Automatic feature selection for performing Unit 2 of vault in wheel gymnastics

• Published in: PLOS ONE Vol. 18; no. 6; p. e0287095
• Main Authors: Kitajima, Eiji, Sato, Takashi, Kurata, Koji, Miyata, Ryota
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science (PLoS) 23.06.2023; Public Library of Science
• Subjects: Acrobatics; Algorithms; Analysis; Angle of reflection; Animals; Athletic ability; Automation; Biology and Life Sciences; Biomechanical Phenomena; Computer and Information Sciences; Computer architecture; Data mining; Engineering and Technology; Feature selection; Flow estimation; Frames (data processing); Gymnastics; Hip; Human mechanics; Image processing; Joints (anatomy); Knee; Legs; Machine learning; Male; Mathematical analysis; Medicine; Medicine and Health Sciences; Methods; Motion; Motion capture; Movement; Optical flow (image analysis); Physical Sciences; Physiological aspects; Physiological research; Pose estimation; Q; R; Regression models; Research and Analysis Methods; Research Article; Science; Skills; Social Sciences; Japan

We propose a framework to analyze the relationship between the movement features of a wheel gymnast around the mounting phase of Unit 2 of the vault event and execution (E-score) deductions from a machine-learning perspective. We first developed an automation system from a video of a wheel gymnast p...