在一项使用表面肌电信号（SEMG）进行手部运动识别的研究中对多类支持向量机（SVM）判别能力的评估。
The evaluation of the discriminant ability of multiclass SVM in a study of hand motion recognition by using SEMG.
作者信息
Futamata Masachika, Nagata Kentaro, Magatani Kazushige
机构信息
School of Engineering, Course of Electrical and Electronic System, Tokai University, Japan.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5246-9. doi: 10.1109/EMBC.2012.6347177.
Electromyogram (EMG) is a kind of biological signal that is generated because of excitement of muscle according to the motor instruction from a brain. We have been experimentally developing the hand motion recognition system by using 4 channels forearm EMG signals. In our system, in order to classify measured EMG SVM (Support Vector Machine) that has higher discriminability is used. Often SVM is used as a non-linear classifier. But, In the conventional system that we developed, we used a canonical discriminant analysis (CDA) method. CDA method is linear discriminant function, but it has shown good experimental results. Therefore, we have compared the discriminant ability between SVM and CDA. In this report, we will describe about the results of this experiment.
肌电图（EMG）是一种生物信号，它是根据大脑发出的运动指令，由肌肉兴奋产生的。我们一直在通过使用4通道前臂肌电信号进行手部运动识别系统的实验开发。在我们的系统中，为了对测量的肌电信号进行分类，使用了具有更高辨别能力的支持向量机（SVM）。通常，支持向量机被用作非线性分类器。但是，在我们开发的传统系统中，我们使用了典型判别分析（CDA）方法。CDA方法是线性判别函数，但它已经显示出良好的实验结果。因此，我们比较了支持向量机和典型判别分析之间的判别能力。在本报告中，我们将描述该实验的结果。
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