ArticleHeliyon2024
The influence of psychological change factors of tennis training strategy using optimized recurrent neural network and artificial intelligence.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the specialization of tennis technical training, the teaching focus of tennis teaching has gradually shifted to the psychological skills training of tennis players. This work addresses the impact of psychological factors on tennis players' insufficient concentration in teaching and training on match results. It discusses the psychological changes' influencing factors in tennis training strategies and analyzes the current psychological changes that are easy to occur in tennis teaching. The Recurrent Neural Network (RNN) can simulate the human brain's information processing and learning process to establish models to study human psychological changes. To explore the influence of psychological changes on tennis training, artificial intelligence technology is combined to optimize the performance of RNN, and a prediction model of psychological distress in tennis training is constructed. Additionally, a questionnaire is applied to compare the sports state of tennis players before and after the psychological regulation intervention. The findings demonstrate that following psychological regulation, 73 % of players perform as usual, 20 % present exceptionally well, and 7 % do not perform as well as usual. These results indicate an improvement compared to previous performances, highlighting the efficacy of psychological regulation supported by optimized RNN under AI assistance. This study aims to foster a consistently positive psychological state among tennis players during daily training and competitions, ensuring that their competitive performance levels remain normal or even exceed their usual standards.
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Registered trials
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