Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers
Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers
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DOI：
10.1016/j.neucom.2021.12.081
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发表时间：
2022-06-15
期刊：
影响因子：
6
通讯作者：
Aoyama, Atsushi
中科院分区：
文献类型：
--
作者：
Hoshino, Takayuki;Kanoga, Suguru;Aoyama, Atsushi
基于表面肌电信号的人机接口是检测人体运动的有效工具。由于基于sEMG的运动识别通常需要来自用户（目标）的长时间数据测量，因此可以将重用来自其他用户的预先测量（源）数据和预先训练的分类器的迁移学习应用于sEMG数据以减少测量时间。然而，很少有知识是关于迁移学习方法和分类器在表面肌电信号数据应用的组合。因此，我们研究了基于数据和参数空间的迁移学习与浅或深分类器在跨学科sEMG分类的分类精度。该数据集包含8类前臂运动记录从25名志愿者参与者。我们使用支持向量机（SVM）作为浅层分类器，以及深度神经网络架构（称为人工神经网络（ANN））作为深度分类器。此外，我们使用风格迁移映射（STM）作为基于数据空间的迁移学习方法，使用微调（FT）作为基于参数空间的迁移学习方法。因此，无论结合使用迁移学习，ANN的分类精度高于SVM。与非转移病例相比，STM和FT显著提高了分类准确性，而不管分类器如何（请注意，FT只能与ANN一起使用）。特别是，FT和ANN的组合使用产生了最好的精度。这些发现表明，基于参数空间的迁移学习和深度分类器适合于跨学科的表面肌电信号分类。结合使用基于参数空间的迁移学习和深度分类器可以有效减少基于sEMG的HCI应用的数据测量时间。(c)2022作者由Elsevier B. V.发布。这是CC BY许可下的开放获取文章（http：//creativecommons.org/licenses/by/4.0/）。
Surface electromyogram (sEMG)-based human-computer interface (HCI) is an effective tool for detecting human movements. Because sEMG-based motion recognition usually requires prolonged data measurements from the user (target), transfer learning reusing pre-measured (source) data from other users and pre-trained classifiers can be applied to sEMG data to reduce the measurement time. However, little knowledge is available regarding the combination of transfer learning methods and classifiers in sEMG data applications. Thus, we investigated the classification accuracy of data-and parameter-space based transfer learning with shallow or deep classifiers in cross-subject sEMG classification. The dataset contains eight classes of forearm motions recorded from 25 volunteer participants. We used a support vector machine (SVM) as a shallow classifier as well as a deep neural network architecture, referred to as an artificial neural network (ANN), as a deep classifier. In addition, we used style transfer mapping (STM) as a data-space-based transfer learning method and fine-tuning (FT) as a parameter-space-based transfer learning method. Consequently, the classification accuracy of the ANN was higher than that of the SVM, regardless of the combinational use of transfer learning. STM and FT significantly improved the classification accuracy compared with non-transfer cases regardless of the classifier (note that FT can only be used with the ANN). In particular, the combined use of FT and the ANN yielded the best accuracy. These findings suggest that parameter-space-based transfer learning and deep classifiers are suitable for cross-subject sEMG classification. The combined use of parameter-space-based transfer learning and deep classifiers can effectively reduce the data measurement time of sEMG-based HCI applications.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).