Big Data System for Dragon Boat Rowing Action Training Based on Multidimensional Stereo Vision

Abstract

With the rapid advancement of artificial intelligence technology and the widespread use of sensing technology in education, human-computer interaction teaching has gradually developed in sports and education. Traditionally, teachers explain and demonstrate the fundamentals of movements first, then organize exercises, and students gradually consolidate technical movements through repetition. This process requires teachers to repeatedly explain, such that students can develop movement concepts, and to assist students in correcting their movements through practice. Eventually, students can master the dragon boat's paddling movements. Teachers frequently struggle to observe all of their students' movements and are therefore unable to correct them in a timely and effective manner. To address the aforementioned issues, this paper proposes a big data system for multidimensional stereo vision training in dragon boat paddling action. The stock price action recognition of dragon boat paddles and the movement of students' dragon boat paddles are realized through the multidimensional fusion of attention mechanism and spatiotemporal graph convolution. Make judgments to more effectively guide and train students' paddling movements.