摘要
由于表面肌电信号(sEMG)具有非平稳、非周期和混沌等特性,导致传统的特征值提取方法存在实时性与准确性难以兼容的问题,基于此提出一种基于sEMG的改进能量核特征提取方法,对采集到的肌电信号进行处理。首先,在EMG振子模型的基础上,详细描述了新提出的"阈值矩阵计数"(threshold matrix count,TMC)特征提取方法;然后,通过在腿部10块不同肌肉表面粘贴肌电传感器来检测下肢不同运动过程中的肌电信号;采集到所需肌电信号后,对10块肌肉上的肌电信号分别进行特征提取,得到10个不同的特征向量x_k,并对其进行分析,选取了4块肌肉作为有效肌肉;最后将有效肌肉的特征向量x_k组合整理,得到特征矩阵X_k,将其输入BP神经网络进行训练,对4种运动模式进行识别。实验结果表明,提出的能量核特征提取方法相比于传统的两种能量核特征提取方法,运算效率分别提升了13倍和9倍;同时,相比常用的时、频域特征提取方法,训练后得到的模型具有更好的稳定性,平均识别精度为95.2%。
Because surface electromyography(sEMG) has non-stationary, aperiodic and chaotic characteristics, the traditional feature extraction method is difficult to be compatible in real-time characteristic and accuracy. In this paper, an improved energy kernel feature extraction method based on sEMG is proposed to process the acquired EMG signals. Firstly, based on the EMG oscillator model, the newly proposed Threshold Matrix Count(TMC) feature extraction method is described in detail. Then, the myoelectric sensors were stuck on the surfaces of 10 different muscles of the leg to detect the EMG signal during different motion processes of the lower limb. After acquiring the required EMG signals, the EMG signal characteristics of the 10 muscles were extracted and ten different feature vectors x_k can be obtained. After analysis, four muscles were selected as effective muscles. Finally, the effective muscle feature vectors x_k were combined to obtain a feature matrix X_k, which is inputted into the BP neural network for training, and four motion patterns were identified. The experiment results show that the calculation efficiency of the proposed energy kernel feature extraction method is improved by 13 times and 9 times compared with those of the traditional two energy kernel feature extraction methods. At the same time, compared with the commonly used time and frequency domain feature extraction methods, after training the obtained model possesses better stability and the average recognition accuracy reaches 95.2%.
作者
石欣
朱家庆
秦鹏杰
翟马强
田文彬
Shi Xin;Zhu Jiaqing;Qin Pengjie;Zhai Maqiang;Tian Wenbin(Chongqing University,Chongqing 400044,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2020年第1期121-128,共8页
Chinese Journal of Scientific Instrument
基金
国防科技创新特区(18-H863-31-ZD-002-002-05)项目资助.
关键词
SEMG
特征提取
能量核
阈值矩阵计数法
肌肉选取
运动信号识别
surface electromyography(sEMG)
feature extraction
energy kernel
threshold matrix counting method
muscle selection
motion signal recognition
