Surface electromyography (sEMG) signals have been widely used in human–machine interaction, providing more nature control expedience for external devices. However, due to the instability of sEMG, it is hard to extract consistent sEMG patterns for motion recognition. This paper proposes a dual-flow network to extract the temporal-spatial feature of sEMG for gesture recognition. The proposed network model uses convolutional neural network (CNN) and long short-term memory methods (LSTM) to, respectively, extract the spatial feature and temporal feature of sEMG, simultaneously. These features extracted by CNN and LSTM are merged into temporal-spatial feature to form an end-to-end network. A dataset was constructed for testing the performance of the network. In this database, the average recognition accuracy by using our dual-flow model reached 78.31%, which was improved by 6.69% compared to the baseline CNN (71.67%). In addition, NinaPro DB1 is also used to evaluate the proposed methods, receiving 1.86% higher recognition accuracy than the baseline CNN classifier. It is believed that the proposed dual-flow network owns the merit in extracting stable sEMG feature for gesture recognition, and can be further applied into practical applications.

中文翻译：

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通过双流网络学习 sEMG 的时空特征

表面肌电（sEMG）信号已广泛应用于人机交互，为外部设备提供了更多的自然控制权宜之计。然而，由于 sEMG 的不稳定性，很难提取一致的 sEMG 模式用于运动识别。本文提出了一种双流网络来提取 sEMG 的时空特征以进行手势识别。所提出的网络模型使用卷积神经网络 (CNN) 和长短期记忆方法 (LSTM) 分别同时提取 sEMG 的空间特征和时间特征。这些由 CNN 和 LSTM 提取的特征被合并为时空特征，形成一个端到端的网络。构建了一个数据集来测试网络的性能。在这个数据库中，使用我们的双流模型的平均识别准确率达到了 78.31%，与基线 CNN (71.67%) 相比提高了 6.69%。此外，NinaPro DB1 也用于评估所提出的方法，其识别准确率比基线 CNN 分类器高 1.86%。相信所提出的双流网络具有提取稳定的sEMG特征用于手势识别的优点，并且可以进一步应用于实际应用。