Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students

Jing Wang

doi:10.4108/eetpht.10.5506

Authors

Jing Wang School of Physical Education Zheng Zhou Sias University

DOI:

https://doi.org/10.4108/eetpht.10.5506

Keywords:

running app software usage analysis, college students' fitness index analysis, two-way gated recurrent unit neural network, driving training heuristic optimisation algorithm

Abstract

With the development of Internet of Things (IoT) technology, the use of running APP to analyse college students' physical fitness indicators has gradually become a commonly used sports analysis method. Aiming at the problems of insufficient precision of the running APP usage analysis method, easy to fall into the local optimum, and insufficiently comprehensive evaluation effect, this paper proposes a running APP usage analysis method based on deep learning network for some college students' physical fitness indicators. Firstly, feature vectors are taken from the running APP user behaviour data to analyse the values of college students' physical fitness indicators and construct a mapping model of running APP usage analysis for the effects of college students' physical fitness indicators; then, the BiGRU neural network is improved by using the driver-training heuristic optimisation algorithm to construct a running APP usage analysis model for some of the college students' physical fitness indicators; finally, a mapping model is constructed for the effect of running APP usage analysis for some of the college students' physical fitness indicators by using college student-oriented running APP Finally, the effectiveness and robustness of the DTBO algorithm are compared with the user behaviour dataset of the running APP for college students. Finally, the effectiveness and robustness of the DTBO algorithm are compared using the user behaviour data set of the running app platform for college students.

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