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.
Chen et al. (Tue,) studied this question.