Multiple kernel learning SVM-based EMG pattern classification for lower limb control
Multiple kernel learning SVM-based EMG pattern classification for lower limb control
复制标题
基于多核学习SVM的肌电模式分类用于下肢控制
DOI：
10.1109/icarcv.2010.5707406
复制
发表时间：
2010
期刊：
影响因子：
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通讯作者：
Ping Xu
中科院分区：
文献类型：
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作者：
Qingshan She;Zhizeng Luo;Ming Meng;Ping Xu
基于多核学习（MKL）支持向量机和决策树组合策略，提出了一种利用肌电图（EMG）信号对下肢运动进行分类的多类分类方法。根据多核学习的框架，采用二叉树分解方法构建了基于MKL的多分类器。首先从下肢肌肉收集四通道表面肌电信号，然后提取一些时域特征并将其输入到所提出的多分类器中。最终确定了水平行走的五种细分模式，即支撑前期、支撑中期、支撑末期、摆动前期和摆动末期。实验结果表明，该方法能够成功识别这些细分模式，并且比标准单核支持向量机分类器具有更高的精度。
Based on multiple kernel learning (MKL) support vector machine and decision tree combined strategy, a multi-class classification method is proposed to classify lower limb motions using electromyography (EMG) signals. According to the framework of multiple kernel learning, the MKL-based multi-classifier is constructed using binary tree decomposition method. Four-channel surface EMG signals are firstly collected from lower limb muscles, and then some time-domain features are extracted and inputted into the proposed multi-classifier. Five subdividing patterns are finally identified in level walking, i.e. support prophase, support metaphase, support telophase, swing prophase and swing telophase. The experimental results show that the proposed method can successfully identify these subdividing patterns with better accuracy than standard single-kernel support vector machine classifier.
DOI：
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发表时间：
2006-12
期刊：
J. Mach. Learn. Res.
影响因子：
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作者：
S. Sonnenburg;Gunnar Rätsch;C. Schäfer;B. Scholkopf
通讯作者：
S. Sonnenburg;Gunnar Rätsch;C. Schäfer;B. Scholkopf