Zhao Dazheng, Ma Yehao, Meng Jingyan, Hu Yang, Hong Mengqi, Zhang Jiaji, Zuo Guokun, Lv Xiao, Liu Yunfeng, Shi Changcheng
School of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China.
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
Front Neurorobot. 2023 Jun 2;17:1174710. doi: 10.3389/fnbot.2023.1174710. eCollection 2023.
The time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles.
In this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient.
The detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints.
This study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction.
表面肌电信号（sEMG）的时变特性和个体差异性可能导致不同受试者的运动意图检测结果较差，以及训练和测试数据集之间的时间间隔较长。在相同任务中使用肌肉协同作用的一致性可能有助于在较长时间范围内提高检测精度。然而，传统的肌肉协同作用提取方法，如非负矩阵分解（NMF）和主成分分析（PCA）在运动意图检测领域存在一些局限性，特别是在上肢关节角度的连续估计方面。
在本研究中，我们提出了一种可靠的多变量曲线分辨交替最小二乘法（MCR-ALS）肌肉协同作用提取方法，并结合长短期记忆神经网络（LSTM），通过使用来自不同受试者和不同日期的sEMG数据集来估计连续的肘关节运动。然后，将预处理后的sEMG信号通过MCR-ALS、NMF和PCA方法分解为肌肉协同作用，分解后的肌肉激活矩阵用作sEMG特征。将sEMG特征和肘关节角度信号输入到LSTM中，建立神经网络模型。最后，使用来自不同受试者和不同日期的sEMG数据集对建立的神经网络模型进行测试，并通过相关系数测量检测精度。
使用所提出的方法，肘关节角度的检测精度超过85%。该结果显著高于使用NMF和PCA方法获得的检测精度。结果表明，所提出的方法可以提高不同受试者和不同采集时间点的运动意图检测结果的准确性。
本研究通过创新的肌肉协同作用提取方法成功提高了sEMG信号在神经网络应用中的鲁棒性。它有助于人类生理信号在人机交互中的应用。
