Predicting Knee Adduction Moment Response to Gait Retraining with Minimal Clinical Data

Link to paper

Citation:

Rokhmanova N, Kuchenbecker KJ, Shull PB, Ferber R, Halilaj E (2022) Predicting knee adduction moment response to gait retraining with minimal clinical data. PLoS Comput Biol 18(5): e1009500. https://doi.org/10.1371/journal.pcbi.1009500

About:

Although foot progression angle gait retraining is overall beneficial as a conservative intervention for knee osteoarthritis, knee adduction moment (KAM) reductions are not consistent across patients. Moreover, customized gait interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a 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 public datasets that contain different gaits for the same patient, we present a method to generate toe-in gait data synthetically, and share the resultant trained model.

Getting started:

Data are freely available for download at: https://simtk.org/projects/predict-kam
With the exception of a functional data analysis (FDA) component for smoothing toe-in patterns (Python), all data and code runs in MATLAB. For running FDA, refer to scikit-FDA documentation for installation: https://fda.readthedocs.io/en/latest/

Scripts for processing each institution's data are contained in the folders by their name.
The trained predictive model and learned toe-in patterns to generate synthetic toe-in gait are shared in the Models folder.