of variability. A week's monitoring is sufficient to reliably assess the long-term propensity for falling. Significance: Statistical distributions of gait performances provide a reference for future wearable device development and research into the complex relationships between daily-life walking patterns, morbidity, and falls.

    Original languageEnglish
    Pages (from-to)2588-2594
    Number of pages7
    JournalIeee transactions on biomedical engineering
    Volume62
    Issue number11
    DOIs
    Publication statusPublished - Nov-2015

    Keywords

    • Accelerometers
    • activity
    • cadence
    • daily
    • distribution
    • exposure
    • falls
    • gait
    • monitoring
    • older
    • patterns
    • people
    • remote
    • sensor
    • variability
    • walking
    • wearable
    • DAILY PHYSICAL-ACTIVITY
    • DAILY-LIFE
    • ACCELERATION PATTERNS
    • PARKINSONS-DISEASE
    • PEOPLE
    • ACCELEROMETER
    • RISK
    • PELVIS
    • SENSOR
    • ADULTS

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