基于NNE技术的手臂运动模式识别算法研究

本文将神经网络集成(Neural network ensemble,NNE)算法应用于人体手臂运动模式识别领域中,通过对手臂不同运动模式下的表面肌电信号(sEMG)的采集、分析与处理,识别出与其对应的手臂运动模式。主要利用小波包分解(WPD)算法提取表面肌电信号的时-频特征向量,利用集成神经网络对表面肌电信号特征向量进行模式识别;神经网络集成模型由Bagging算法生成,参与集成的个体神经网络均为BP神经网络,集成神经网络的输出由单个神经网络的输出通过相对多数投票法产生。最后,对手臂4个不同运动模式下的表面肌电信号进行了模式识别实验。实验结果表明,与个体神经网络相比,集成神经网络可以显著地提高手臂动作的识别率,证明了将神经网络集成技术用于手臂运动模式识别的有效性和可行性。

Pattern recognition of human arm motion based on neural network ensemble method

Neural network ensemble(NNE) was applied to the pattern recognition of human arm motion with sEMG signal's analyzing.The feature vectors of sEMG were extracted with wavelet packet decomposition,then a NNE model was generated using Bagging algorithm and BP neural network was used as sub neural network.The output of NNE was achieved by relative majority voting decision method.Finally,recognition experiments were done with sEMG signals gathered from four different hand movements,and the results reveal that NNE can increase the correct recognition rates significantly comparing with single neural network,proving the validity and feasibility of using NNE in the field of human arm motion recognition.