Arjunan S P, Kumar D K, Naik G R
School of Electrical and Computer Engineering, RMIT university, Melbourne, VIC 3001, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4821-4. doi: 10.1109/IEMBS.2010.5627902.
Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors. These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF).
表面肌电图（sEMG）信号的分类对于诸如假肢控制和人机接口等各种应用而言至关重要。表面肌电图能更好地洞察肌肉收缩的强度，可将其用作不同应用的控制信号。由于不同肌肉活动之间存在各种干扰，在低水平屈曲期间利用表面肌电图识别运动很困难。作者先前已报道了一组新的分形特征——表面肌电图的分形维数和最大分形长度。这些特征可测量低水平手指屈曲期间肌肉收缩的复杂性和强度。为了基于分形特性利用这些特征对低水平手指屈曲进行分类和识别，已提出一种最近开发的基于机器学习的分类器——孪生支持向量机（TSVM）。TSVM基于基本学习方法工作，并将分类任务作为针对每个类别的两个支持向量机来解决。本文报道了使用孪生支持向量机对表面肌电图分形特征进行基于机器学习分类的新方法。使用线性和径向基函数（RBF）这两种不同的核函数进行训练和测试。
