ArticleScientific reports2025
Construction of intelligent gymnastics teaching model based on neural network and artificial intelligence.
Article in Scientific reports, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Advanced utilization of motion decomposition and visualization technology in gymnastics education.Frontiers in psychology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to develop intelligent gymnastics teaching model based on Artificial Neural Network (ANN). It addresses key issues in traditional gymnastics teaching, such as difficulty in quantifying the teaching process and lack of personalized guidance. The proposed model integrates Kinect devices and multi-sensor data. The model uses ANN to extract key features of gymnastics movements, including joint angles, velocity, and acceleration. A Hidden Markov Model (HMM) is applied to capture temporal dependencies in the movement sequences. In addition, a Graph Neural Network (GNN) is introduced to better learn complex movement patterns and improve recognition performance. The experiments used the UTKinectAction3D dataset, which contains 10 different types of gymnastics actions. The results show that the ANN-HMM-GNN model achieves excellent recognition performance. The accuracy reaches 98.2%, with a recall of 97.5% and an F1-score of 97.8%. The model also shows strong robustness and adaptability under various environmental and physical conditions. For example, under strong lighting, the accuracy, recall, and F1-score are 97.8%, 97.2%, and 97.5%, respectively. Under dim or changing lighting, performance drops slightly but remains above 95%. The model performs best with athletes of medium height (160-180 cm) and weight (60-80 kg), achieving an accuracy and F1-score of 97.5%. By combining ANN, HMM, and GNN, the proposed model not only provides accurate and robust movement recognition but also offers quantitative feedback through an intelligent feedback mechanism. This significantly improves the scientific and personalized nature of gymnastics teaching. The study offers strong technical support for the intelligent development of gymnastics education. By automatically detecting and analyzing movements, the model can deliver real-time feedback to both teachers and students, helping learners master skills more efficiently and improve performance.
Indexed as
Identifiers
What OpenQuestion holds
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.