Research Article
Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students
@ARTICLE{10.4108/eetpht.10.5506, author={Jing Wang}, title={Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={12}, keywords={running app software usage analysis, college students' fitness index analysis, two-way gated recurrent unit neural network, driving training heuristic optimisation algorithm}, doi={10.4108/eetpht.10.5506} }
- Jing Wang
Year: 2024
Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students
PHAT
EAI
DOI: 10.4108/eetpht.10.5506
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.
Copyright © 2024 Wang, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.