标题： 基于Transformer的高密度肌电手势识别：从即时识别到运动单位脉冲列车融合 显示英文标题

标题： Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains

摘要： 设计高效且省力的假手需要强大的手势识别算法，这些算法能够在有限的复杂度和延迟下实现高精度。 在此背景下，本文提出了一种紧凑型深度学习框架，称为CT-HGR，该框架利用视觉变换器网络使用高密度表面肌电信号（HD-sEMG）进行手势识别。 所提出的模型中的注意力机制能够以更大的并行计算能力识别不同数据段之间的相似性，并在处理长序列输入时解决内存限制问题。 CT-HGR可以从头开始训练，无需任何迁移学习，并且可以同时提取HD-sEMG数据的时间和空间特征。 此外，CT-HGR框架可以使用由HD-sEMG信号组成的sEMG图像进行即时识别。 还设计了CT-HGR的一个变体，通过盲源分离（BSS）从HD-sEMG信号中提取运动单位尖峰列车（MUSTs）的微观神经驱动信息。 此变体通过混合架构与基线版本结合，以评估融合宏观和微观神经驱动信息的潜力。 使用的HD-sEMG数据集涉及20个受试者与65种等长手部姿势相关的128个电极信号。 所提出的CT-HGR框架应用于上述数据集的31.25、62.5、125、250毫秒窗口大小，使用32、64、128个电极通道。 所有参与者使用32个电极和31.25毫秒窗口大小的平均准确率为86.23%，随着电极数量增加到128个以及窗口大小增加到250毫秒，准确率逐渐提高至91.98%。 基于单帧HD-sEMG图像的CT-HGR即时识别准确率达到89.13%。 显示英文摘要

摘要： Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a compact deep learning framework referred to as the CT-HGR, which employs a vision transformer network to conduct hand gesture recognition using highdensity sEMG (HD-sEMG) signals. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while dealing with inputs of large sequence lengths. CT-HGR can be trained from scratch without any need for transfer learning and can simultaneously extract both temporal and spatial features of HD-sEMG data. Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals. A variant of the CT-HGR is also designed to incorporate microscopic neural drive information in the form of Motor Unit Spike Trains (MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS). This variant is combined with its baseline version via a hybrid architecture to evaluate potentials of fusing macroscopic and microscopic neural drive information. The utilized HD-sEMG dataset involves 128 electrodes that collect the signals related to 65 isometric hand gestures of 20 subjects. The proposed CT-HGR framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the above-mentioned dataset utilizing 32, 64, 128 electrode channels. The average accuracy over all the participants using 32 electrodes and a window size of 31.25 ms is 86.23%, which gradually increases till reaching 91.98% for 128 electrodes and a window size of 250 ms. The CT-HGR achieves accuracy of 89.13% for instantaneous recognition based on a single frame of HD-sEMG image.