This study aims to develop a predictive model for alertness levels in elite shooting athletes by analyzing heart rate variability (HRV) dynamics under simulated competitive stress. 83 national-level shooting athletes completed a 60-minute Psychomotor Vigilance Task (PVT) protocol designed to mimic the sustained attentional demands of a competition, while HRV data were continuously recorded. Pearson correlation analysis identified HRV features associated with behavioral performance. Key predictors were selected via recursive feature elimination with Random Forest. Four machine learning algorithms-Support Vector Machine (SVM), Random Forest (RF), XGBoost, and AdaBoost-were employed to construct classification models for alertness. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). SHAP analysis was applied to interpret feature contributions. The binary classification framework (optimal vs. sub-optimal alertness) demonstrated superior reliability over multi-class approaches. The AdaBoost model achieved the best performance, with an accuracy of 0.75, an F1-score of 0.73, and an AUC of 0.77. SHAP analysis revealed that the very low frequency percentage (VLF%) was the most critical predictor, followed by the SD2/SD1 ratio. Notably, elevated VLF% values were associated with lower alertness levels. The binary classification model, integrating key HRV indices (notably VLF%) with the AdaBoost algorithm, can effectively distinguish alertness levels in shooting athletes during simulated competitive stress. This approach provides a validated, non-invasive tool for objective psychophysiological monitoring in training, offering actionable insights for pre-competition readiness assessment.
Accurate assessment of exercise-induced muscle fatigue is essential for optimizing training loads and preventing overtraining in elite athletes. This study presents a deep learning framework for continuous fatigue estimation from raw surface electromyography (sEMG) signals during high-intensity cycling. Fourteen elite track cyclists performed a 30-second all-out sprint on a cycle ergometer. Power output was recorded at 1 Hz to derive a continuous fatigue index (percentage decline from peak power). Simultaneously, sEMG signals were recorded at 1000 Hz from four lower-limb muscles. A sliding window approach (3-second windows, 0.25-second stride) was used to construct input samples. A deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (Bi-LSTM), and channel attention mechanisms was developed to predict the fatigue index directly from raw sEMG. Model performance was evaluated using leave-one-subject-out cross-validation with subject-specific fine-tuning, and compared against eight baseline models. Ablation studies were conducted to quantify each component’s contribution. The proposed CNN–Bi-LSTM–attention model achieved a mean absolute error of 0.048 ± 0.019 and a Pearson correlation coefficient of 0.822 ± 0.123, and was the only model yielding a positive coefficient of determination (R² = 0.493 ± 0.249). Compared to baseline machine learning models, the proposed model achieved superior prediction accuracy. Furthermore, it reduced prediction errors by 35.1
We analyzed isometric knee extension strength in 6,761 adults to determine how lower-limb strength varies with age and whether age-related patterns differ by sex. Isometric knee extension strength was positively associated with back strength, grip strength, height, vertical jump, and body weight, and negatively associated with age. Men had substantially higher strength than women. Restricted cubic spline, first-derivative, and piecewise regression analyses showed a clear nonlinear and sex-specific age pattern. In men, the relationship changed around 38 years and the steepest decline occurred around 45 years. In women, age-related decline became more pronounced after 50 years, although the oldest-age derivative estimate was boundary-sensitive. The association remained significant after adjustment for height, weight, and grip strength. These findings support sex-specific monitoring of lower-limb strength and earlier intervention windows across adulthood.
This study aimed to culturally adapt and validate the Sport Emotion Questionnaire (SEQ) for routine monitoring of emotional states in Chinese professional athletes and to identify distinct emotional patterns using latent profile analysis. The original SEQ item pool was translated, culturally and linguistically reviewed, and supplemented with emotion expressions provided by experienced athletes. Classification data from 122 professional athletes were used to construct the 41-item preliminary Sport Emotion Questionnaire for Chinese Professional Athletes (SEQ-CPA), which represented the five original domains. Factor structure was examined using exploratory factor analysis (n = 320) and confirmatory factor analysis (n = 249). Following ACO-based item reduction, the selected model underwent cross-sample CFA, and measurement invariance was tested across gender and sport type. Validity was further examined using measures of emotion, athletic mental energy, psychological strain, and well-being across 527 athlete observations. Latent profile analysis was conducted in the combined EFA and CFA samples (N = 569). Exploratory and confirmatory factor analyses supported three dimensions: anxiety, happiness, and anger. ACO produced the final nine-item SEQ-CPA. The selected model showed good fit across samples, with strict measurement invariance across gender and scalar invariance across individual- and team-sport groups. Internal consistency was satisfactory for all dimensions. The three dimensions showed differentiated relationships with other emotion measures, athletic mental energy, psychological strain, and well-being. Latent profile analysis identified four profiles: happiness, negative state, anxiety, and submerged. Psychological strain was lowest in the happiness profile and highest in the negative state profile. The nine-item SEQ-CPA demonstrated satisfactory reliability and validity for assessing anxiety, happiness, and anger in Chinese professional athletes. It provides a concise measure for routine emotional monitoring, while profile analysis offers a complementary way to interpret the three dimensions in combination.
The drop landing (DL) process is a complex neuromuscular control process influenced by multiple factors, among which fatigue state and landing height play significant roles. This study aimed to investigate the influence mechanisms of these two factors on lower-limb landing strategies and to examine the feasibility of explainable machine learning methods in analyzing landing strategies. Joint angles and joint moments of the hip, knee, and ankle in the sagittal plane, as well as vertical ground reaction force (vGRF), were collected under different landing conditions (40 cm and 80 cm heights and their corresponding fatigue states). Cohen’s d effect size analysis was applied to examine group differences in each feature across the time series, and seven classification models were constructed based on multi-dimensional biomechanical features to identify different landing states. Meanwhile, SHapley Additive exPlanations (SHAP) method was employed to analyze feature importance and interactions. Results showed that the Light Gradient Boosting Machine achieved classification accuracies above 0.915 across all four tasks, verifying the feasibility of machine learning methods for landing strategy analysis. Temporal effect size analysis revealed that differences induced by changes in landing height were mainly concentrated in the early landing phase (0%–40%). In contrast, the fatigue effect resulted in a wider distribution and higher magnitude of regions with large effect sizes. SHAP analysis indicated that the knee joint moment had the highest importance across all classification tasks, vGRF contributed prominently in fatigue-related tasks, and feature interaction patterns differed significantly across tasks. In conclusion, explainable machine learning methods can effectively identify and explain differences in DL strategies under various landing conditions, providing a theoretical basis and methodological support for sports injury risk assessment and training strategy optimization.
