Intelligent Estimation Method for Sports Injuries Based on RBF Neural Network

Evaluating sports injuries is of great significance in improving the quality of sports. In order to achieve an accurate and effective evaluation of sports injuries, this paper proposes an intelligent estimation method for sports injuries based on the RBF neural network. It includes the following: sampling statistical data for sports evaluation based on a big data network environment; integrating outlier cleaning methods based on mean and standard deviation with weighted moving average filtering methods to achieve data cleaning, smoothing, and normalization processing; analyzing the injury factors that cause sports injuries from the perspectives of internal factors, external factors, and triggering stimuli; and extracting sports data features. On the basis of the quantification level of sports injury risk, an innovative radial basis function (RBF) neural network is used to achieve an intelligent estimation of sports injuries. The experimental results show that the proposed method for estimating sports injuries suggests significant advantages in comparing different methods. Specifically, when estimating sports injuries for test samples, the maximum time consumed by our method is only 0.94[Formula: see text]s. This indicator is particularly important in sports health monitoring systems that require high real-time performance, as it ensures that the system can respond promptly and provide injury risk estimates, thereby helping athletes or coaches take preventive measures quickly. Meanwhile, the performance of this method in estimating errors is also outstanding, with a maximum estimation error of only 0.015. This error value is very small compared to the actual risk value, indicating that this method can accurately quantify the risk of sports injuries. In practical applications, this high-precision estimation is crucial for developing personalized training plans, avoiding potential sports injuries, and helping to improve athletes’ training effectiveness and competitive performance.