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Medical & Biological Engineering & Computing

, Volume 54, Issue 4, pp 663–674 | Cite as

Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different

  • Matthew A. D. BrodieEmail author
  • Milou J. M. Coppens
  • Stephen R. Lord
  • Nigel H. Lovell
  • Yves J. Gschwind
  • Stephen J. Redmond
  • Michael Benjamin Del Rosario
  • Kejia Wang
  • Daina L. Sturnieks
  • Michela Persiani
  • Kim Delbaere
Original Article

Abstract

Morbidity and falls are problematic for older people. Wearable devices are increasingly used to monitor daily activities. However, sensors often require rigid attachment to specific locations and shuffling or quiet standing may be confused with walking. Furthermore, it is unclear whether clinical gait assessments are correlated with how older people usually walk during daily life. Wavelet transformations of accelerometer and barometer data from a pendant device worn inside or outside clothing were used to identify walking (excluding shuffling or standing) by 51 older people (83 ± 4 years) during 25 min of ‘free-living’ activities. Accuracy was validated against annotated video. Training and testing were separated. Activities were only loosely structured including noisy data preceding pendant wearing. An electronic walkway was used for laboratory comparisons. Walking was classified (accuracy ≥97 %) with low false-positive errors (≤1.9 %, κ ≥ 0.90). Median free-living cadence was lower than laboratory-assessed cadence (101 vs. 110 steps/min, p < 0.001) but correlated (r = 0.69). Free-living step time variability was significantly higher and uncorrelated with laboratory-assessed variability unless detrended. Remote gait impairment monitoring using wearable devices is feasible providing new ways to investigate morbidity and falls risk. Laboratory-assessed gait performances are correlated with free-living walks, but likely reflect the individual’s ‘best’ performance.

Keywords

Gait analysis Wavelet Remote Wearable devices Falls 

Notes

Acknowledgments

We gratefully acknowledge support which made this research possible. Devices were lent from Philips Research Europe, Netherlands. M.B., S.L., and K.D. were NHMRC Fellows, Australia. Y.G. was supported by the Margarete and Walter Lichtenstein Foundation, Switzerland.

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Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  • Matthew A. D. Brodie
    • 1
    • 2
    Email author
  • Milou J. M. Coppens
    • 1
  • Stephen R. Lord
    • 1
  • Nigel H. Lovell
    • 2
  • Yves J. Gschwind
    • 1
  • Stephen J. Redmond
    • 2
  • Michael Benjamin Del Rosario
    • 2
  • Kejia Wang
    • 2
  • Daina L. Sturnieks
    • 1
  • Michela Persiani
    • 1
  • Kim Delbaere
    • 1
  1. 1.Falls and Balance Research Group, Neuroscience Research AustraliaUniversity of New South WalesRandwick, SydneyAustralia
  2. 2.Graduate School of Biomedical EngineeringUniversity of New South WalesRandwick, SydneyAustralia

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