Biomechanics

Background/Objectives: Wheelchair fencing requires effective trunk coordination because athletes cannot use the lower limbs for propulsion. This exploratory study investigated whether coach-guided scapular-control instruction was associated with differences in spinal kinematics during fencing lunges. Methods: Seven competitive wheelchair fencers performed six lunges under Natural and Coach-guided conditions. Four inertial measurement units were positioned at C7–Th1, Th6–Th7, Th12–L1, and L5–S1. Eight kinematic outcomes were analyzed using linear mixed-effects models with Condition, Segment, and their interaction as fixed effects and Participant as a random intercept. Repeated lunges were averaged within participant, condition, and segment, resulting in 56 observations. Benjamini–Hochberg FDR correction was applied across 24 omnibus tests, followed by Holm correction for post hoc segment comparisons. Results: Three effects remained significant after FDR correction. RMS angular acceleration differed between conditions (F(1,42) = 17.00, p < 0.001, q = 0.004, η²p = 0.288). Significant Segment effects were observed for peak angular jerk (F(3,42) = 5.00, p = 0.005, q = 0.038, η²p = 0.263) and time to peak angular velocity (F(3,42) = 5.33, p = 0.003, q = 0.038, η²p = 0.276). Holm-adjusted comparisons showed significant differences between L5–S1 and Th6–Th7 for peak angular jerk (pHolm = 0.010) and time to peak angular velocity (pHolm = 0.004). No Condition × Segment interaction survived FDR correction. Conclusions: The findings indicate condition-associated differences in RMS angular acceleration and segment-related differences in peak angular jerk and timing of peak angular velocity. Given the small sample and fixed condition order, these findings should be interpreted as exploratory associations rather than evidence of a causal coaching effect.

Background: The jump shot is a critical technical element in modern handball, requiring the complex integration of speed, power, and precision. As the game evolves toward higher intensities, traditional coaching models require validation against objective biomechanical data. This study aimed to investigate the kinematic characteristics of the jump shot in elite players and establish a methodological framework for transitioning raw kinematic parameters into expert-based performance models. Methods: Kinematic analysis was conducted on five elite male handball players (n = 5, 15 total trials), focusing on the approach, flight, and release phases. Additionally, ten highly qualified handball experts (N = 10) independently established a theoretical performance model. To enable direct comparison between objective measurements and expert assessments, kinematic frequencies were linearly normalized onto a five-point ordinal continuum. The relationship between the kinematic model and the hypothetical expert model was determined using Spearman’s rank correlation coefficient. “Kinematic analysis was conducted on elite handball players, focusing on the approach, flight, and release phases. Results: Elite players predominantly utilize a two-step approach, with the first step slightly longer than the second to optimize the kinematic chain. Kinematic data and expert evaluations showed high congruency regarding body posture, highlighting the necessity of diagonally aligned shoulders and a forward-leaning trunk during the flight phase. A statistically significant correlation was observed between objective kinematic parameters and expert ratings, confirming the model’s validity. Conclusions: This study demonstrates a novel approach for evaluating technical performance by bridging the gap between biomechanical data and expert intuition. These findings provide a valuable exploratory foundation for automated technical evaluation systems and offer practical insights into contemporary technical trends for elite-level coaching in the rapid tempo of modern handball.

Background: Asymmetrical gait refers to an unequal weight-bearing and movement pattern between the two sides of the lower limbs, commonly seen in patients with stroke or cerebral palsy. There is little research exploring how to identify asymmetrical gait using neural network (NN) models. This study aimed to investigate whether NN models would detect asymmetrical gait using marker coordinates around the pelvis. Methods: Twenty healthy volunteers aged between 23 and 35 participated in the study and performed a simulated hemiplegic gait with one leg supported and the other leg straight and swinging laterally; they also walked as normal as a control gait. The four markers around the pelvis were collected via a motion capture system. The movements in the centre of the pelvis (CPL) were calculated as the ranges of motion in 3D space. An NN model was constructed to identify gait modes using CPL moving ranges. Results: The NN model identified gait modes quickly and efficiently with correction rates of approximately 99.56% and 97.61% for asymmetrical and normal gait modes, respectively. The area under the Relative Operating Characteristic curves was approximately 0.992. The movements in the sagittal plane were the most important features contributing to NN modelling. The ratio of the CPL’s moving area in the transverse plane to the product of stride length and step width was higher in the asymmetrical gait than in normal gait. Conclusions: This study showed that the NN models can identify gait modes using a few of the markers in the pelvis. The study highlights NN application in gait recognition and indicates that the NN models could be applied in motion analysis, especially gait analysis for people with asymmetrical conditions.