Explainable Machine Learning Using Sensor-Derived Biomechanical Features to Classify Elevated VALR-Related Loading Across Midsole Hardness Conditions in School-Aged Boys.

(1)

Background

Changes in midsole hardness may affect lower-limb impact loading during forefoot strike (FFS) running in children, yet the biomechanical basis for discriminating elevated VALR-related loading remains unclear. (2)

Methods

Fourteen school-aged boys performed FFS running tests in experimental shoes with four midsole hardness levels (37, 42, 47, and 52 Shore C). Lower-limb kinematics and surface electromyography (sEMG) data were collected during the dominant leg stance phase. After preprocessing, VALR was calculated from 336 valid trials, and 28 stance-phase biomechanical features were extracted, yielding a final machine-learning dataset of 324 trials after excluding incomplete feature data. VALR was used to compare loading changes and define trial-level elevated-loading labels based on the median VALR value. Classification models were evaluated under participant-level GroupKFold validation, and XGBoost was retained for exploratory SHAP analysis. (3)

Results

VALR showed an upward trend with increasing hardness, but no statistically supported change point was identified. XGBoost achieved an accuracy of 75.93%, precision of 74.14%, recall of 79.63%, F1-value of 0.768, and pooled out-of-fold AUC of 0.738. SHAP analysis indicated that distal and non-sagittal kinematic features contributed most to model classification. (4)

Conclusions

Elevated VALR-related loading during children's FFS running may be characterized by a multi-feature model-based pattern rather than a fixed midsole hardness threshold.