Real-time continuous assessment of fatigue from surface electromyography with deep learning for training load regulation in elite cyclists.

backgroundAccurate 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.

methodsFourteen 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.

resultsThe 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% relative to time-only regression. Ablation studies revealed that removing the CNN or Bi-LSTM modules caused marked performance degradation, whereas removing the attention mechanism minimally affected prediction accuracy but enhanced model interpretability.

conclusionsThe proposed CNN-Bi-LSTM-attention framework enables accurate, continuous fatigue estimation from raw sEMG during high-intensity cycling, offering a promising tool for real-time fatigue monitoring and training load regulation in all-out cycling sprint.