The application of suitable sports games for junior high school students based on deep learning and artificial intelligence - Ji, Xueyan; Samsudin, Shamsulariffin; Hassan, Muhammad Zarif; Farizan, Noor Hamzani; Yuan, Yubin; Chen, Wang (Nature Research, 2025)
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.