从人体获取生物信号需要严格的实验设置和道德批准,这导致大数据时代用于分类器训练的数据有限。如果可以基于真实数据生成合成数据,这将改变这种情况。本文提出了一种使用深度卷积生成对抗网络(DCGAN)的多通道肌电图(EMG)数据增强方法。生成过程如下:首先,通过矩阵变换将滑动窗口内的多个通道的EMG信号转换为灰度图像,归一化,和直方图均衡。第二,利用每个类的灰度图像对DCGAN进行训练,从而在输入随机噪声的情况下生成每个类的合成灰度图像。为了评估合成数据是否与真实数据具有相似性和多样性,本文采用了分类精度指标。公共EMG数据集(即,ISRMyo-I)用于手部运动识别,以证明所提出方法的可用性。实验结果表明,在训练数据中加入合成数据对分类性能影响不大,表示真实数据与合成数据的相似性。此外,还注意到,支持向量机(SVM)和随机森林(RF)的平均精度(五类)略微提高了1%-2%,分别,使用额外的合成数据进行训练。虽然改善没有统计学意义,这意味着DCGAN生成的数据具有新的特征,并且可以丰富训练数据集的多样性。此外,交叉验证分析表明,合成样品具有较大的类间距离,纯合成样本分类的交叉验证精度更高。此外,本文还证明了直方图均衡化可以显著提高基于EMG的手部动作识别的性能。
The acquisition of bio-signal from the human body requires a strict experimental setup and ethical approvements, which leads to limited data for the training of classifiers in the era of big data. It will change the situation if synthetic data can be generated based on real data. This article proposes such a kind of multiple channel electromyography (EMG) data enhancement method using a deep convolutional generative adversarial network (
DCGAN). The generation procedure is as follows: First, the multiple channels of EMG signals within sliding windows are converted to grayscale images through matrix transformation, normalization, and histogram equalization. Second, the grayscale images of each class are used to train
DCGAN so that synthetic grayscale images of each class can be generated with the input of random noises. To evaluate whether the synthetic data own the similarity and diversity with the real data, the classification accuracy index is adopted in this article. A public EMG dataset (that is, ISR Myo-I) for hand motion recognition is used to prove the usability of the proposed method. The experimental results show that adding synthetic data to the training data has little effect on the classification performance, indicating the similarity between real data and synthetic data. Moreover, it is also noted that the average accuracy (five classes) is slightly increased by 1%-2% for support vector machine (SVM) and random forest (RF), respectively, with additional synthetic data for training. Although the improvement is not statistically significant, it implies that the generated data by
DCGAN own its new characteristics, and it is possible to enrich the diversity of the training dataset. In addition, cross-validation analysis shows that the synthetic samples have large inter-class distance, reflected by higher cross-validation accuracy of pure synthetic sample classification. Furthermore, this article also demonstrates that histogram equalization can significantly improve the performance of EMG-based hand motion recognition.