【摘要】 利用单通道表面肌电信号对肘收缩、肘伸直、前臂外旋和前臂内旋四个上肢动作进行了识别.用肌电信号最高频率20倍的过采样率对表面肌电信号进行采样,利用抽取滤波技术将过采样带来的冗余数据去除并保留了过采样带来的低噪声的优点.通过小波变换提取出5个子频带的相对能量与表面肌电信号的总能量一起作为原始的特征向量,然后通过K-L展开将6维的原始特征向量降为2维.通过建立BP网络分别用6维特征向量和2维特征向量对上述的四个动作进行识别.结果显示该方法在减少肌电电极的同时保持了较高的识别率,有很好的识别效果.更多还原

【Abstract】 Uses the one-channel SEMG signal to recognize such actions as elbow flextion,elbow extension,forearm supination and forearm pronation,of which the signal sampling is 20 times as large as the highest frequency of SEMG.Although such an oversampling procedure reduces the background noise,it brings heavy job to calculation.However,the decimation and filtering can convert the sampling rate into Nyquist frequency and maintain the signal with lower noise.Using wavelet transform,the five relative energies in five sub-bands and the total energy of SEMG are obtained as primary characteristic vector comprising 6 dimensions.Then by K-L expansion,the primary characteristic vector with 6 dimensions is changed to that with 2 dimensions.Using BP networks as the classifier,the characteristic vector with 2 dimensions can maintain highly accurate recognition rate which isn’t lower than the rate of multi-channel systems.更多还原