ArticleHeliyon2023
Convolutional neural network-based recognition method for volleyball movements.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative analysis and evaluation of research on the application of computer vision in sports since the 21st century.Frontiers in sports and active living · 2025Pooled it
- Recent advances in the application of artificial intelligence and wearable devices in volleyball.Frontiers in sports and active living · 2026Review
- A narrative review of deep learning applications in sports performance analysis: current practices, challenges, and future directions.BMC sports science, medicine & rehabilitation · 2025Article
- Verification and application of deep learning models in daily sports activities of teenagers.PloS one · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the development of network technology and computer intelligent monitoring technology, a large number of video data came into being. In view of the analysis of specific targets in video, the traditional artificial analysis method can not meet the existing needs. In volleyball teaching in college physical education, because each student has different movements, intelligent processing of video data has become a key issue. Through collecting and sorting out the relevant research results on behavior recognition, it is found that in the research of deep learning, the algorithm structure applied in this paper is representative, and its strong learning ability, especially compared with the traditional algorithm, can more accurately identify human movements, natural language processing and so on, but the research on volleyball action recognition is still less. Therefore, this paper constructs a data set, improves the convolution neural network model, subsequently, new models are constructed through the neural network structure to improve the accuracy of the nonlinear expression and optimize the content of the input data. In order to more accurately analyze the effectiveness of this algorithm, the new data are obtained by grouping the volleyball games in college sports courses. Compared with the original paper, the accuracy of the improved 3D network is improved by 3.3%-88.5%, and the complexity is reduced by 33.6%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.