Consistency of gait characteristics as determined from acceleration data collected at different trunk locations.
Estimates of gait characteristics may suffer from errors due to discrepancies in accelerometer location. This is particularly problematic for gait measurements in daily life settings, where consistent sensor positioning is difficult to achieve. To address this problem, we equipped 21 healthy adults with tri-axial accelerometers (DynaPort MiniMod, McRoberts) at the mid and lower lumbar spine and anterior superior iliac spine (L2, L5 and ASIS) while continuously walking outdoors back and forth (20 times) over a distance of 20 m, including turns. We compared 35 gait characteristics between sensor locations by absolute agreement intra-class correlations (2, 1; ICC). We repeated these analyses after applying a new method for off-line sensor realignment providing a unique definition of the vertical and, by symmetry optimization, the two horizontal axes. Agreement between L2 and L5 after realignment was excellent (ICC>0.9) for stride time and frequency, speed and their corresponding variability and good (ICC>0.7) for stride regularity, movement intensity, gait symmetry and smoothness and for local dynamic stability. ICC values benefited from sensor realignment. Agreement between ASIS and the lumbar locations was less strong, in particular for gait characteristics like symmetry, smoothness, and local dynamic stability (ICC generally<0.7). Unfortunately, this lumbar-ASIS agreement did not benefit consistently from sensor realignment. Our findings show that gait characteristics are robust against limited repositioning error of sensors at the lumbar spine, in particular if our off-line realignment is applied. However, larger positioning differences (from lumbar positions to ASIS) yield less consistent estimates and should hence be avoided.
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands.
Sietse M
SM
Rispens
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands. Electronic address: m.pijnappels@vu.nl.
Mirjam
M
Pijnappels
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands.
Kimberley S
KS
van Schooten
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands; Brunel University, School of Sport & Education, Uxbridge, UK.
Peter J
PJ
Beek
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands.
Andreas
A
Daffertshofer
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands; King Abdulaziz University, Jeddah, Saudi Arabia.
Jaap H
JH
van Dieën
D061725:Accelerometry / Q000706:statistics & numerical data*; D000203:Activities of Daily Living / Q000145:classification; D000328:Adult; D001415:Back / Q000502:physiology; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008159:Lumbar Vertebrae / Q000502:physiology*; D008297:Male; D009068:Movement / Q000502:physiology*; D011187:Posture / Q000502:physiology; D012016:Reference Values; D015203:Reproducibility of Results; D016138:Walking / Q000502:physiology
Agreement; Daily life activities; Inertial sensor; Realignment; Sensor positioning