With the advantages of comfortable wearing and outdoor usage, the myoelectric gesture recognition techniques have gained much attention in the field of human-machine interaction (HMI). The purpose of this study is to optimize model structure and transfer generalized features to improve the robustness of myoelectric hand motion decoding. We derived the hand motion recognition framework from the muscle synergy theory, which is formulated as a temporal convolutional (TC) model of array sEMG signals, then a hierarchical myoelectric decoding model was proposed to predict simultaneous and continuous hand motion. The model was trained by the methods of unsupervised low-level feature learning and automated data labeling to minimize training supervision. Extensive experiments on the public sEMG database (17 subjects in Biopatrec) show that the TC model can extract muscle synergy features with higher fidelity ( R2=0.85±0.23{R}^{2} = 0.85\pm 0.23 ) than the traditional instantaneous mixture model, the results of online test demonstrate robust myoelectric decoding on multiple simultaneous and continuous hand motions. More importantly, the analysis of weights visualization shows that the low-level feature representation layer of TC model can be migrated across the individuals, which provides a transferrable feature extraction layer for generalized hand motion decoding.

中文翻译：

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使用可穿戴阵列肌电传感器进行鲁棒连续手部动作识别


肌电手势识别技术凭借佩戴舒适、户外使用等优点，在人机交互（HMI）领域备受关注。本研究的目的是优化模型结构并传递广义特征以提高肌电手运动解码的鲁棒性。我们从肌肉协同理论中推导了手部运动识别框架，该框架被表述为阵列 sEMG 信号的时间卷积（TC）模型，然后提出了分层肌电解码模型来预测同时和连续的手部运动。该模型通过无监督低级特征学习和自动数据标记的方法进行训练，以最大限度地减少训练监督。在公共sEMG数据库（Biopatrec中的17个受试者）上进行的大量实验表明，TC模型可以比传统瞬时混合模型以更高的保真度提取肌肉协同特征（R2=0.85±0.23{R}^{2} = 0.85\pm 0.23）模型，在线测试的结果表明对多个同时和连续的手部运动具有强大的肌电解码能力。更重要的是，权重可视化分析表明TC模型的低级特征表示层可以跨个体迁移，这为广义手部运动解码提供了可迁移的特征提取层。