Research on Prediction of Physical Fitness Test Results in Colleges and Universities Based on Deep Learning

Abstract

All-round development strategy of quality education makes primary and secondary school students not only pursue the improvement of achievement but also carry out physical exercise. Physical training is the material basis for students to study other disciplines, and the core is to improve students’ own physical quality and increase their physique. Having a strong body helps students have certain physical strength to study in other courses. In recent years, in the background of the scientific era, college students in China obviously have some problems, such as insufficient awareness of physical exercise and serious decline in physical fitness. Nowadays, teenagers are addicted to games and go out to become members of the low-headed people. Nowadays, it is very unhealthy for teenagers to go out with their mobile phones as “low-headed people.” In order to avoid college students getting rid of this living condition, colleges and universities carry out physical fitness tests every year to promote contemporary college students to strengthen exercise. College students, as the main force in the future construction of the motherland, should not only master professional knowledge but also improve their physical fitness. Good health is the greatest capital in one’s life. Every year, some students fail to pass the physical fitness test in universities. It stands to reason that college students are in the age of high youth, and physical fitness test should be a piece of cake for them. In the face of the inconsistency between the predicted results and the actual results, this paper analyzes this. Based on the above situation, With the aim of improving students’ training efficiency and physical performance, the physical performance prediction model of deep learning is designed and analyzed to predict the performance, analyze the influencing factors of the model and how to reduce the influencing components of the factors, and analyze and compare the performance of various prediction models to find out the best model, so as to make the predicted value closer to the true value.

Predicting lower body explosive strength using hand grip dynamometer strength test

“…The all-around development strategy of quality education makes college students not only pursue the improvement of academic achievement but also carry out physical exercise. Having a strong body helps students have certain physical strength to study in other courses (Nara, 2013(Nara, , 2015(Nara, , 2017c; J. Wang et al, 2022) [21,20,24,32] . Assessment of physical fitness in educational settings is a costly and complex task due to the strength of the subjects.…”

Predict the College Sports Scores using a Weighted BP-SVR Model

“…(5) The expected and actual outputs are used to calculate the network prediction error. (6) The connection weights from the hidden to output layers and those from the hidden to input layers are corrected. (7) The network error is evaluated if it satisfies the requirements.…”

Research on the Application and Effect of Innovative Education Model in Youth Physical Fitness Training

“…The current situation of adolescent physical health survey in China shows that the current adolescent physical fitness has a downward trend compared with before, showing overweight or ultra-light, even obesity or thinness. Not only the change of body shape, but also the failure rate of vital capacity and other physical functions is high, and the indexes of sitting forward, running, sit-ups and pull-ups in physical fitness are poor [2][3]. At present, China's youth physical fitness training is still in its infancy, which is different from the original youth amateur sports school training or traditional sports school training methods.…”