The kinematic and kinetic performance of tennis players differs across skill levels, with joint range of motion (ROM), moments, and stiffness being strongly linked to injury risk. Focusing on the biomechanical characteristics of lower-limb joints throughout the landing stage, especially among athletes of different skill levels, aids in understanding the link between injury risk and performance level. This study recruited 15 male campus tennis enthusiasts and 15 male professional tennis players. The kinematic and kinetic differences between amateur and professional players during the landing phase of the tennis serve were analyzed using SPM1D 0.4.11 and SPSS 27.0.1, with independent-sample t-tests applied in both cases. Throughout the tennis serve’s landing stage, the professional group exhibited significantly greater sagittal plane hip-joint stiffness (p < 0.001), horizontal plane moment (59~91%; p = 0.036), and a significantly higher peak moment (p = 0.029) in comparison with the amateur group. For the knee joint, the professional group exhibited significantly larger ROM in flexion–extension (0~82%; p = 0.003); along with greater ROM (0~29%; p = 0.042), moment (12~100%; p < 0.001), peak moment (p < 0.001) in adduction-abduction; and internal–external rotational moments (19~100%; p < 0.001) were markedly higher. The professional group showed significantly higher ankle joint ROM (p < 0.001) and moments (6~74%; p = 0.004) in the sagittal plane, as well as greater horizontal-plane ROM (27~67%; p = 0.041) and peak moments (p < 0.001). Compared with amateur tennis players, professional tennis players exhibit greater ROM, joint moments, and stiffness in specific planes, potentially increasing their risk of injury during the landing phase.

Background: Running is a widely practiced physical activity but carries a high risk of injury, with foot structure, particularly the medial arch, playing a vital role in biomechanical performance and injury prevention. As the core of foot support, the arch is essential for absorbing impact, transmitting force, and maintaining dynamic stability. This study aims to compare the dynamic stability of runners with moderate flatfoot and those with normal arches in the initial, steady, and fatigue stages in order to elucidate how fatigue differently affects their dynamic postural control. Methods: Twelve male runners were recruited. Using inertial measurement units (IMUs) and a Zebris treadmill system, data on Maximum Lyapunov Exponent(MLE) and plantar center of pressure (COP) trajectories were collected during the initial, steady-state, and fatigued phases. Results: In the fatigue phase, runners with flatfoot showed an increase of 0.05 s⁻¹ in short-term MLE compared to those with normal arches (p < 0.05), indicating significantly lower stability under fatigue. Conclusions: The deterioration of lower-limb dynamic stability in flatfoot runners is dependent on fatigue. Specifically, their overall lower dynamic stability stems primarily from a marked increase in MLE when entering the fatigued phase. Concurrently, fatigue induces alterations in COP trajectory and temporal gait parameters in flatfoot runners; they signify reduced efficiency in gait control.

(1) Background: Changes in midsole hardness may affect lower-limb impact loading during forefoot strike (FFS) running in children, yet the biomechanical basis for discriminating elevated VALR-related loading remains unclear. (2) Methods: Fourteen school-aged boys performed FFS running tests in experimental shoes with four midsole hardness levels (37, 42, 47, and 52 Shore C). Lower-limb kinematics and surface electromyography (sEMG) data were collected during the dominant leg stance phase. After preprocessing, VALR was calculated from 336 valid trials, and 28 stance-phase biomechanical features were extracted, yielding a final machine-learning dataset of 324 trials after excluding incomplete feature data. VALR was used to compare loading changes and define trial-level elevated-loading labels based on the median VALR value. Classification models were evaluated under participant-level GroupKFold validation, and XGBoost was retained for exploratory SHAP analysis. (3) Results: VALR showed an upward trend with increasing hardness, but no statistically supported change point was identified. XGBoost achieved an accuracy of 75.93%, precision of 74.14%, recall of 79.63%, F1-value of 0.768, and pooled out-of-fold AUC of 0.738. SHAP analysis indicated that distal and non-sagittal kinematic features contributed most to model classification. (4) Conclusions: Elevated VALR-related loading during children’s FFS running may be characterized by a multi-feature model-based pattern rather than a fixed midsole hardness threshold.