Annals of Biomedical Engineering

, Volume 47, Issue 5, pp 1191–1202 | Cite as

Using Gait Variability to Predict Inter-individual Differences in Learning Rate of a Novel Obstacle Course

  • Sophia Ulman
  • Shyam Ranganathan
  • Robin Queen
  • Divya SrinivasanEmail author


This study aimed to determine whether inter-individual differences in learning rate of a novel motor task could be predicted by movement variability exhibited in a related baseline task, and determine which variability measures best discriminate individual differences in learning rate. Thirty-two participants were asked to repeatedly complete an obstacle course until achieving success in a dual-task paradigm. Their baseline gait kinematics during self-paced level walking were used to calculate stride-to-stride variability in stride characteristics, joint angle trajectories, and inter-joint coordination. The gait variability measures were reduced to functional attributes through principal component analysis and used as predictors in multiple linear regression models. The models were used to predict the number of trials needed by each individual to complete the obstacle course successfully. Frontal plane coordination variability of the hip-knee and knee-ankle joint couples in both stance and swing phases of baseline gait were the strongest predictors, and explained 62% of the variance in learning rate. These results show that gait variability measures can be used to predict short-term differences in function between individuals. Future research examining statistical persistence in gait time series that can capture the temporal dimension of gait pattern variability, may further improve learning performance prediction.


Movement variability Kinematics Inter-joint coordination Vector coding Motor learning 



Standard deviation


1st metatarsal head


5th metatarsal head






Femoral condyle


Anterior superior iliac spine


Posterior superior iliac spine


Iliac crest


Greater trochanter


L4/L5 inter-vertebral space


Thigh cluster


Shank cluster


Coupling angle


Coupling angle variability


Principal component analysis


Principal component


Multiple linear regression model using the principal components from stride characteristic variability measures only


Multiple linear regression model using the principal components from stance phase kinematic variability measures only


Multiple linear regression model using the principal components from swing phase kinematic variability measures only


Overall regression model using predictors that were selected by a step-wise regression model which included principal components from all three gait variability models


Trial completion time on the first obstacle course trial


Explained variance


First principal component of the principal component analysis that used stride characteristics only


Second principal component of the principal component analysis that used stride characteristic variability measures only


First principal component of the principal component analysis that used stance phase kinematic variability measures only


Second principal component of the principal component analysis that used stance phase kinematic variability measures only


Third principal component of the principal component analysis that used stance phase kinematic variability measures only


First principal component of the principal component analysis that used swing phase kinematic variability measures only


Second principal component of the principal component analysis that used swing phase kinematic variability measures only


Third principal component of the principal component analysis that used swing phase kinematic variability measures only



The authors would like to acknowledge the contributions of Melanie Kangelaris and Willow Ruud for their assistance with data collection and processing.

Conflict of interest

No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript.


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Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  • Sophia Ulman
    • 1
  • Shyam Ranganathan
    • 2
  • Robin Queen
    • 3
  • Divya Srinivasan
    • 1
    Email author
  1. 1.Department of Industrial and Systems EngineeringVirginia TechBlacksburgUSA
  2. 2.Department of StatisticsVirginia TechBlacksburgUSA
  3. 3.Department of Biomedical Engineering and MechanicsVirginia TechBlacksburgUSA

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