ArticleFrontiers in physiology2026
Feasibility of explainable machine learning for analyzing drop landing strategies: effects of landing height and fatigue.
Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The drop landing (DL) process is a complex neuromuscular control process influenced by multiple factors, among which fatigue state and landing height play significant roles. This study aimed to investigate the influence mechanisms of these two factors on lower-limb landing strategies and to examine the feasibility of explainable machine learning methods in analyzing landing strategies. Joint angles and joint moments of the hip, knee, and ankle in the sagittal plane, as well as vertical ground reaction force (vGRF), were collected under different landing conditions (40 cm and 80 cm heights and their corresponding fatigue states). Cohen's d effect size analysis was applied to examine group differences in each feature across the time series, and seven classification models were constructed based on multi-dimensional biomechanical features to identify different landing states. Meanwhile, SHapley Additive exPlanations (SHAP) method was employed to analyze feature importance and interactions. Results showed that the Light Gradient Boosting Machine achieved classification accuracies above 0.915 across all four tasks, verifying the feasibility of machine learning methods for landing strategy analysis. Temporal effect size analysis revealed that differences induced by changes in landing height were mainly concentrated in the early landing phase (0%-40%). In contrast, the fatigue effect resulted in a wider distribution and higher magnitude of regions with large effect sizes. SHAP analysis indicated that the knee joint moment had the highest importance across all classification tasks, vGRF contributed prominently in fatigue-related tasks, and feature interaction patterns differed significantly across tasks. In conclusion, explainable machine learning methods can effectively identify and explain differences in DL strategies under various landing conditions, providing a theoretical basis and methodological support for sports injury risk assessment and training strategy optimization.
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