Rivera L A, DeSouza G N
IEEE Int Conf Rehabil Robot. 2011;2011:5975392. doi: 10.1109/ICORR.2011.5975392.
Rehabilitation devices, prosthesis and human machine interfaces are among many applications for which surface electromyographic signals (sEMG) can be employed. Systems reliant on these muscle-generated electrical signals require various forms of machine learning algorithms for specific signature recognition. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used. However, in all those cases, the use of multiple sensors is a constant. In this paper, we present a new technique for source signal separation that relies on a single sEMG sensor. This proposed technique was employed in a classification framework for hand movements that achieved comparable results to other approaches in the literature, but yet, it relied on a much simpler classifier and used a very small number of features.
康复设备、假肢和人机接口是可以使用表面肌电信号（sEMG）的众多应用领域。依赖这些肌肉产生的电信号的系统需要各种形式的机器学习算法来进行特定特征识别。这些系统在信号检测方法、特征选择和所使用的分类算法方面各不相同。然而，在所有这些情况下，使用多个传感器是不变的。在本文中，我们提出了一种基于单个sEMG传感器的源信号分离新技术。该技术应用于手部运动分类框架中，取得了与文献中其他方法相当的结果，但它依赖于更简单的分类器，并且使用的特征数量非常少。
