Fig 1.
Toe-in gait reduces the knee adduction moment.
(A) The external moment about the knee is computed using the ground reaction force and lever arm from the knee joint center. Toe-in gait shifts the knee joint center medially and the foot center of pressure laterally in the first half of stance, reducing the KAM. Group average data [18] illustrate how (B) toe-in gait shortens the lever arm and (C) reduces the KAM. Ground reaction force magnitude (not shown in the figure) does not change.
Fig 2.
This study focused on building a regression model that uses minimal clinical data to predict the extent of first peak KAM reduction after toe-in gait retraining. Given the lack of large datasets that contain both baseline and toe-in gaits for the same patient, we generated this dataset synthetically. Gait patterns of knee joint center (KJC) and foot center of pressure (FCP) learned from a ground-truth dataset (N = 12) with both baseline and toe-in gait trials (2.1, Stanford University dataset) enabled the creation of extensive synthetic toe-in data (N = 138) from a dataset that contained only baseline gait trials (2.2, Calgary Running Injury Clinic dataset). A regression model using height, weight, walking speed, limb alignment, baseline FPA, and toe-in FPA was then built to predict KAM reduction (2.3). Both the synthetic data generation approach and the predictive model were tested using data (N = 15) collected by a separate research group (2.4, Carnegie Mellon University dataset).
Table 1.
Summary demographics for participants included in each dataset.
Fig 3.
Synthetic gait generated via learned patterns.
At each toe-in angle from 1° to 10°, all subject trajectories for the foot center of pressure and knee joint center (KJC) were binned, averaged, and fit with a spline. (A) At a given toe-in angle, the trajectory represents the positional offset from baseline gait. (B) Here, knee joint center position in the mediolateral direction was predicted for a representative subject with a 5° toe-in angle by adding the learned offset to the baseline trajectory.
Fig 4.
Validation of toe-in KAM trajectories and first peak KAM reductions (Stanford University dataset).
With leave-one-out cross-validation, the synthetic toe-in KAM trajectory (red, dashed line) from the Stanford University dataset closely matched the ground-truth toe-in KAM (blue, dotted line). Synthetic KAM captured the within-subject reduction in the first peak of KAM relative to baseline (black, solid line) with an MAE of 0.174%BW*HT. The left plot captures mean (±STD) KAM trajectories across subjects, while the right plot shows individual and mean peak KAM with 95% confidence intervals.
Table 2.
Evaluation of the learned gait patterns against ground truth measurements in the Stanford University dataset.
Fig 5.
Prediction of KAM reduction using synthetic training data (Calgary Running Injury Clinic dataset).
Synthetic toe-in data from 108 subjects were used to train the predictive model, which achieved an MAE of 0.0826 (± 0.0628) %BW*HT on the test set of 15 subjects. The line of best fit (dashed line) has an R2 of 0.87. The signed errors (the difference between actual and predicted KAM reduction) of the training and test sets were similarly distributed around zero.
Fig 6.
Validation of synthetic toe-in KAM (Carnegie Mellon University dataset).
The synthetic toe-in KAM trajectory (red, dashed line) closely matched the real toe-in KAM (blue, dotted line). Synthetic KAM captured the within-subject reduction in the first peak of KAM, relative to baseline (black, solid line) with an MAE of 0.170%BW*HT. The left plot captures mean (±STD) KAM trajectories across subjects, while the right plot shows individual and mean peak KAM with 95% confidence intervals.
Table 3.
Validation of the learned gait patterns using the Carnegie Mellon University dataset.
Fig 7.
Validation of the predictive model (Carnegie Mellon dataset).
An independent dataset of toe-in gait from 15 subjects was used to evaluate the predictive model, which achieved an MAE of 0.134%BW*HT (±0.0932%BW*HT). The line of best fit (dashed line) has an R2 of 0.55. The mean signed error of the model was not significantly different between the Carnegie Mellon University test data and the synthetic training or synthetic testing data.