An Effective Prediction Method of Physical Fitness for College Students Based on GWO-GRU Model

With the increasing awareness of social health, Testing college students' physical fitness has gained a lot of attention as a crucial tool for assessing their physical fitness. Physical fitness tests may encourage college students to engage in sports training and practice good health habits. Currently, the process of calculating physical fitness examination results is highly intricate, and the majority of these approaches rely on manual calculation techniques that are not precise enough to let students grasp physical changes on their own. Therefore, this article adopts the Grey Wolf optimistic (GWO) to optimize the parameters of the Gated Recurrent Unit (GRU) neural network, in order to constructs a physical fitness score prediction model based on hyperparameter adaptive optimization. The research used historical college student physical measurement data as its experimental input and used GWO-GRU neural network to build the model framework to achieve more accurate grade prediction. The experimental results show that the prediction method based on GWO-GRU model offers more stability and forecast accuracy than conventional techniques, making it a dependable resource for managing student health at colleges and teaching physical education.