中文摘要：

传统特征向量提取算法得到的特征向量无法识别手指不同角度动作,将幅值立方法引入特征向量的提取算法之中,并对立方计算后信号的特征量进行降数量级处理。实验表明,提出的特征向量提取算法对表面肌电信号微小特征差异的零偏差识别率达到75%,且大偏差保持在5%以下。设计的基于BP神经网络的手指运动模式分类器,能有效地提高手指运动形式的正确识别率。

英文摘要：

Feature vector obtained from traditional characteristic vector extraction algorithm cannot identify the different angle motion of the finger,this paper introduced a method of amplitude cubic to the characteristic vector extraction algorithm,and then decreased the orders of magnitude.The comparison with traditional feature vector identification algorithms suggests that the zero error rate in identification to the tiny different SEMG feature of the feature vector extraction algorithms proposed reaches more than 75%,meanwhile the huge error rate remained below 5%.And the finger movement pattern recognizer based on BP neural network was designed to enhance the correct rate of the mode of finger movement.