The application of suitable sports games for junior high school students based on deep learning and artificial intelligence

In the contemporary educational environment, junior high school students’ physical education is facing the challenge of improving teaching quality, strengthening students’ physique, and cultivating lifelong physical habits. Traditional physical education teaching methods are limited by resources, fe...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 17056 - 13
Hauptverfasser: Ji, Xueyan, Samsudin, Shamsulariffin Bin, Hassan, Muhammad Zarif Bin, Farizan, Noor Hamzani, Yuan, Yubin, Chen, Wang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 16.05.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705
639/705/1041
639/705/1042
639/705/1045
639/705/1046
639/705/117
639/705/258
639/705/531
639/705/794
Accuracy
Action recognition
Adolescent
Algorithms
Artificial Intelligence
Computer vision
Data collection
Data processing
Deep Learning
Feedback
Female
Humanities and Social Sciences
Humans
Machine learning
Male
Middle schools
multidisciplinary
Neural Networks, Computer
Physical education
Physical Education and Training - methods
Physiology
Quality of education
Schools
Science
Science (multidisciplinary)
Secondary school students
Secondary schools
Self image
Sensors
Skeleton
Skills
Sports
Sports game
Students
Deep learning
Action recognition
Artificial intelligence
Sports game
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In the contemporary educational environment, junior high school students’ physical education is facing the challenge of improving teaching quality, strengthening students’ physique, and cultivating lifelong physical habits. Traditional physical education teaching methods are limited by resources, feedback efficiency and other factors, and it is difficult to meet students’ personalized learning needs. With the rapid development of artificial intelligence and deep learning technology, a new opportunity is provided for physical education innovation. This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. This is achieved by integrating deep learning and artificial intelligence technologies. The algorithm aims to accurately identify the performance of junior high school students in sports activities, particularly in exercises such as sit-ups. By doing so, the study seeks to enhance the adaptability and teaching quality of physical education. Finally, this approach promotes the individualized development of students. By constructing the spatio-temporal graph model of human skeletal point sequence, accurate recognition of sit-ups can be achieved. Firstly, the algorithm obtains the data of human skeleton points through attitude estimation technology. Then it constructs a spatio-temporal graph model, which represents human skeleton points as nodes in the graph and the connectivity between nodes as edges. In HMDB51 dataset, the proposed average detection accuracy of ST-GCN action recognition algorithm based on MediaPipe framework reaches 88.3%. The proposed method has advantages in long-term prediction (> 500ms), especially at 1000ms, the values of Mean Absolute Error and Mean Per Joint Position Error are 71.1 and 1.04 respectively. They are obviously lower than those of other algorithms. ST-GCN action detection algorithm based on deep learning and artificial intelligence technology can significantly improve the accuracy of action recognition in junior middle school students’ sports activities, and provide an immediate and accurate feedback mechanism for physical education teaching. This approach helps students correct their movements and enhance their sports skills. Additionally, it enables teachers to gain a deeper understanding of students’ physical performance. These benefits provide strong support for the implementation of differentiated teaching.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-01941-z