基于肌电信号的手部动作模式识别新思路

为了更好地识别手部动作，提出了一种新思路，将单个手指的状态作为识别目标集。采集常用手部联合动作的6路表面肌电信号，以单个手指的状态为基准将动作合理规划，提取各通道样本均值构造特征向量，设计3个并行BP神经网络，从联合动作样本中学习单个手指的状态，使得分类基数小，从而降低分类的复杂度，克服了传统多分类方法中需要采集动作多的缺点。实验结果表明，采集12种手部动作的肌电信号，将手部动作合理简化为手指动作后，利用手指的状态来训练神经网络，就能够识别出手指的3个状态的所有组合动作，即所有常用的18种手部联合动作。

New thought in hand gestures recognition based on sEMG

For better recognizing hand gestures,this paper reports a new thought that has taken the single finger＇s condition as recognizing target set.Six groups＇sEMG of commonly used hand gestures are gathered,which are planned reasonably taking the single finger＇s condition as datum.Each channel＇s sample means are used to constitute feature eigenvector.Three parallel BP neural networks are designed,which can study the single finger＇s condition from the hand gesture sample.The method makes the classified cardinal number to be small,thus reduces the complexity of classified order,and overcomes the shortcomings,which need to gather the movement many enough in the traditional multi-taxonomic approach.The experimental result indicates that：the sEMG of 12 kinds of hand movements are gathered;the hand movement is simplified reasonably to the finger movement,and the neural network is trained using finger＇s condition.All composite movements of finger＇s three conditions can be distinguished,that is to say,all commonly used 18 kinds of hand gestures have been classified.