Affiliations: School of Physical Education, Changsha University, Changsha, Hunan, China
Correspondence:
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Corresponding author. Tan Shi, School of Physical Education, Changsha University, Changsha, Hunan, China. E-mail: tanjiu2020@163.com.
Abstract: Because rhythmic gymnastics requires a combination of human movements and hand-held instruments, it is difficult to teach and requires high movement standards. Therefore, the actual course teaching is difficult. In order to improve the teaching efficiency of rhythmic gymnastics courses, based on VR image recognition technology and digital twins, this paper combines the actual teaching needs of rhythmic gymnastics to build a corresponding auxiliary teaching system. The sports database designed in this article mainly has three kinds of sports: difficulty movements, connecting movements and equipment movements. It is different from the traditional method in that each movement and the device-related connection movement correspond to a difficulty movement of the same length and close coordination, and the connection movement plays a role in smoothly connecting the two difficulty movements. In addition, the performance of the auxiliary teaching system constructed in this paper is studied through system experiments. The research results show that this system is feasible.
Keywords: VR images, digital twins, artistic gymnastics, feature recognition
