Key points are not available for this paper at this time.
Anterior cruciate ligament (ACL) injuries are common, and re-injuries remain high despite advances in rehabilitation. Return-to-sport (RTS) assessments focus on strength, clinical and hop tests, and time-based criteria but often exclude objective movement quality measures. Biomechanical deficits during jump-landings can persist post-reconstruction, contributing to re-injury risk. Fatigue further alters neuromuscular control, potentially exacerbating risk-related movement patterns, yet most RTS tests are conducted in non-fatigued states. This study introduces a motion dataset of 2199 trials across six bilateral (countermovement jump, drop jump) and unilateral (forward hop, countermovement jump, cross-over hop, 90° medial rotation hop) jump-landing tasks, performed under fatigued and non-fatigued conditions. The dataset includes 3D motion capture and ground reaction force data, including full-body inverse kinematics data (joint angles: knee, hip flexion, abduction, rotation, ankle flexion, trunk and pelvis) processed in OpenSim software for 43 participants comprising individuals with prior ACL injury (n = 21) and healthy controls (n = 22). The dataset enables detailed analyses of jump-landing biomechanics under fatigue, aiming to improve RTS decision-making to reduce re-injury risk.
Calisti et al. (Wed,) studied this question.