Nazarpour K, Sharafat A, P Firoozabadi S
Department of Electrical Engineering, Tarbiat Modarres University, P. O. Box 14155-4838, Tehran, Iran. E-mail:
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:4208-11. doi: 10.1109/IEMBS.2005.1615392.
We describe a novel application of Higher Order Statistics (HOS) for classifying Surface Electromyogram (sEMG) signals. We have followed seven approaches to identify discriminating signals representative of four primitive motions, i.e., elbow flexion/extension and forearm supination/pronation. The Sequential Forward Selection (SFS) method is utilized to reduce the number of HOS features to a sufficient minimum while retaining their discriminatory information. The SFS selected the kurtosis of sEMG as well as its second order statistics as discriminating features. Our method is robust, and does not require additional computations as compared to existing efficient methods for providing higher rates of correct classification of sEMG, which make it useful in practical sEMG' controlled prostheses.
我们描述了一种高阶统计量（HOS）在表面肌电图（sEMG）信号分类中的新应用。我们采用了七种方法来识别代表四种基本动作的区分性信号，即肘部屈伸和前臂旋前/旋后。顺序前向选择（SFS）方法用于将HOS特征数量减少到足够的最小值，同时保留其区分信息。SFS选择了sEMG的峰度及其二阶统计量作为区分特征。我们的方法具有鲁棒性，与现有的有效方法相比，不需要额外的计算就能提供更高的sEMG正确分类率，这使其在实际的sEMG控制假肢中很有用。
