To improve the accuracy of fatigue assessment in patients undergoing upper limb rehabilitation, this study proposes a lightweight fatigue detection algorithm based on ensemble learning and single-channel surface electromyography (sEMG) signals. Thirty healthy subjects without upper limb injuries or severe chronic diseases were recruited, and dynamic sEMG signals of the biceps brachii were collected during dumbbell bicep curls at a sampling frequency of 2048 Hz, yielding 6650 valid experimental samples. To address the class imbalance in the sEMG dataset, the SMOTETomek hybrid sampling algorithm was employed for data balancing. Three ensemble strategies—voting, stacking, and mean fusion—were integrated and combined with four different sets of base classifiers to construct a total of 12 fusion models, and the optimal model was identified through comparative screening. The experimental results demonstrated that, after SMOTETomek sample balancing, the LightGBM-LR-MLP soft voting ensemble model achieved the best overall performance, with the accuracy, recall, precision, and F1-score for both fatigue categories all exceeding 0.93. Multiple statistical analyses, including paired t-tests, effect sizes, and 95% confidence intervals, verified the reliability of the model selection and confirmed that the soft voting algorithm significantly outperformed the other comparison schemes. Machine learning can efficiently interpret dynamic biceps brachii sEMG signals; the proposed method improves the accuracy of fatigue recognition and provides an objective, quantitative fatigue reference index for upper limb rehabilitation training. It holds promise for assisting the dynamic adjustment of rehabilitation training intensity, thereby potentially reducing the risk of overtraining injuries and enhancing the safety of rehabilitation training.