In recent years, competitive aerobics has been rapidly popularized and developed, and the level of sports skills has also been greatly improved. The performance 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.

中文翻译：

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基于大数据和计算机视觉的健美操运动员伤害风险预测

近年来，竞技健美操得到了迅速的普及和发展，运动技能水平也得到了极大的提高。某些赛事的表演已逐渐接近并达到了先进水平。因此，投资于健美操性能及其相关因素的定量分析和跨学科综合研究至关重要。本文采用基于卷积神经网络的大数据分析技术和计算机视觉技术，根据运动生物力学和计算机图像识别的相关理论，建立了健美操运动员的损失风险预测模型。该方法首先使用大数据分析技术来分析竞技健美操运动数据的特征。第二，该方法结合了卷积神经网络，以视觉方式识别有氧运动图像，并建立了两个分支的预测模型。最后，可以融合输出结果以准确地诊断和评估健美操运动员的身体素质发展水平，明确训练内容的重点和目标，提高健美操训练的科学度。该研究可以基于大数据和计算机视觉的应用来帮助有氧运动员的受伤风险预测。