Identifying gait quality metrics sensitive to changes in lower limb constraint

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.

Keywords:

• Gait analysis
• outcome assessment
• biomechanics
• locomotion
• asymmetrical walking

Reviewing Editor:

• Zhongmin Jin
• Xian Jiao Tong University
• China and Leeds University
• UK

SUBJECTS:

• Biophysics
• biomedical engineering
• allied health

Acknowledgements

The authors gratefully acknowledge the Georgia Tech EPIC and PoWeR labs for use of their laboratory space for this experiment. Additionally, the authors acknowledge the efforts of Margaret Berry and Navya Katragadda in processing the Vicon data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, KH, upon reasonable request.

Additional information

Funding

This work was supported by the National Science Foundation under Grant [Grant number NSF-NRI 1734416].