Analysis on the Steps of Physical Education Teaching Based on Deep Learning
Author:
Affiliation:
1. Wuxi Institute of Technology, China
Abstract
The rapid progress of the internet of things and artificial intelligence has brought new opportunities for the construction and development of intelligent sports. This paper designs an analysis and evaluation system of physical education teaching steps based on deep learning technology. The intelligent wearable devices are used to conduct real-time dynamic monitoring of students' exercise steps and heart rate in class so as to build a sports teaching activity data set. The authors analyze the time step sequence based on transformer deep model to realize the estimation of motion effect. In addition, they propose a hierarchical fusion model based on transformer, which makes full use of the steps and heart rate information to predict the abnormal situation in physical education. The experimental results show the effectiveness of the system.
Publisher
IGI Global
Subject
Computer Networks and Communications,Hardware and Architecture
Reference32 articles.
1. Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET) (pp. 1-6). IEEE.
2. Guidelines for school and community programs to promote lifelong physical activity among young people.;T.Baranowski;Morbidity and Mortality Weekly Report,1997
3. Adopting a models-based approach to teaching physical education
4. Physical Activity Levels in Middle and High School Physical Education: A Review
5. The School and Promotion of Children’s Health-Enhancing Physical Activity: Perspectives from the United Kingdom
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