Zhiguan Huang

Background: The lunge step in badminton involves frequent impacts that contribute to lower-limb injuries. Although arch support insoles (ASI) are widely used for sports protection, studies on their application in badminton are limited, and internal loading during the lunge maneuver has rarely been examined. This study aimed to investigate the immediate effects of functional arch support insoles on lower-limb biomechanics and patellofemoral joint loading during a forward lunge in badminton. Methods: Sixteen students from a badminton class at the University of Physical Education performed a forward lunge using both original and arch support insoles. Kinematic and kinetic data were collected using a motion capture system and a force platform, while patellofemoral joint loading was estimated via a mathematical model based on cadaveric data. Results: Compared with the original insole, the arch support insole results in a smaller knee external rotation angle ( P = 0.030, ES = 0.60), a larger ankle dorsiflexion angle at initial contact ( P = 0.024, ES = 0.63), and a peak knee external rotation angle during the phase ( P = 0.042, ES = 0.55); they also produce a reduced vertical ground reaction force loading rate ( P = 0.003, ES = 0.90) and peak negative ankle power ( P = 0.026, ES = 0.62). Additionally, peak patellofemoral joint stress ( P = 0.006, ES = 0.81) and stress-time integral ( P = 0.019, ES = 0.66) were significantly increased. Conclusion: The results indicate that ASI altered ankle cushioning strategies, producing lower vertical ground reaction force loading rates. Furthermore, changes in hip and knee metrics suggest a potential reduction in dynamic knee valgus patterns. These results suggest that ASI may be able to reduce injury risk in badminton. However, ASI may also increase patellofemoral joint loading due to greater knee extension moments. Thus, balancing injury prevention and potential risks is crucial. Future research should investigate individualized fit strategies and the long-term biomechanical effects of ASI.

Objective: This exploratory study aimed to investigate how shooting distance is associated with the adaptations in lower limb inter-joint coordination of male university basketball athletes during the Loading and Jump Phases. Methods: Kinematic data from 14 university basketball athletes performing jump shots at four distances (4.6 m, 5.8 m, 7.0 m, and 8.325 m) were collected using OpenCap. Inter-joint coordination was quantified using a vector coding technique based on angular velocity data. This approach was selected to conform with biomechanical conventions for calculating angular dynamics, preserve more temporal information, and make the measure more robust to any noise that may be present in the data. Subsequently, the adaptations were analyzed using Bayesian circular mixed-effects models. Results: Analysis of the data showed a primary adaptation in the Jump Phase: as shooting distance increased, coordination showed a trend towards greater Distal (knee and ankle) Dominancy. This change was underpinned by interaction effects (95% HPD that did not contain zero) between shooting distance and movement phase in multiple lower-limb joint couplings. Alongside this primary adaptation, an asymmetrical adjustment in the bilateral Knee-Ankle coordination during the Loading Phase emerged: with increasing distance, the right side showed enhanced Distal Dominancy while the left side trended towards Proximal Dominancy. Conclusion: Based on these observed patterns, we propose two hypotheses for future confirmatory research: (1) that this trend towards greater Distal Dominancy may reflect a functional optimization of the lower limb for long-range shooting, and (2) that the observed asymmetry might be a functional adaptation to the specific demands of the shooting motion.

Objectives: This study aimed to examine the effects of an 8-week resistance training (RT) or complex training (CT) program on lower limb biomechanical characteristics during the backhand forward lunge in male amateur badminton players.Methods: Twenty male amateur badminton players were randomly assigned to either a complex training group or a resistance training group for an eight-week intervention. Lower limb kinematics and dynamics were captured before and after the intervention using an eight-camera Vicon motion system and two AMTI force plates. The measured variables included phase time, joint angles, joint range of motion (ROM), and joint moments during the stance phase. Discrete variables were analyzed using Generalized Estimating Equations (GEE), while continuous variables were evaluated using Statistical Parametric Mapping 1D (SPM1D).Results: The RT group significantly reduced recovery phase time (0.3 vs. 0.28 s, p < 0.01) and increased ankle transverse plane ROM (16.83° vs. 20.61°, p = 0.005), along with improvements in hip and knee flexion angles and ankle plantarflexion angle. The CT group significantly reduced both braking phase time (0.32 vs. 0.28 s, p = 0.002) and recovery phase time (0.31 vs. 0.28 s, p < 0.01), as well as decreased knee sagittal ROM (83.30° vs. 78.84°, p = 0.04).Conclusion: Both training interventions enhanced the performance of the backhand forward lunge. Complex training resulted in greater improvements in execution efficiency, while resistance training not only improved efficiency but also demonstrated potential for reducing knee and ankle injury risk.

Background: Forehand and backhand forward lunges are frequently performed in badminton, placing significant demands on the lower limbs. The purpose of this study was to examine the differences in lower limb biomechanics between these two lunge types in female amateur players.Methods: This study involved 17 female amateur badminton players performing forehand and backhand forward lunges. Lower limb kinematics and dynamics were recorded using an eight-camera Vicon motion capture system and two AMTI force plates. Variables such as joint angle, range of motion, stiffness, and ground reaction forces measured during the stance phase were analyzed using paired t-tests. To account for the one-dimensional nature of joint angles, moments, and ground reaction forces, the analysis was performed using paired sample t-tests in Statistical Parametric Mapping 1D.Results: The forehand lunge exhibited a smaller hip flexion angle, greater hip internal rotation angle, and increased hip stiffness compared to the backhand lunge. The backhand lunge, in contrast, demonstrated a higher ankle varus angle and greater transverse plane hip range of motion. SPM1D analysis revealed significant differences in both the early (0%–10%) and late (80%–100%) phases of the stance phase. In the early phase, the backhand lunge showed a larger internal rotation moment at the hip, an external rotation moment at the knee, and a smaller knee extension moment. In the late phase, the forehand lunge revealed greater internal rotation moments at the hip, external rotation moments at the knee, ankle valgus moments, and smaller knee flexion moments.Conclusion: The backhand lunge requires greater hip internal rotation than the forehand lunge. Additionally, it is associated with higher ankle varus angles, which may increase the risk of ankle injuries. In contrast, the forehand lunge demonstrates greater hip stiffness, potentially reflecting an adaptation of the lower limb to varying directional demands. These findings emphasize the importance of incorporating targeted ankle and hip training exercises into conditioning programs.

In this paper, a novel type of feed-forward neural network with a simple structure is proposed and investigated for pattern classification. Because the novel type of forward neural network’s parameter setting is mirrored with those of the Extreme Learning Machine (ELM), it is termed the mirror extreme learning machine (MELM). For the MELM, the input weights are determined by the pseudoinverse method analytically, while the output weights are generated randomly, which are completely different from the conventional ELM. Besides, a growing method is adopted to obtain the optimal hidden-layer structure. Finally, to evaluate the performance of the proposed MELM, abundant comparative experiments based on different real-world classification datasets are performed. Experimental results validate the high classification accuracy and good generalization performance of the proposed neural network with a simple structure in pattern classification.