Characteristics analysis of muscle function network and its application to muscle compensatory in repetitive movement.

In rehabilitation medicine, highly repetitive rehabilitation training can help patients recover their physical and cognitive abilities. When the patient undergoes rehabilitation training, the muscles that are supposed to function are unable to perform their functions properly, resulting in other muscles compensating or even replacing their functions, a phenomenon is known as muscle compensation. Analysis of muscle compensation allows the study of mechanisms of central nervous system control of movement. Muscle functional networks quantify the functional connectivity between synergistic muscles. They can identify the frequency characteristics between muscles regulated by standard neural inputs, which can be used as a valuable feature to analyze changes in muscle compensation. Therefore, this paper analyzes muscle compensation induced by repetitive fatiguing movements of the arm (with rest). Our work recruited 30 subjects to perform four bouts of isotonic exercise and collected Surface EMG and kinematic data. The muscle function network was used to extract the muscle compensatory characteristics of the subjects, and the neuromodulatory mechanisms of muscle compensation were explored at a macroscopic scale by linear fitting of the characteristics. The results showed that BOUT2 had the highest slope of the feature fit in the four bouts, with an average increase of 90.20% compared to BOUT1, an average decrease of 80.70% compared to BOUT3, and an average decrease of 71.56% compared to BOUT4.