In recent years, competitive aerobics has developed rapidly in my country, and the corresponding sports injury risks have gradually increased. A number of studies have shown that due to the characteristics of aerobics itself, difficult movement requirements, fast-paced music accompaniment and coherent coordinated movements, athletes will suffer sports injuries if they are not paying attention. Therefore, discovering the causes of athletes’ injuries in time and preventing them in time is crucial for improving athletes’ skill level and prolonging sports life. Through the functional movement screening (FMS) test, understanding young aerobics athletes’ insufficiency in trunk stability, joint flexibility, muscle extension, and core strength can further help athletes reduce the risk of sports injuries. Therefore, this article proposes a novel sports injury risk model based on big data technology and deep learning, which can effectively predict the risk of sports injury and can play a positive role in improving the quality of athletes’ movements and prolonging their sports life.

中文翻译：

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健美操运动员FMS分数预测的大数据和深度学习模型

近年来，竞技健美操在我国发展迅速，相应的运动损伤风险也逐渐增加。多项研究表明，由于健美操本身的特点、运动要求难度大、音乐节奏快、动作连贯协调等特点，运动员不注意就会发生运动损伤。因此，及时发现运动员受伤原因并及时预防，对于提高运动员技术水平、延长运动寿命至关重要。通过功能性运动筛查（FMS）测试，了解年轻健美运动员在躯干稳定性、关节灵活性、肌肉伸展和核心力量等方面的不足，可以进一步帮助运动员降低运动损伤的风险。所以，