Analysis on the Steps of Physical Education Teaching Based on Deep Learning

Analysis on the Steps of Physical Education Teaching Based on Deep Learning

Aixia Dong
Copyright: © 2023 |Pages: 15
DOI: 10.4018/IJDST.317937
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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.
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1. Introduction

School physical education has always been one of the main roles in the field of health promotion and is also at the forefront of sports policy implementation (Fairclough et.al, 2005). The trend of school physical education often reflects the attitude of the national sports policy (Baranowski et.al, 1997; Fox et.al, 2004). The World Health Organization recommends that children and adolescents should accumulate at least 60 minutes of moderate to high-intensity physical activity every day. Only 20% of children and adolescents in China can achieve this amount of physical activity. Physical education is one of the main ways to achieve the physical activity goals of children and adolescents. Physical education not only provides students with opportunities to learn sports skills and sports knowledge, but also provides them with opportunities to accumulate high-intensity physical activity (Zhu et.al, 2017; Casey et.al, 2018).

The existing traditional physical education teaching content lacks systematicness and coherence, and the curriculum is boring. The teaching plan adopted is still many years ago, so it is difficult to carry out innovative teaching based on the original basis. In addition, the centralized teaching mode ignores the individual differences among students. Either the students are not exercising enough, or leads to excessive fatigue, or even sports injuries. Under the previous teaching mode, it is impossible for teachers to accurately control the physiological conditions of each student (Maksymchuk et.al, 2018). Subjectivity is too strong, and it is difficult to have a more accurate judgment of each student’s physiological conditions only through observation. Suppose that a student’s sports state reaches its extreme point, and the teacher still thinks that the student can continue training after making a wrong judgment, in this case, it easily leads to excessive fatigue of the student, or even sudden death of exercise, leading to teaching accidents (World Health Organization, 2010; Shimon et.al, 2019).

The arrival of the 5G Internet of Things era shows a qualitative leap in the field of artificial intelligence, such as machine learning (Zhu et al. 2022), data mining (Zhu et al. 2017), anomaly detection (Zhu et al. 2014; Zhu et al. 2016). The rapid progress of information technology means provides strong scientific and technological support for the development of education informatization and brings new opportunities for the construction and development of the intelligent sports service system in colleges and universities. “Smart sports” is a model of the integration of science and technology and sports. It is not “smart” in the traditional sense but emphasizes the application of a more “smart” teaching method to sports teaching to promote the reform, development and upgrading of the sports service system (Hansen et.al, 2021; Mohamed et.al, 2020). A wearable intelligent device is a new tool in the field of physical activity measurement. It not only combines accelerometers and heart rate meters, but also combines other measurement methods to obtain more comprehensive information. To some extent, it can be considered as the representative of joint measurement. Wearable intelligent devices are relatively cheap, easy to operate, and can be worn in a variety of ways. They can track the physical activity of users for a long time, and can reflect the physical activity of users in real-time. Wearable devices are easy to wear, low in cost, diverse in shape, highly accepted by people (Mooses et.al, 2018), and highly consistent with the evaluation of physical education. With the development of wearable technology, many researchers have applied wearable devices to the evaluation of physical education (Zhou et.al, 2019). The application of new technology not only provides a lot of convenience for evaluation, but also has a certain impact on traditional evaluation methods. The previously unpredictable indicators are gradually measurable, and the very difficult indicators become easy to measure.

Figure 1.

The flowchart of our physical education teaching system

IJDST.317937.f01

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