Markerless motion capture systems, such as Theia3D, offer a promising alternative to traditional marker-based systems (e.g., Vicon) not only for dynamic motion analysis, but also potentially for postural control research. The purpose of this study is to simultaneously derive and then compare principal movements (PMs) from Theia3D and Vicon data collected from 13 volunteers during eyes-open and eyes-closed bipedal quiet stance using principal component analysis (PCA). Agreement between systems was assessed by computing correlation coefficients for corresponding PM time series and evaluating their sensitivity to visual condition changes. Theia3D and Vicon produced highly similar PM structures, with the first two PMs-representing anterior-posterior ankle-strategy and medio-lateral sway-exhibiting near-perfect correlation (r = 99.8 % and r = 99.0 %) and explaining a comparable portion of the entire postural variance (PM1: 74 % vs. 76 %; PM2: 9.4 % vs. 9.9 % for Vicon and Theia3D results, respectively). The first seven PMs, which collectively accounted for 97 % of postural variance, showed strong agreement (r = 0.814 to 0.928) despite fundamental differences in marker placement and tracking methodology. Correlation declined in higher-order PMs, suggesting they capture distinct aspects of postural movements or system-specific noise. Both systems demonstrated comparable sensitivity to postural control modulations induced by visual deprivation. Notably, Theia3D exhibited higher noise levels, particularly in higher-order PMs, indicating that pre-processing with filtering should be considered before PCA. Overall, these findings confirm that Theia3D provides highly comparable results to Vicon for postural PMs, reinforcing its potential as a valid and efficient alternative for postural control research using PCA-based approaches.

Previous research has documented brain plasticity after an anterior cruciate ligament (ACL) tear and suggests that these neural adaptations contribute to poorer motor control. Since both brain hemispheres show adaptations, we hypothesized that reduced dynamic stability occurs not only in the injured, but also the contralateral, uninjured leg. Further, given brain hemispheric specialization’s impact on motor coordination, we hypothesized the need to consider the injury side. A total of 41 female athletes and 18 controls performed single-leg jump-landings. Dynamic postural stability was measured as time-to-stabilization (TTS). We found reduced medio-lateral dynamic stability for the ACL injured leg (p = 0.006) with a similar trend for the contralateral leg (p = 0.050) compared to the control group. However, when distinguishing between injuries to the dominant and non-dominant legs, we found increased medio-lateral TTS only if the injury had occurred on the dominant side where landings on injured (p = 0.006) and contralateral (p = 0.036) legs required increased TTS. Assessments of dynamic stability, e.g., in the context of return-to-sport, should consider the injury side and compare results not only between the injured and the contralateral leg, but also to uninjured controls. Future research should not pool data from the dominant-leg ACL with non-dominant-leg ACL injuries when assessing post-injury motor performance.

In view of the frequent use of the portable balance board (Tymo^®, Tyromotion, Graz, Austria) in neurorehabilitation, the objective of this study is to assess its posturographic validity and reliability in comparison to a floor-embedded force platform (AMTI, Watertown, MA, USA), as well as its clinical validity in stroke patients. To evaluate posturographic validity, thirty healthy participants were tested in four different measurement conditions (M1–M4: standing with/without a balance pad, eyes open/closed) while simultaneously measuring with both devices placed on top of each other. Clinical validity is obtained by correlating stroke patients’ Tymo^® outcome variables (path length and ellipse) of M1–M4 with Berg Balance Scale (BBS) results. Reliability is determined by correlating repeated measurements. The Center of pressure (COP) paths of both force plates show a very strong correlation. The repeated measurement reliability of both force plates is comparable. Tymo^® outcome variables exhibit moderate to strong correlation with the BBS and demonstrated repeated measurement reliability analogous to healthy participants. Therefore, the Tymo^® device demonstrates measurement accuracy comparable to a gold-standard device while offering the advantages of portability and user-friendly software. Tymo^® is suitable for assessing balance in stroke patients, though protocol adaptations and averaging repeated measurements are recommended to enhance feasibility and reliability.

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.

