Analysis and Usage: Subject-to-subject Linear Domain Adaptation in sEMG Classification
Analysis and Usage: Subject-to-subject Linear Domain Adaptation in sEMG Classification
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DOI：
10.1109/embc44109.2020.9175755
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Takayuki Hoshino;S. Kanoga;Masashi Tsubaki;A. Aoyama
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文献类型：
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作者：
Takayuki Hoshino;S. Kanoga;Masashi Tsubaki;A. Aoyama
在基于生物信号的应用程序运行之前，需要长时间校准以将预训练的分类器调整为新的用户数据（目标数据）。为了减少这种耗时的步骤，线性域适应（DA）迁移学习方法被强调，该方法传输与目标数据相关的池数据（源数据）。在过去的十年中，它们已被应用于表面肌电图 (sEMG) 数据，并隐含地假设 sEMG 数据是线性的。然而，sEMG 通常具有非线性特性，并且由于假设与实际特性之间的差异，线性 DA 方法会导致负迁移。本研究调查了应用线性 DA 方法后源数据和目标数据之间的相关性如何影响 8 类前臂运动分类。结果，我们发现分类精度和源目标相关性之间存在显着的正相关性。此外，源-目标相关性取决于运动类别。因此，我们的结果表明，当对象或运动类别之间的源-目标相关性较低时，我们应该选择非线性 DA 方法。
Before the operation of a biosignal-based application, long-duration calibration is required to adjust the pre-trained classifier to a new user data (target data). For reducing such time-consuming step, linear domain adaptation (DA) transfer learning approaches, which transfer pooled data (source data) related to the target data, are highlighted. In the last decade, they have been applied to surface electromyogram (sEMG) data with the implicit assumption that sEMG data are linear. However, sEMGs typically have non-linear characteristics, and due to the discrepancy between the assumption and actual characteristics, linear DA approaches would cause a negative transfer. This study investigated how the correlation between the source and target data affects an 8-class forearm movement classification after applying linear DA approaches. As a result, we found significant positive correlations between the classification accuracy and the source-target correlation. Additionally, the source-target correlation depended on the motion class. Therefore, our results suggest that we should choose a non-linear DA approach when the source-target correlation among subjects or motion classes is low.