Liu Wansu, Lu Biao
Information Engineering Department, Suzhou University, Suzhou, China.
Front Bioeng Biotechnol. 2022 Mar 3;10:833793. doi: 10.3389/fbioe.2022.833793. eCollection 2022.
Surface electromyographic (sEMG) signals are weak physiological electrical signals, which are highly susceptible to coupling external noise and cause major difficulties in signal acquisition and processing. The study of using sEMG signals to analyze human motion intention mainly involves data preprocessing, feature extraction, and model classification. Feature extraction is an extremely critical part; however, this often involves many manually designed features with specialized domain knowledge, so the experimenter will spend time and effort on feature extraction. To address this problem, deep learning methods that can automatically extract features are applied to the sEMG-based gesture recognition problem, drawing on the success of deep learning for image classification. In this paper, sEMG is captured using a wearable, flexible bionic device, which is simple to operate and highly secure. A multi-stream convolutional neural network algorithm is proposed to enhance the ability of sEMG to characterize hand actions in gesture recognition. The algorithm virtually augments the signal channels by reconstructing the sample structure of the sEMG to provide richer input information for gesture recognition. The methods for noise processing, active segment detection, and feature extraction are investigated, and a basic method for gesture recognition based on the combination of multichannel sEMG signals and inertial signals is proposed. Suitable filters are designed for the common noise in the signal. An improved moving average method based on the valve domain is used to reduce the segmentation error rate caused by the short resting signal time in continuous gesture signals. In this paper, three machine learning algorithms, K-nearest neighbor, linear discriminant method, and multi-stream convolutional neural network, are used for hand action classification experiments, and the effectiveness of the multi-stream convolutional neural network algorithm is demonstrated by comparison of the results. To improve the accuracy of hand action recognition, a final 10 gesture classification accuracy of up to 93.69% was obtained. The separability analysis showed significant differences in the signals of the two cognitive-behavioral tasks when the optimal electrode combination was used. A cross-subject analysis of the test set subjects illustrated that the average correct classification rate using the pervasive electrode combination could reach 93.18%.
表面肌电（sEMG）信号是微弱的生理电信号，极易受到外部噪声耦合的影响，给信号采集和处理带来很大困难。利用sEMG信号分析人体运动意图的研究主要涉及数据预处理、特征提取和模型分类。特征提取是极其关键的部分；然而，这通常涉及许多需要专业领域知识的手动设计特征，因此实验者会在特征提取上花费大量时间和精力。为了解决这个问题，借鉴深度学习在图像分类方面的成功经验，将能够自动提取特征的深度学习方法应用于基于sEMG的手势识别问题。在本文中，使用可穿戴的柔性仿生设备采集sEMG，该设备操作简单且安全性高。提出了一种多流卷积神经网络算法，以增强sEMG在手势识别中表征手部动作的能力。该算法通过重构sEMG的样本结构虚拟地增加信号通道，为手势识别提供更丰富的输入信息。研究了噪声处理、活动段检测和特征提取方法，并提出了一种基于多通道sEMG信号与惯性信号相结合的手势识别基本方法。针对信号中的常见噪声设计了合适的滤波器。采用基于阀值域的改进移动平均法，降低连续手势信号中因静息信号时间短而导致的分割错误率。本文使用三种机器学习算法，即K近邻、线性判别法和多流卷积神经网络进行手部动作分类实验，并通过结果比较证明了多流卷积神经网络算法的有效性。为提高手部动作识别的准确率，最终获得了高达93.69%的10种手势分类准确率。可分性分析表明，使用最佳电极组合时，两种认知行为任务的信号存在显著差异。对测试集受试者的跨主体分析表明，使用普遍电极组合时的平均正确分类率可达93.18%。
