LABORATORY VERSUS ECOLOGICAL RUNNING: A COMPARISON OF FOOT STRIKE ANGLE AND PATTERN ESTIMATION

The purpose of the current study was to evaluate the ecological validity of two previously developed laboratory-based random forest machine learning models and train two new ecologically valid models for the 1) prediction of foot strike angle (FSA) and 2) classification of foot strike pattern (FSP) from wearable insoles during running. The original models performed worse with track-surface running data inputs than in their original validation (prediction RMSE = 6.84° vs. 3.65°, classification accuracy = 79.5% vs. 94.1%). The new models, trained using track-surface data, improved the estimation of FSA (RMSE = 4.10°) and FSP (accuracy = 84.8%). To ensure estimation accuracy, future models should be trained with respect to the environment/conditions in which they will be implemented