AI 中文总结
研究利用拮抗肌活动冗余，通过比较单网格与双网格训练的卷积神经网络，对20名受试者高密度sEMG信号中16种手势实验，发现仅伸肌网格性能与双网格相当，可减半HGR硬件要求和计算复杂度且精度无显著损失。
AI 中文摘要
表面肌电图（sEMG）对于人机接口中的意图预测至关重要，如假肢控制。尽管用于手势识别（HGR）的深度学习模型取得了优异成果，但对计算和硬件要求很高。本文通过利用拮抗肌活动中的冗余来解决这一瓶颈，假设仅来自伸肌或屈肌群的sEMG信号中的协同激活就足以进行准确的HGR。通过比较在一个肌肉网格上训练的卷积神经网络（CNN）与在两个网格上联合训练的CNN架构来评估。使用来自20名受试者的高密度sEMG信号数据集中的16种手势进行实验。结果表明，仅伸肌网格的性能（平衡准确率89.5%，AUROC 0.99）与双网格系统（平衡准确率94.6%，AUROC 1.00）相当。即使应用慢关节融合来捕获跨网格的空间特征，模型性能也未提高。GradCAM可视化和解剖分析进一步表明，伸肌区域的信号质量优于屈肌。研究结果表明，对于一组基本的自由度手势，HGR的硬件要求和计算复杂度可以减半，而不会有显著的精度损失。
英文摘要
Surface Electromyography (sEMG) is critical for intention prediction in human-computer interfaces, such as for prosthetics control. Although deep learning models for Hand Gesture Recognition (HGR) yield excellent results, they impose high computational and hardware demands. This paper addresses this bottleneck by exploiting redundancies in agonist-antagonist muscle activity, hypothesizing that coactivations present in the sEMG signals from the extensor or flexor groups alone are sufficient for accurate HGR. We evaluate this by comparing convolutional neural networks (CNNs) trained on one muscle grid against CNN architectures trained jointly on both grids. Experiments were conducted using 16 gestures from a dataset of high-density sEMG signals from 20 subjects. The results demonstrate that the extensor grid alone achieves performance (89.5% balanced accuracy, 0.99 AUROC) comparable to the dual-grid system (94.6% balanced accuracy, 1.00 AUROC). Notably, even when applying slow joint fusion to capture spatial features across grids, model performance did not improve. GradCAM visualizations and anatomical analysis further indicate that the extensor region provides superior signal quality compared to the flexors. Our findings suggest that for a base set of DoF gestures, HGR hardware requirements and computational complexity can be halved without a prohibitive loss in accuracy.
