Reliable control of myoelectric prostheses is often hindered by high inter-subject variability and the clinical impracticality of high-density sensor arrays. This study proposes a deep learning framework for accurate gesture recognition using only two surface electromyography (sEMG) channels. The method employs a Convolutional Sparse Autoencoder (CSAE) to extract temporal feature representations directly from raw signals, eliminating the need for heuristic feature engineering. On a 6-class gesture set, our model achieved a multi-subject F1-score of 94.3% $\pm$ 0.3%. To address subject-specific differences, we present a few-shot transfer learning protocol that improved performance on unseen subjects from a baseline of 35.1% $\pm$ 3.1% to 92.3% $\pm$ 0.9% with minimal calibration data. Furthermore, the system supports functional extensibility through an incremental learning strategy, allowing for expansion to a 10-class set with a 90.0% $\pm$ 0.2% F1-score without full model retraining. By combining high precision with minimal computational and sensor overhead, this framework provides a scalable and efficient approach for the next generation of affordable and adaptive prosthetic systems.

翻译：肌电假肢的可靠控制常受限于较高的受试者间差异以及高密度传感器阵列在临床上的不实用性。本研究提出了一种仅使用两个表面肌电信号通道即可实现精确手势识别的深度学习框架。该方法采用卷积稀疏自编码器直接从原始信号中提取时序特征表示，无需启发式特征工程。在一个包含6类手势的数据集上，我们的模型取得了94.3% $\pm$ 0.3%的多受试者F1分数。为应对受试者特异性差异，我们提出了一种小样本迁移学习方案，仅需极少量的校准数据，即可将模型在未见受试者上的性能从基线水平35.1% $\pm$ 3.1%提升至92.3% $\pm$ 0.9%。此外，该系统通过增量学习策略支持功能扩展，无需完整模型重新训练即可将识别类别扩展至10类，并保持90.0% $\pm$ 0.2%的F1分数。该框架将高精度与极低的计算和传感器开销相结合，为下一代经济、自适应的假肢系统提供了一种可扩展且高效的解决方案。