Annals of Biomedical Engineering

, Volume 41, Issue 8, pp 1670–1679

Accelerometry Reveals Differences in Gait Variability Between Patients with Multiple Sclerosis and Healthy Controls

Authors

    • Landon Center on AgingUniversity of Kansas Medical Center
    • Landon Center on Aging
  • Martina Mancini
    • Department of NeurologyOregon Health and Science University
  • Rebecca J. St. George
    • Department of NeurologyOregon Health and Science University
  • Fay B. Horak
    • Department of NeurologyOregon Health and Science University
Article

DOI: 10.1007/s10439-012-0697-y

Cite this article as:
Huisinga, J.M., Mancini, M., St. George, R.J. et al. Ann Biomed Eng (2013) 41: 1670. doi:10.1007/s10439-012-0697-y

Abstract

Variability of movement reflects important information for the maintenance of the health of the system. For pathological populations, changes in variability during gait signal the presence of abnormal motor control strategies. For persons with multiple sclerosis (PwMS), extensive gait problems have been reported including changes in gait variability. While previous studies have focused on footfall variability, the present study used accelerometers on the trunk to measure variability during walking. Thus, the purpose of this study was to examine the variability of the acceleration pattern of the upper and lower trunk in PwMS compared to healthy controls. We extracted linear and nonlinear measures of gait variability from 30 s of steady state walking for 15 PwMS and 15 age-matched healthy controls. PwMS had altered variability compared to controls with greater Lyapunov exponent in the ML (p < 0.001) and AP (p < 0.001) directions, and greater frequency dispersion in the ML direction (p = 0.034). PwMS also demonstrated greater mean velocity in the ML direction (p = 0.045) and lower root mean square of acceleration in the AP direction (p = 0.040). These findings indicate that PwMS have altered structure of variability of the trunk during gait compared to healthy controls and agree with previous findings related to changes in gait variability in PwMS.

Keywords

NonlinearTrunkSteady stateLyapunov exponent

Copyright information

© Biomedical Engineering Society 2012