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Applied Bionics and Biomechanics
Volume 2019, Article ID 1286864, 8 pages
https://doi.org/10.1155/2019/1286864
Research Article

Human Gait Analysis Metric for Gait Retraining

University of South Florida, USA

Correspondence should be addressed to Kyle B. Reed; ude.fsu@deerelyk

Received 19 April 2019; Revised 25 July 2019; Accepted 10 September 2019; Published 11 November 2019

Guest Editor: Michelle Johnson

Copyright © 2019 Tyagi Ramakrishnan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The combined gait asymmetry metric (CGAM) provides a method to synthesize human gait motion. The metric is weighted to balance each parameter’s effect by normalizing the data so all parameters are more equally weighted. It is designed to combine spatial, temporal, kinematic, and kinetic gait parameter asymmetries. It can also combine subsets of the different gait parameters to provide a more thorough analysis. The single number quantifying gait could assist robotic rehabilitation methods to optimize the resulting gait patterns. CGAM will help define quantitative thresholds for achievable balanced overall gait asymmetry. The study presented here compares the combined gait parameters with clinical measures such as timed up and go (TUG), six-minute walk test (6MWT), and gait velocity. The comparisons are made on gait data collected on individuals with stroke before and after twelve sessions of rehabilitation. Step length, step time, and swing time showed a strong correlation to CGAM, but the double limb support asymmetry has nearly no correlation with CGAM and ground reaction force asymmetry has a weak correlation. The CGAM scores were moderately correlated with TUG and strongly correlated to 6MWT and gait velocity.