The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset consisting of 10 gestures from 35 subjects. The highest classification accuracy obtained by CORrelation Alignment (CORAL) reaches more than 90%, which is 10% higher than the methods without using TL. In addition, the proposed model outperforms 4 common traditional classifiers (KNN, LDA, SVM, and Random Forest) using the minimal calibration data (two repeated trials for each gesture). The results also demonstrate the model has a great transfer robustness/flexibility for cross-gesture and cross-day scenarios, with an accuracy of 87.94% achieved using calibration gestures that are different with model training, and an accuracy of 84.26% achieved using calibration data collected on a different day, respectively. As the outcomes confirm, the proposed CNN TL method provides a practical solution for freeing new users from the complicated acquisition paradigm in the calibration process before using sEMG-based HMI systems.

中文翻译：

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使用未知校准手势通过无监督迁移学习进行基于 EMG 的多用户手势分类

手势分类模型泛化性能差和训练负担重是阻碍基于 sEMG 的人机交互（HMI）系统商业化的两个主要障碍。为了克服这些挑战，我们在由 35 名受试者的 10 个手势组成的数据集上探索和比较了基于卷积神经网络 (CNN) 开发的八种无监督迁移学习 (TL) 算法。CORrelation Alignment (CORAL) 获得的最高分类精度达到 90% 以上，比不使用 TL 的方法高 10%。此外，使用最小校准数据（每个手势两次重复试验），所提出的模型优于 4 个常见的传统分类器（KNN、LDA、SVM 和随机森林）。结果还表明，该模型对于跨手势和跨日场景具有很好的传输鲁棒性/灵活性，使用与模型训练不同的校准手势实现了 87.94% 的准确率，使用校准数据实现了 84.26% 的准确率分别在不同的日期收集。结果证实，所提出的 CNN TL 方法提供了一种实用的解决方案，可将新用户在使用基于 sEMG 的 HMI 系统之前从校准过程中复杂的采集范例中解放出来。