A Data Glove-Based Table Tennis Posture Reproduction Method

Table tennis is a kind of sport that requires a high level of motor skill, adopting an appropriate table tennis posture can aid players in mastering technique and enhancing their skill level. Conducting comprehensive research into the effects of various postures on stroke power, stability and accuracy allows players to modify and improve their approach according to their individual characteristics and requirements. For a thorough analysis of table tennis stance, which can enhance athletes' abilities and performance in the game. This paper presents a novel data glove-based method for tracking the attitude of table tennis. Hand and arm motion data are collected using a complete set of data glove equipment, and a joint-based arm motion position calculation method is used to accurately track the positioning and motion of three commonly used classical table tennis dribbling postures. The Blender software was employed to construct a 3D model of the human body and visualize three postures, results show good performance.