It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Gait quality is of significant interest in studies investigating interventions for individuals with gait pathology. A primary challenge in gait analysis is selecting an appropriate metric which adequately reflects aberrant deviations and provides an assessment of gait quality in individuals with gait pathology. Metrics vary in definition of gait quality, and there is lack of consensus on an objective outcome measure for assessing gait; while three-dimensional gait analysis is the gold standard, metrics operationalizable in the form of wearable sensors would provide important information to clinicians and researchers without requiring a laboratory. In this study, we investigated and compared the ability of four metrics to detect aberrant gait through systematically applied joint constraint: Prosthetic Observational Gait Score (POGS), Impulse Asymmetry, Lateral Sway, and Gait Deviation Index (GDI). We analyze these metrics to understand their sensitivity and ability to detect systematic perturbed gait with an eye toward future operationalization in the form of a wearable sensor suite. We systematically applied four unilateral lower limb joint constraint conditions to nine able-bodied participants walking at three speeds creating four distinct gait patterns with variations from the baseline. Notably, POGS and GDI together distinguished five of six joint constraint comparisons, with each metric able to distinguish four joint constraint comparisons. Lateral Sway distinguished three joint constraint conditions and two speed conditions, while Impulse Asymmetry distinguished three constraint conditions. No single metric distinguished every condition. A single metric may be adequate for assessing specific gait features; however, multiple metrics are recommended for comprehensive assessment of pathological gait.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details

1 Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA; Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA
2 School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
3 Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA; School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA