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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
Article

Abstract

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.

Keywords

Movement variability Kinematics Inter-joint coordination Vector coding Motor learning 

Abbreviations

SD

Standard deviation

1MET

1st metatarsal head

5MET

5th metatarsal head

MAL

Malleoli

CAL

Calcaneus

FEM

Femoral condyle

ASIS

Anterior superior iliac spine

PSIS

Posterior superior iliac spine

IC

Iliac crest

GT

Greater trochanter

SAC

L4/L5 inter-vertebral space

TC

Thigh cluster

SC

Shank cluster

CA

Coupling angle

CAV

Coupling angle variability

PCA

Principal component analysis

PC

Principal component

PCR_StrideChar

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

PCR_StanceKin

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

PCR_SwingKin

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

PCR_Overall

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

Initial_Time

Trial completion time on the first obstacle course trial

R2

Explained variance

PC1_StrideChar

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

PC2_StrideChar

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

PC1_StanceKin

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

PC2_StanceKin

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

PC3_StanceKin

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

PC1_SwingKin

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

PC2_SwingKin

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

PC3_SwingKin

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

Notes

Acknowledgments

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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