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The help of neural networks for promoting fitness
- 1 Aquinas International Academy
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Abstract
This scientific research report focuses on the help of neural networks in fitness promotion. The project background points out that the growth in fitness demand, rich data, technological development, and the popularity of smart devices provide conditions for the application of neural networks in fitness promotion. The project content includes learning neural network knowledge, collecting data, and querying information. Through neural networks, personalized fitness plan recommendations,4 fitness effect prediction and motivation, assistance from smart fitness equipment, and fitness content recommendation and education can be achieved. The project encounters difficulties such as data collection, content organization, and language expression. Teachers provide help in data collection, content organization, and language improvement. The project gains are reflected in realizing the importance of the rigor and systematicness of scientific research, the spirit of innovative exploration, and the importance of patience and perseverance. At the same time, there is a new understanding of neural networks. Their learning ability is strong but there are challenges in interpretability, and combining with other technologies can play a greater role. In short, this project shows the potential of neural networks in the field of fitness promotion and the gains and challenges in the scientific research process.
Keywords
neural network, fitness promotion, individuation, Data collection and analysis, Innovation and challenges
[1]. Li, J.-H., Mo, Q., Zhang, J.-J., Mo, S.-Y., & Xu, X.-X. (2019). The platform of movement teaching based on deep convolutional neural network. Digital Technology & Application.
[2]. Zayegh, A., & Bassam, N. A. (2018). Neural network principles and applications.
[3]. Hao, L., & Zhao, X. (2022). Prediction of body fat percentage of college male students based on convolutional neural network. Liaoning Sports Technology, 44(5), 76-84.
[4]. Wu, C.-J., & Yu, J.-Y. (2017). Research on the prediction method of optimum movement based on rbf neural network. Modern Computer.
[5]. Fang, R. (2021). Fitness action recognition method, device, equipment and medium based on neural network. CN201910278750.8.
Cite this article
Yang,T. (2024).The help of neural networks for promoting fitness.Advances in Engineering Innovation,13,76-78.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Journal：Advances in Engineering Innovation
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