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Med Eng Phys. 2014 Feb;36(2):169-76. doi: 10.1016/j.medengphy.2013.06.005. Epub 2013 Jul 27.

Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection.

Author information

  • 1Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: lugade.vipul@mayo.edu.
  • 2Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: fortune.emma@mayo.edu.
  • 3Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: morrow.melissa@mayo.edu.
  • 4Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: kaufman.kenton@mayo.edu.

Abstract

A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.

Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

KEYWORDS:

Acceleration; Accuracy; Gait velocity; Movement analysis

PMID:
23899533
[PubMed - in process]
PMCID:
PMC3866210
[Available on 2015/2/1]
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