中文摘要：

通过虚拟仪器同时采集桡侧腕屈肌和肱桡肌两路表面肌电信号,取其平均绝对值（MAV）和均方根（RMS）作为特征参数,并应用线性判别分析（LDA）方法对采集的样本进行模式识别。与其它特征识别方式的实验对比表明,所提的识别方法能够成功地从表面肌电信号中识别握拳、展拳、手腕内翻和手腕外翻4种动作,且动作识别精度更高。

英文摘要：

Through the acquisition of two channels of surface electromyogram signals（ SEMG） on flexor carpi radialis and brachioradialis with a virtual instrument,the mean absolute value（ MAV） and root-mean-square（ RMS） are taken as feature parameters,and the linear discriminant analysis（ LDA） method is applied to the pattern recognition of collected samples. The experiments compared with other identification methods show that the proposed recognition method can successfully identify four kinds of motions such as hand grasping,hand opening,radial flexion and ulnar flexion,and the recognition accuracy is much higher.