ArticleScientific data2025
Motion capture data of six jump-landings, fatigued and non-fatigued, after anterior cruciate ligament injury.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Task dependent early landing kinematics after return to sport in individuals with ACL reconstruction: a secondary analysis.BMC musculoskeletal disorders · 2026Article
- [Chinese expert consensus on anterior cruciate ligament reconstruction using the over-the-top technique (2025 edition)].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026Article
- Motion capture data of six jump-landings, fatigued and non-fatigued, after anterior cruciate ligament injury.Scientific data · 2025Article
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3 authors.
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Abstract
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.
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