Abstract

During the last few years, significant attention has been paid to surface electromyographic (sEMG) signal–based gesture recognition. Nevertheless, sEMG signal is sensitive to various user-dependent factors, like skin impedance and muscle strength, which causes the existing gesture recognition models not suitable for new users and huge precision dropping. Therefore, we propose a dual layer transfer learning framework, named dualTL, to realize user-independent gesture recognition based on sEMG signal. DualTL is composed of two layers. The first layer of dualTL leverages the correlations of sEMG signal among different users to label partial gestures with high confidence from new users. Then, according to the consistencies of sEMG signal from the same users, the rest gestures are labeled in the second layer. We compare our method with three universal machine learning methods, seven representative transfer learning methods, and two deep learning–based sEMG gesture recognition methods. Experimental results show that the average recognition accuracy of dualTL is 80.17%. Comparing with SMO, KNN, RF, PCA, TCA, STL, and CWT, the performance improves 24.26% approximately.

中文翻译：

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双层传输学习，用于基于sEMG的用户独立手势识别

摘要

在过去的几年中，基于表面肌电（sEMG）信号的手势识别得到了极大的关注。然而，sEMG信号对各种用户相关因素敏感，例如皮肤阻抗和肌肉力量，这导致现有的手势识别模型不适用于新用户，并且精度下降幅度很大。因此，我们提出了一种双层转移学习框架，称为dualTL，以实现基于sEMG信号的用户独立手势识别。DualTL由两层组成。dualTL的第一层利用不同用户之间sEMG信号的相关性，以高置信度标记来自新用户的部分手势。然后，根据来自相同用户的sEMG信号的一致性，在第二层中标记其余手势。我们将我们的方法与三种通用机器学习方法，七种代表性转移学习方法和两种基于深度学习的sEMG手势识别方法进行了比较。实验结果表明，dualTL的平均识别准确率为80.17％。与SMO，KNN，RF，PCA，TCA，STL和CWT相比，性能大约提高了24.26％。