基于下肢运动协同的肌电网络建模与分析。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Lingling Chen;Yanglong Wang;Xulong Lu;Junjie Geng;Tengyang Feng
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引用次数: 0

摘要

肌肉之间的协同作用是人体完成各种运动的前提,在运动过程中会发生动态变化,这是传统肌肉协同作用提取方法无法表征的。因此,有必要建立一个涵盖整个动态过程的分析框架,以完整地解码肌间相互作用信息。通过提取肌电信号的时域特征值,建立复杂网络模型,从网络的节点和边缘分析下肢肌电信号动态。一方面,以最大模块化为目标对节点进行社区检测,将肌电网络硬划分为多个离散的社区;另一方面,对边进行矩阵分解以获得不同时间的子图。采集18例受试者在康复机器人被动训练和主动训练时的表面肌电信号。实验结果表明,在下肢运动屈伸阶段,肌肉协同作用最为显著,肌肉群主要由股直肌、大腿外侧肌和小腿腓肠肌组成。从子图分解的结果可以看出,腿部不同区域之间的协同效应是相似的,大腿肌肉之间的协同能力明显高于小腿肌肉。动态网络建模框架为肌肉协同分析提供了一种新的思路,不仅可以从宏观角度分析肌肉群的群体聚类,还可以量化肌肉区域之间的协同程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling and Analysis of Myoelectric Networks Based on Lower Limb Motor Synergies
The synergy between muscles is a prerequisite for the human body to complete various movements, and it undergoes dynamic changes during the movement process, which cannot be characterized using traditional muscle synergy extraction methods. Therefore, it is necessary to establish an analytical framework that covers the entire dynamic process to decode inter-muscular interaction information completely. By extracting the time-domain characteristic values of EMG, a complex network model is established to analyze the EMG dynamics of lower limb from the nodes and edges of network. On the one hand, the nodes are community-detected with the goal of maximum modularity, and the EMG network is hard-divided into several discrete communities. On the other hand, the edges are matrix-decomposed to obtain different subgraphs over time. The surface EMG of 18 subjects were collected during passive and active training with rehabilitation robot. The experimental results show that the muscle synergy is most substantial during the flexion and extension phases of lower limb movement, with muscle groups mainly composed of the rectus femoris, lateral thigh muscles, and calf gastrocnemius. From the results of subgraph decomposition, it can be concluded that the synergistic effects between different regions of the legs are similar, and the synergistic ability between thigh muscles is significantly higher than that of calf muscles. The dynamic network modeling framework provides a new idea for muscle synergy analysis, which can not only analyze the community clustering of muscle groups from a macro perspective, but also quantify the degree of synergy between muscle regions.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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