Subject Transfer Framework Based on Source Selection and Semi-Supervised Style Transfer Mapping for Semg Pattern Recognition
Subject Transfer Framework Based on Source Selection and Semi-Supervised Style Transfer Mapping for Semg Pattern Recognition
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DOI：
10.1109/icassp40776.2020.9054070
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
S. Kanoga;Takayuki Hoshino;H. Asoh
中科院分区：
文献类型：
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作者：
S. Kanoga;Takayuki Hoshino;H. Asoh
要使用合并数据集为新主题构建特定于主题的特征提取器和分类器，需要克服主题间的变异性。在这项研究中，我们调查的效率，建议的主题转移框架，它适用于基于歧视性的源选择方法和半监督风格转移映射算法，通过构建支持向量机分类器。我们收集了使用可穿戴表面肌电信号传感器从25名受试者获取的表面肌电信号（sEMG）数据集。分类器的训练与黄金标准的时域和自回归特征提取的八通道sEMG数据。与传统的主题转移框架（85.08±1.38%）相比，该框架将协变量移位自适应算法应用于线性判别分析分类器并使用所有源数据，通过在欧氏空间中选择判别源数据和映射目标，提高了模式识别的准确率（90.63 ± 1.27%）。
To construct subject-specific feature extractors and classifiers for a new subject using pooled datasets, overcoming intersubject variabilities is required. In this study, we investigate the efficiency of the proposed subject transfer framework, which applies a discriminability-based source selection approach and semi-supervised style transfer mapping algorithm, by constructing support vector machine classifiers. We collect a surface electromyogram (sEMG) dataset acquired from 25 subjects using a wearable sEMG sensor. Classifiers are trained with gold-standard time-domain and autoregressive features extracted from eight-channel sEMG data. Compared with conventional subject transfer framework (85.08±1.38%), which applies the covariate shift adaptation algorithm to the linear discriminant analysis classifier and uses all source data, our proposed framework improves pattern recognition accuracy (90.63 ± 1.27%) by selection of discriminative source data and the mapping destination in the Euclidean space.