In order to prevent sports injury and ensure the physical safety of athletes,a sports injury estimation model based on big data analysis is proposed. The big data analysis technology is introduced. The reasons for sports injury are divided into A internal injury factor,B external injury factor and C stimulation inducing factor. On the basis of big data analysis technolo-gy,a sports injury model was constructed by means of RBF neural network. The basic RBF neural network is analyzed. The Gaussian function is regarded as the activation function of the hidden layer unit. The hidden layer was designed in a simple way to let all risk levels correspond to a Gaussian function. The center,weight and width of the radial basis function are updated. The gradient descent method is used to learn center and other parameters of radial basis function. On the basis of risk sample da-tabase of sports injury,the RBF neural network is trained,and the sports injury data is input into the RBF neural network. While the transmission data corresponds to the risk level of sports injury,RBF neural network will output the corresponding value to estimate sports injury. Experimental results show that the model has high accuracy and high efficiency.%为了预防运动损伤,保证运动员的身体安全,提出一种基于大数据分析的运动损伤估计模型.介绍了大数据分析技术,将引发运动损伤的原因划分成A内部致伤因子、B外部致伤因子、C刺激诱发因子.在大数据分析技术的基础上,通过RBF神经网络构建运动损伤估计模型.分析了基本RBF神经网络,将高斯函数看作隐含层单元的激活函数,通过一种简单的方式设计隐含层,令所有风险等级和一个高斯函数相对应.对径向基函数中心、权值和宽度进行更新,通过梯度下降法对径向基函数中心和其余参数进行学习.依据运动损伤风险样本库对RBF神经网络进行训练,将运动损伤数据输入到RBF神经网络中,当传输数据和某运动损伤风险等级相对应时,RBF神经网络将输出相应值,从而实现运动损伤估计.实验结果表明所设计模型精度和效率都高.
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