Liu Qiming, Wang Shan, Dai Yuxing, Wu Xingfu, Guo Shijie, Su Weihua
Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China.
State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082 PR China.
Gait Posture. 2025 Mar;117:191-203. doi: 10.1016/j.gaitpost.2024.12.028. Epub 2024 Dec 28.
Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals. Therefore, this paper proposes a two-dimensional recognition method of lower limb gait features based on sEMG signal decomposition under multiple motion modes to improve the accuracy and robustness of gait recognition.
First, in order to obtain gait information of human lower limbs, gait experiments in different motion modes are carried out using the sEMG acquisition system with 7 channels. Then, the gait dataset of human lower limbs is expanded and transformed using the variational modal decomposition (VMD) algorithm and Gramian Angular Field (GAF). The processing not only enhances the data, improves the learning ability of classifiers and avoid the overfitting during the training of the convolutional neural network (CNN), but also effectively utilizes the feature extraction capability of the CNN and preserves the temporal correlation of the EMG. Finally, the gait features in four motion modes are recognized using the processed sEMG data and trained ResNet network.
The recognition results show that the proposed method in this paper has the highest recognition rate under four motion modes compared to BP neural network and CNN network based on original sEMG signal. This research is helpful for the effective implementation of intelligent control strategies and the coordination of human-exoskeleton system.
步态特征识别对于提高外骨骼辅助的效率和协调性至关重要。基于表面肌电（sEMG）信号的识别方法很受欢迎。然而，由于忽略了sEMG信号时间序列的相关性，这些方法的识别准确率较低。因此，本文提出一种基于多运动模式下sEMG信号分解的下肢步态特征二维识别方法，以提高步态识别的准确率和鲁棒性。
首先，为了获取人体下肢的步态信息，使用7通道的sEMG采集系统进行不同运动模式下的步态实验。然后，利用变分模态分解（VMD）算法和格拉姆角场（GAF）对人体下肢步态数据集进行扩展和变换。该处理不仅增强了数据，提高了分类器的学习能力并避免了卷积神经网络（CNN）训练过程中的过拟合，还有效利用了CNN的特征提取能力并保留了肌电的时间相关性。最后，使用处理后的sEMG数据和训练好 的ResNet网络识别四种运动模式下的步态特征。
识别结果表明，与基于原始sEMG信号的BP神经网络和CNN网络相比，本文提出的方法在四种运动模式下具有最高的识别率。本研究有助于智能控制策略的有效实施以及人机外骨骼系统的协调。
