哈尔滨体育学院

Rapid single-leg landings and rebounds are central to high-performance handball, yet little is known about neuromuscular coordination adaptations to changing mechanical demands under such conditions. This study examined how different landing heights would modulate lower limb muscle synergy patterns in elite male handball players performing single-leg drop jumps. Twenty professional athletes executed rebound jumps from five standardized heights (0.15-0.75 m). Kinematic data, ground reaction forces, and surface electromyography (sEMG) from seven lower limb muscles were collected and synchronized. Muscle synergies were extracted using non-negative matrix factorization (NMF), while the temporal characteristics of activation patterns were analyzed with SPM1d. Across landing heights, synergy dimensionality remained stable while both muscle weightings and phase-specific activation (pre-activation, buffering, propulsion) were systematically modulated. These height-dependent adjustments were consistent with redistribution within existing motor modules rather than isolated muscle-specific changes. Practically, programming unilateral plyometrics at ~0.60 m may elicit the most pronounced, yet controlled, adaptation of braking and push-off strategies relevant to impact-force management.

This study proposes an intelligent evaluation system for traditional sports movements based on generative artificial intelligence (GAI) and large-scale generative adversarial network (BigGAN). The system constructs a multimodal analysis framework integrating biological features and kinematic features and establishes a systematic movement evaluation index system. Based on BigGAN, the system implements a movement generation and visual reconstruction module to improve the intuitiveness and interpretability of evaluation. Experimental results show that, in the movements generated and evaluation results obtained by this system, the average absolute error of joint angles is 2.73 degrees, the root mean square error (RMSE) of trajectories is 14.36mm, the movement smoothness is 0.87, and the deviation of muscle synergy activation index is 0.082. These indicators are all superior to those of existing mainstream methods, including dynamic time warping (DTW), long short-term memory (LSTM), temporal convolutional network (TCN), and generative adversarial network (GAN). The system has obtained recognition from professional coaches in terms of visual authenticity and error recognition accuracy. Its comprehensive scores for all movements are higher than 3.7. In addition, the system maintains stable performance under noise interference and its response time is controlled within 182 milliseconds. Compared with existing methods, this method achieves better performance in quantitative indicators, and further improves the systematicness, interpretability and practicability of movement evaluation through cross-modal fusion and high-fidelity visual feedback strategies. This study provides a new technical path for the intellectualization of sports training.

This study examined the psychological mechanisms that underlie the use of MIDI accompaniment for learning and its impact on perceptual-motor synchronization, temporal structuring, and engagement dynamics. The main task was to compare the effectiveness of Guide Mode with conventional MIDI accompaniments in structured piano instruction. The study employed a mixed-methods approach, incorporating pre-test and post-test assessment of performance, real-time MIDI tracking analytics, and self-report motivation scales. The experiment involved a group of 48 s-year piano students (with a gender ratio of 1:1). The sample was divided into two experimental groups (Guide Mode vs. MIDI accompaniment only), with data triangulated through quantitative error tracking (pitch and rhythm deviation), qualitative observations from the instructor, and self-esteem mapping based on a survey. Statistical analysis (ANOVA, t-tests, and Cronbach’s alpha for reliability assessment) was used to identify inter-group differences and intra-individual learning trajectories. The Guide Mode group demonstrated excellent adherence to the prescribed tempo (an average improvement in tempo accuracy of 14.2

Human–machine interfaces are increasingly vital for health monitoring and human–computer interaction. However, their practical application is hindered by challenges such as material swelling, delamination, and signal distortion caused by sweat immersion and mechanical strain. This review systematically examines recent breakthroughs in multiscale design strategies for anti‐swelling hydrogels, encompassing molecular‐scale crosslinking network regulation, micro/nano‐scale confinement design, and macro‐scale device integration. Through approaches including hydrophobic modification, dynamic bond introduction, nanocomposite integration, and gradient structure fabrication, the structural integrity, consistent electromechanical performance, and biocompatibility of hydrogels in complex physiological environments have been significantly improved. The functional implementation and efficacy of these materials are explored in applications such as motion sensing, wearable rehabilitation devices, and implantable neural interfaces, highlighting their advantages in maintaining high signal‐to‐noise ratios, low impedance fluctuations, and stable long‐term adhesion under wet or mechanically dynamic conditions. Finally, this paper proposes that future research should focus on bio‐inspired intelligent structures, dynamically responsive interfaces, and heterogeneous integration technologies to advance anti‐swelling hydrogels toward clinical‐grade, highly robust human–machine interaction systems.

ObjectiveBased on an epidemiological investigation of 2-h of daily physical activity among primary and secondary school students across China's seven major administrative regions, this study analyzes the association between social support and cognitive factors, thereby providing recommendations for policy formulation.MethodsA cross-sectional study was conducted using random cluster sampling across seven Chinese administrative regions from May to July 2025. Data were collected using standardized scales measuring physical activity levels, achievement of the daily 2-h physical activity target, academic stress, and health cognition. Statistical analyses, including descriptive statistics, chi-square tests, and multivariable logistic regression, were performed using SPSS 26.0.Results(1) The prevalence of insufficient 2-h daily physical activity showed significant differences across various demographic variables (all p < 0.001). Higher prevalence rates were observed among students in rural areas (30.50%), private schools (29.41%), boarding students (30.03%), and those with lower frequency of reunions with parents. (2) Key risk factors identified included lack of health cognition, low perceived value of physical activity, lack of sports facilities, insufficient allocated physical education time, and lack of access to smart devices. (3) Significant urban-rural/regional disparities were found regarding access to smart devices (t = 3.142, p = 0.002) and academic stress levels (t = 2.499, p = 0.012).ConclusionHealth cognition, resource availability, and time allocation are significant factors associated with the insufficiency of daily 2-h physical activity among Chinese primary and secondary school students. The education department has increased the guarantee of student system construction, and has implemented differentiated management for higher grades.