Accurately identifying residual biomechanical deficits following an anterior cruciate ligament injury is critical for effective rehabilitation and safe return to sport. This study aimed to determine which of four jump‐landing tasks demonstrated the greatest sensitivity in distinguishing individuals with a history of ACL injury from healthy controls. Forty‐three participants formed the ACL ( n = 21, 11 females) and the control group ( n = 22, 12 females). Three‐dimensional motion data (Vicon, 250 Hz) were recorded during a single‐leg hop, unilateral countermovement jump, unilateral crossover hop, and medial‐rotation hop before and after a fatigue‐inducing intervention (single‐leg squats and step‐ups). Logistic regression models to classify participants were built using 13 lower‐body, trunk, and pelvis joint angles at 50 ms after initial ground contact, angular changes in these angles between 50 and 80 ms, and principal components derived from these variables. Classification rates and individual classification outcomes were assessed. The results revealed that no single jump‐landing task consistently outperformed others in detecting ACL injury history. Classification outcomes were influenced by fatigue state and analytical approaches. Fatigue was found to enhance classification rates. Combining joint angles with their temporal changes improved classification rates compared to using joint angles alone. However, applying principal component analysis as a preprocessing step did not consistently enhance model performance. Overall, the study demonstrated that jump‐landing tasks, combined with a variety of analytical approaches, can effectively detect ACL injury history. Fatigue enhanced classification outcomes, suggesting that it amplifies differences between post‐injury and healthy movement characteristics.

Introduction & Purpose In fast-paced clinical decision-making, jump-landing movement assessments are rarely used to guide return-to-sport (RTS) decisions after an anterior cruciate ligament (ACL) injury. While these tests are crucial for identifying deficits in movement execution (e.g., dynamic valgus, extended knee, knee internal rotation) that could predispose to ACL re-rupture (Kaplan & Witvrouw, 2019), identifying these deficits can be challenging. To integrate RTS movement assessments into clinical practice, the first step is to identify the most sensitive jump-landing test that can best expose remaining deficits in neuromuscular control and resulting landing kinematics associated with ACL injury. Further, RTS tests are mostly performed in a non-fatigued state although fatigued individuals often fail to reach the RTS criteria (Dingenen & Gokeler, 2017). This exploratory study aimed to rank common jump-landing tests in their ability to identify (sensitivity and classification rate) deficits in whole-body landing kinematics following ACL injury, under both fatigued and non-fatigued conditions. Methods A total of 43 volunteers were recruited into ACL group (n = 21, 11 females, 3.9 years post ACL injury) and control group (n = 22, 12 females). 3D motion data (Vicon, 250 Hz) were recorded during a single-leg forward hop (SH), single-leg countermovement jump (CMJ), single-leg triple crossover hop (COH), and single-leg 90° medial rotation hop (MRH) before and after a fatigue-inducing intervention (single-leg squats and step-ups). Thirteen joint angles (50 ms after initial contact) from the lower body, trunk, and pelvis representing the landing kinematics were calculated in OpenSim. A correlation analysis was performed to check for multicollinearity issues. Eight distinct logistic regression models (four jumps, fatigued/non-fatigued, α = .05) were computed to predict ACL injury history based on a combination of joint angles. For each model, a backward stepwise method was employed to determine which combination of joint angles resulted in the highest classification rates. Results ACL injury history was successfully predicted in seven (χ² > 4.4, p < .036) of the eight logistic regression models (Figure 1). The SH in the fatigued condition (χ² = 22.5, p = .008) demonstrated the highest sensitivity of 85.7% and a classification rate of 86%. Significant predictors for this model were knee rotation (Odds ratios [OR] = 1.537), hip abduction (OR = 1.385), hip rotation (OR = 1.525), and ankle flexion (OR = 0.771). The CMJ (non-fatigued) showed the second-highest sensitivity of 81% and a classification rate of 81.4% (p = .027). Significant predictors were knee rotation (OR = 1.440), hip abduction (OR = 1.428), hip rotation (OR = 1.297), and pelvis rotation (OR = 0.664). The MRH in the non-fatigued condition exhibited the lowest sensitivity, and no classification was possible in the non-fatigued COH model (χ² = 3.6, p = .056). Discussion & Conclusion The fatigued SH was the most sensitive jump-landing test for classifying groups and detecting kinematic differences due to previous ACL injury. Thus, we recommended the SH over other tests for RTS decision-making, as it is most likely to reveal kinematic abnormalities. Higher classification rates in the fatigued condition support RTS testing in fatigued states to better identify movement deficits. To integrate jump-landing assessments into RTS decisions in a feasible way, our future work will involve 3D motion capture using pose estimation algorithms on video data. References Kaplan, Y., & Witvrouw, E. (2019). When is it safe to return to sport after ACL reconstruction? Reviewing the criteria. Sports Health, 11(4), 301–305. https://doi.org/10.1177/1941738119846502 Dingenen, B., & Gokeler, A. (2017). Optimization of the return-to-sport paradigm after anterior cruciate ligament reconstruction: A critical step back to move forward. Sports Medicine, 47, 1487-1500. https://doi.org/10.1007/s40279-017-0674-6