仅用两通道肌电数据实现高精度手势识别，适合低成本假肢应用。
Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG
- 用卷积稀疏自编码器直接从原始信号提取时序特征，无需人工设计特征。
- 多受试者测试中手势识别F1分数达94.3%，少样本迁移后对新受试者提升至92.3%。
- 支持增量学习扩展到10类手势，无需重新训练模型，适合实际部署。
可靠的肌电假肢控制常受限于受试者间差异大以及高密度传感器阵列的临床不实用性。本研究提出一种深度学习框架，仅使用两个表面肌电（sEMG）通道即可实现精准手势识别。方法采用卷积稀疏自编码器（CSAE）直接从原始信号提取时序特征表示，避免了启发式特征工程。在6类手势集上，模型达到94.3% ± 0.3%的多受试者F1分数。为应对个体差异，提出少样本迁移学习协议，使未见受试者的性能从基线35.1% ± 3.1%提升至92.3% ± 0.9%。此外，系统通过增量学习策略支持功能扩展，可在不重新训练模型的前提下将分类数扩展至10类，获得90.0% ± 0.2%的F1分数。该框架结合高精度与低计算及传感开销，为下一代低成本、自适应假肢系统提供了可扩展且高效的技术路径。
原文摘要 · Abstract (English)
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.
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