Recently, convolutional neural network (CNN) has been widely investigated to decode human intentions using surface Electromyography (sEMG) signals. However, a pre-trained CNN model usually suffers from severe degradation when testing on a new individual, and this is mainly due to domain shift where characteristics of training and testing sEMG data differ substantially. To enhance inter-subject performances of CNN in the wrist kinematics estimation, we propose a novel regression scheme for supervised domain adaptation (SDA), based on which domain shift effects can be effectively reduced. Specifically, a two-stream CNN with shared weights is established to exploit source and target sEMG data simultaneously, such that domain-invariant features can be extracted. To tune CNN weights, both regression losses and a domain discrepancy loss are employed, where the former enable supervised learning and the latter minimizes distribution divergences between two domains. In this study, eight healthy subjects were recruited to perform wrist flexion-extension movements. Experiment results illustrated that the proposed regression SDA outperformed fine-tuning, a state-of-the-art transfer learning method, in both single-single and multiple-single scenarios of kinematics estimation. Unlike fine-tuning which suffers from catastrophic forgetting, regression SDA can maintain much better performances in original domains, which boosts the model reusability among multiple subjects.

中文翻译：

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使用 sEMG 进行基于 CNN 的手腕运动学估计的跨主题域适应


最近，卷积神经网络（CNN）已被广泛研究，以使用表面肌电图（sEMG）信号解码人类意图。然而，预训练的 CNN 模型在对新个体进行测试时通常会遭受严重退化，这主要是由于训练和测试 sEMG 数据的特征存在显着差异的域转移所致。为了增强 CNN 在手腕运动学估计中的主体间性能，我们提出了一种新的监督域适应（SDA）回归方案，基于该方案可以有效减少域移位效应。具体来说，建立了具有共享权重的双流 CNN，以同时利用源和目标 sEMG 数据，从而可以提取域不变特征。为了调整 CNN 权重，同时采用回归损失和域差异损失，其中前者支持监督学习，后者最小化两个域之间的分布差异。在这项研究中，招募了八名健康受试者进行手腕屈伸运动。实验结果表明，在运动学估计的单单和多单场景中，所提出的回归 SDA 均优于微调（一种最先进的迁移学习方法）。与遭受灾难性遗忘的微调不同，回归 SDA 可以在原始领域保持更好的性能，从而提高了模型在多个主题之间的可重用性。