Naik Ganesh R, Kumar Dinesh Kant
Department of Electrical and Computer Engineering, Royal Melbourne Institute of Technology, Melbourne 3001, Australia.
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):301-8. doi: 10.1109/TITB.2009.2037752. Epub 2009 Dec 15.
Surface electromyogram (sEMG) is a measure of the muscle activity from the skin surface, and is an excellent indicator of the strength of muscle contraction. It is an obvious choice for control of prostheses and identification of body gestures. Using sEMG to identify posture and actions that are a result of overlapping multiple active muscles is rendered difficult by interference between different muscle activities. In the literature, attempts have been made to apply independent component analysis to separate sEMG into components corresponding to the activities of different muscles, but this has not been very successful, because some muscles are larger and more active than the others. We address the problem of how to learn to separate each gesture or activity from all others. Multicategory classification problems are usually solved by solving many one-versus-rest binary classification tasks. These subtasks naturally involve unbalanced datasets. Therefore, we require a learning methodology that can take into account unbalanced datasets, as well as large variations in the distributions of patterns corresponding to different classes. This paper reports the use of twin support vector machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications.
表面肌电图（sEMG）是一种从皮肤表面测量肌肉活动的方法，是肌肉收缩强度的极佳指标。它是控制假肢和识别身体姿势的明显选择。由于不同肌肉活动之间的干扰，使用sEMG来识别由多个活跃肌肉重叠导致的姿势和动作变得困难。在文献中，人们尝试应用独立成分分析将sEMG分离为对应于不同肌肉活动的成分，但这并不是很成功，因为一些肌肉比其他肌肉更大且更活跃。我们解决了如何学会将每个手势或活动与其他所有手势或活动区分开来的问题。多类别分类问题通常通过解决许多一对其余的二元分类任务来解决。这些子任务自然涉及不平衡数据集。因此，我们需要一种能够考虑不平衡数据集以及不同类别对应模式分布的巨大差异的学习方法。本文报告了基于sEMG使用孪生支持向量机进行手势分类，并表明该技术非常适合此类应用。
