Journal
EXPERIMENTAL BRAIN RESEARCH
Volume 224, Issue 2, Pages 287-294Publisher
SPRINGER
DOI: 10.1007/s00221-012-3310-6
Keywords
Visual information; Eyes closed; Gait variability; Coefficient of variation; Detrended fluctuation analysis; Fractal dimension
Categories
Funding
- German Research Foundation (Deutsche Forschungsgemeinschaft) [DFG JA1087/1-1]
- German Hertie Foundation
- Federal Ministry for Education and Science of Germany [BMBF 01EO0901]
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Healthy persons exhibit relatively small temporal and spatial gait variability when walking unimpeded. In contrast, patients with a sensory deficit (e.g., polyneuropathy) show an increased gait variability that depends on speed and is associated with an increased fall risk. The purpose of this study was to investigate the role of vision in gait stabilization by determining the effects of withdrawing visual information (eyes closed) on gait variability at different locomotion speeds. Ten healthy subjects (32.2 +/- A 7.9 years, 5 women) walked on a treadmill for 5-min periods at their preferred walking speed and at 20, 40, 70, and 80 % of maximal walking speed during the conditions of walking with eyes open (EO) and with eyes closed (EC). The coefficient of variation (CV) and fractal dimension (alpha) of the fluctuations in stride time, stride length, and base width were computed and analyzed. Withdrawing visual information increased the base width CV for all walking velocities (p < 0.001). The effects of absent visual information on CV and alpha of stride time and stride length were most pronounced during slow locomotion (p < 0.001) and declined during fast walking speeds. The results indicate that visual feedback control is used to stabilize the medio-lateral (i.e., base width) gait parameters at all speed sections. In contrast, sensory feedback control in the fore-aft direction (i.e., stride time and stride length) depends on speed. Sensory feedback contributes most to fore-aft gait stabilization during slow locomotion, whereas passive biomechanical mechanisms and an automated central pattern generation appear to control fast locomotion.
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