Individual Intervention and Assessment of Students' Physical Fitness Based on the "Three Precision" Applet and Mixed Strategy Optimised CNN Networks

Daomeng Zhang

doi:10.4108/eetpht.10.5852

Authors

Daomeng Zhang Zhengzhou Business University

DOI:

https://doi.org/10.4108/eetpht.10.5852

Keywords:

individual intervention for student fitness, mushroom propagation optimisation algorithm, convolutional neural network, Backpropagation Neural Network, "Three Precision" applet

Abstract

With the development of network technology and intelligent application platforms, the "Three Precision" applet as a method of individual intervention for students' physical fitness can not only enable students to obtain the improvement of physical fitness and lifelong sports habits, but also establish a new bridge of cooperation between home and school. The analysis method of student physical fitness individual intervention assessment is affected by a variety of factors such as the framework design of the WeChat applet platform and the subjectivity of the intervention, which leads to the inefficiency of the student physical fitness individual intervention assessment method. To address this problem, we analyse the mode and content of students' physical fitness individual intervention based on the "Three Precision" applet, extract the feature vectors of students' physical fitness individual intervention, construct a system of students' physical fitness individual intervention assessment indexes, and establish a method of students' physical fitness individual intervention assessment based on big data technology and WeChat applet by combining the mushroom propagation optimization algorithm and convolutional neural network. Individual intervention assessment method based on big data technology and WeChat applet. The effectiveness and robustness of the proposed method are verified by using the data recorded in the "Three Precision" applet as the input data of the model. The results show that the proposed method meets the real-time requirements and improves the prediction accuracy of the individual intervention assessment method, which significantly improves the efficiency of the individual intervention assessment of students' physical fitness.

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