title: Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers.
abstract: OBJECTIVE
  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 greater mode 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.
  
  
  CONCLUSIONS
  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.
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Brodie
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Lord
authors.forename: Milou J
authors.initials: MJ
authors.lastname: Coppens
authors.forename: Janneke
authors.initials: J
authors.lastname: Annegarn
authors.forename: Kim
authors.initials: K
authors.lastname: Delbaere
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D016138:Walking / Q000502:physiology