The jump smash is badminton's most aggressive technical manoeuvre, which is often the key to winning a match. This paper aims to explore the neuromuscular control strategies of advanced and beginner players when jumping smash in different ways. Collecting sEMG and kinematic data from 18 subjects with different motor experiences when jumping smash. Nonnegative Matrix Factorization and K-Means clustering were used to extract muscle synergies and exclude irrelevant combined synergies. Uncontrolled manifold analysis was then used to explore the association between synergies and shoulder stability. In addition, motor output at the spinal cord level was assessed by mapping sEMG to each spinal cord segment. The study found that advanced subjects could respond to different jump smash styles by adjusting the coordinated activation strategies of the upper-limb and postural muscles. Long-term training can induce a rapid decrease in the degree of co-variation of the synergies before contact with a shuttlecock to better cope with an upcoming collision. It is recommended that beginners should focus more on training the coordination of upper-limb muscles and postural muscles.
Keywords: Jump smash; Machine learning; Muscle synergy; Uncontrolled manifold.
Copyright © 2023. Published by Elsevier B.V.