Task Coverage and Generalization of Contact-Aligned Kinematic Estimates of Early Landing Vertical Force

The contribution of contact-aligned kinematics to landing-force estimation requires evaluation against simple task-based predictions. We analyzed 2191 trials from 43 participants across six jump-landing tasks and two fatigue conditions. The outcome was peak total vertical ground reaction force within 150 ms of the assigned contact event, normalized to body weight (BW). Participant-disjoint nested cross-validation evaluated 160 kinematic features, fixed model families, and a procedure selecting the family and hyperparameters through inner validation. ElasticNet achieved a participant-balanced mean absolute error of 0.662 BW, compared with 0.608 BW for a training-derived task-specific median; all four learning procedures had higher error. Target-task exclusion increased AdaptiveML’s six-task macroaveraged error by 0.113 BW (95% confidence interval, 0.076–0.150) relative to an equal-size training control retaining the target task. Fatigued-to-non-fatigued transfer increased its error by 0.022 BW (adjusted p=0.0096); the reverse estimate was −0.018 BW (adjusted p=0.1684). The directional contrast varied across model families. A broader engineered representation improved Ridge performance but did not clearly improve on the task-specific median. Task coverage affected estimator performance, while these models did not demonstrate additional accuracy beyond task-based references. The analyses concern retrospective estimation from processed, force-aligned kinematics and do not establish prospective or clinical validity.