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.
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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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DOI: https://doi.org/10.1007/s10439-019-02236-x