PE Classroom Teaching Innovation Based on Deep Learning from the Perspective of New Curriculum Standard

Abstract

In recent years, due to the influence of various factors, most of the physical quality of primary and secondary school students in China are in a state of subhealth. According to relevant studies, nearly 70 percent of Chinese students lack daily physical exercise. Physical quality is the primary factor and prerequisite of study and work, so how to improve the physical quality of primary and middle school students has become the top priority of physical education. Based on the requirements and guidance of China’s physical education in the new curriculum standard, this paper innovates China’s physical education classroom teaching to a certain extent based on deep learning algorithm. The final results show that the students who choose PE class based on deep learning algorithm account for about 85% of the total number of students, which exceeds the students who choose traditional PE class by 70%. Therefore, we believe that the estimation and recognition of students’ movement posture in PE class can not only greatly improve students’ enthusiasm for physical exercise but also avoid sports injuries caused by inaccurate operation in the process of exercise. Human posture estimation is to detect the position of each part of the human body from the image and calculate its direction and scale information. The advent of the era of big data is based on the relationship between multiple frames of images, while human posture recognition is based on the processing of single-frame static images. Correctly recognizing the pose information of multiple frames of continuous images still makes it possible to realize correct behavior analysis and understanding.

Retracted: PE Classroom Teaching Innovation Based on Deep Learning from the Perspective of New Curriculum Standard

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”