Injury risk prediction of aerobics athletes based on big data and computer vision

Abstract

In recent years, competitive aerobics has been rapidly popularized and developed, and the level of sports skills has also been greatly improved. Theperformance of some events has gradually approached and reached the advanced level. Therefore, it is vital to invest in the quantitative analysis and cross-disciplinary comprehensive research of aerobics performance and related factors. This paper adopts big data analysis technology and computer vision technology based on convolutional neural network, according to the related theories of sports biomechanics and computer image recognition, to establish a loss risk prediction model for aerobics athletes. The approach firstly has used technology of big data analysis for analyzing the characteristics of competitive aerobics sports data. Secondly, the approach combines the convolutional neural network to visually recognize the aerobics sports images and establish a two-branch prediction model. Finally, the output can be fused to accurately diagnose and evaluate the level of physical fitness development of aerobics athletes, the focus and goal of training content are clarified, and the scientific degree of aerobics training is improved. The study can help injury risk prediction of aerobic athletes based on applications of big data and computer vision.