Du Yihao, Bai Xiaolin, Yang Wenjuan, Zheng Lin, Xie Ping
Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Apr 25;37(2):288-295. doi: 10.7507/1001-5515.201908048.
Human motion control system has a high degree of nonlinear characteristics. Through quantitative evaluation of the nonlinear coupling strength between surface electromyogram (sEMG) signals, we can get the functional state of the muscles related to the movement, and then explore the mechanism of human motion control. In this paper, wavelet packet decomposition and : coherence analysis are combined to construct an intermuscular cross-frequency coupling analysis model based on wavelet packet- : coherence. In the elbow flexion and extension state with 30% maximum voluntary contraction force (MVC), sEMG signals of 20 healthy adults were collected. Firstly, the subband components were obtained based on wavelet packet decomposition, and then the : coherence of subband signals was calculated to analyze the coupling characteristics between muscles. The results show that the linear coupling strength (frequency ratio 1:1) of the cooperative and antagonistic pairs is higher than that of the nonlinear coupling (frequency ratio 1:2, 2:1 and 1:3, 3:1) under the elbow flexion motion of 30% MVC; the coupling strength decreases with the increase of frequency ratio for the intermuscular nonlinear coupling, and there is no significant difference between the frequency ratio : and : . The intermuscular coupling in beta and gamma bands is mainly reflected in the linear coupling (1:1), nonlinear coupling of low frequency ratio (1:2, 2:1) between synergetic pair and the linear coupling between antagonistic pairs. The results show that the wavelet packet- : coherence method can qualitatively describe the nonlinear coupling strength between muscles, which provides a theoretical reference for further revealing the mechanism of human motion control and the rehabilitation evaluation of patients with motor dysfunction.
人体运动控制系统具有高度的非线性特征。通过定量评估表面肌电图(sEMG)信号之间的非线性耦合强度,我们可以了解与运动相关肌肉的功能状态,进而探究人体运动控制的机制。本文将小波包分解与相干分析相结合,构建了基于小波包 - 相干的肌肉间交叉频率耦合分析模型。在最大自主收缩力(MVC)为30%的肘屈伸状态下,采集了20名健康成年人的sEMG信号。首先,基于小波包分解获得子带分量,然后计算子带信号的相干性以分析肌肉间的耦合特性。结果表明,在MVC为30%的肘屈曲运动中,协同肌和拮抗肌对的线性耦合强度(频率比1:1)高于非线性耦合(频率比1:2、2:1和1:3、3:1);肌肉间非线性耦合的耦合强度随频率比增加而降低,频率比 和 之间无显著差异。β和γ频段的肌肉间耦合主要体现在协同肌对之间的线性耦合(1:1)、低频比(1:2、2:1)的非线性耦合以及拮抗肌对之间的线性耦合。结果表明,小波包 - 相干方法能够定性描述肌肉间的非线性耦合强度,为进一步揭示人体运动控制机制及运动功能障碍患者的康复评估提供了理论参考。