基于CNN和sEMG的手势识别及康复手套控制

Gesture Recognition and Recovery Glove Control Based on CNN and sEMG

由于sEMG(Surface Electromyography)对肌肉疲劳、不同患者以及电极位移等都非常敏感,设计一种可靠、鲁棒的智能手部康复设备仍然是一项艰巨的工作。为此,提出一种基于深度学习的康复手势神经解码方法,利用患者前臂的表面肌电信号,通过卷积神经网络(CNN:Convolutional Neural Network)识别患者的手部运动意图。通过组合特征提取方法,对8通道肌电信号每个通道的信号进行组合特征提取,组合特征包括小波包分解能量特征、时域特征和频域特征共32个特征。将8个通道特征组成一个8×32的数值矩阵并进行灰度处理成特征图,再用此特征图训练卷积神经网络,对5种不同手势进行分类,分类器准确率达到98.1%。最后通过STM32 I/O口根据分类结果输出对应的PWM(Pulse Width Modulation)控制信号控制康复手套的动作,表明了该方法的可行性,为深入研究康复手套运动控制奠定了基础。

Because the sEMG(Surface Electromyography)is very sensitive to muscle fatigue,different patients and electrode displacement,it is an arduous task to design a reliable robust and intelligent hand rehabilitation device.To address these difficulties,a neural decoding method of rehabilitation gestures based on deep learning is presented by using sEMG on the forearm of patients and CNN(Convolutional Neural Network)to recognize the movement intention.A combined feature extraction method is proposed to extract the combined features of each channel of 8-channel sEMG.The combined feature includes 32 features which are wavelet packet decomposition energy features,time-domain features and frequency-domain features.The eight channel features are formed into an 8×32 numerical matrix and grayscale processed into a feature map,to train the convolutional neural network.For five different gestures recognition,the classifier’s accuracy reached 98.1%.Finally,according to the classification results,STM32 I/O port outputs the corresponding PWM(Pulse Width Modulation)signal,which shows the feasibility of this method and laying a foundation for further control of rehabilitation glove movement.