Zhang Yi, Li Peiyang, Zhu Xuyang, Su Steven W, Guo Qing, Xu Peng, Yao Dezhong
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, 611731, Chengdu, China.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, 610054, Chengdu, China.
PLoS One. 2017 Jul 10;12(7):e0180526. doi: 10.1371/journal.pone.0180526. eCollection 2017.
The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which outperforms other feature models.
肌电图（EMG）信号表明了对日常活动，特别是下肢膝关节运动的电生理反应。文献报道显示，小波分析在用于模式识别的肌电图特征提取中具有诸多益处。然而，仅使用单个肌电图通道时，其在典型膝关节运动中的应用有限。在本研究中，对14名未经训练的健康受试者进行了三种类型的膝关节运动研究，即腿部向上屈曲（站立）、从坐姿进行髋关节伸展（坐着）和步态（行走），同时同步监测并记录了来自股内侧肌肌群的肌电图信号以及被检测腿部膝关节上的角度计数据。对包括站立、坐着、步行支撑相和步行摆动相在内的四种下肢运动进行了分段。提出了基于小波变换（WT）的奇异值分解（SVD）方法，用于使用来自股内侧肌肌群的单通道肌电图信号对四种下肢运动进行分类。基于所有受试者的下肢运动，采用五级小波分解和SVD的组合来构成特征向量。然后配置支持向量机（SVM）来构建一个多受试者分类器，该分类器将给出所有受试者对四种下肢运动分类的独立于受试者的准确率。为了有效表明分类性能，还使用了时域（例如，平均绝对值（MAV）、均方根（RMS）、积分肌电图（iEMG）、过零率（ZC））和频域（例如，平均频率（MNF）和中位数频率（MDF））的肌电图特征来对下肢运动进行分类。进行了五折交叉验证，并重复五十次以获得稳健的独立于受试者的准确率。结果表明，所提出的基于WT的SVD方法具有91.85%±0.88%的分类准确率，优于其他特征模型。
