Sequential Learning on sEMGs in Short- and Long-term Situations via Self-training Semi-supervised Support Vector Machine
Sequential Learning on sEMGs in Short- and Long-term Situations via Self-training Semi-supervised Support Vector Machine
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DOI：
10.1109/embc48229.2022.9871311
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Yuto Okawa;S. Kanoga;Takayuki Hoshino;T. Nitta
中科院分区：
文献类型：
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作者：
Yuto Okawa;S. Kanoga;Takayuki Hoshino;T. Nitta
本研究旨在评估自训练支持向量机（ST-S3 VM）序列学习对短期和长期表面肌电图（sEMG）数据集的影响。基于机器学习的监督分类器可以实现稳定、复杂和高性能的运动控制。可穿戴传感技术的发展使无标签表面肌电信号的测量变得容易。因此，半监督学习方法吸引了人们的注意，利用未标记的表面肌电信号数据的监督分类器与少量的标记数据。为了评估ST-S3 VM在现实条件下的鲁棒性，使用了分别包含短期和长期数据集的两个公共数据集。我们比较了ST-S3 VM与四种SVM分类器的性能。在短期和长期的情况下，ST组合分类器（ST-SVM和ST-S3 VM）表现出更高的性能比没有ST的方法（SVM和S3 VM）。在某些情况下，ST-S3 VM的性能最好，但在其他情况下，ST-SVM的性能优于ST-S3 VM。为了更好地利用未标记数据，我们将开发ST-S3 VM来减少有害的未标记数据的影响。
The purpose of this study it to assess the effect of sequential learning of self-training support vector machine (ST-S3VM) on short- and long-term surface electromyogram (sEMG) datasets. A machine learning-based supervised classi-fier is enabling stable, complex, and high-performance motion control. Unlabeled sEMG measurements are easy by the devel-opment of wearable sensing technology. Thus, semi-supervised learning methods are attracted attention to utilize unlabeled sEMG data for supervised classifier with a small amount of labeled data. To evaluate robustness of ST-S3VM in realistic conditions, two public datasets which respectively contain a short- and long-term dataset were used. We compared the performance of ST-S3VM with four-kinds of SVM classifiers. In both short- and long-term situations, ST combined classifiers (ST-SVM and ST-S3VM) showed higher performances than the methods without ST (SVM and S3VM). In some cases, ST-S3VM had the best performance, but in other cases, ST-SVM had better performance than ST-S3VM. In order to make better use of unlabeled data, we will develop ST-S3VM to reduce the impact of harmful unlabeled data.