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

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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.

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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

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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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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA

    Sophia Ulman & Divya Srinivasan

  2. Department of Statistics, Virginia Tech, Blacksburg, VA, USA

    Shyam Ranganathan

  3. Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA

    Robin Queen

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  1. Sophia Ulman
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  2. Shyam Ranganathan
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  4. Divya Srinivasan
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Correspondence to Divya Srinivasan.

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Associate Editor Jane Grande-Allen oversaw the review of this article.

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Ulman, S., Ranganathan, S., Queen, R. et al. Using Gait Variability to Predict Inter-individual Differences in Learning Rate of a Novel Obstacle Course. Ann Biomed Eng 47, 1191–1202 (2019). https://doi.org/10.1007/s10439-019-02236-x

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