Physical Education Department, Jiangsu University of Technology, Changzhou 213001, Jiangsu, China.
School of Sports and Health Engineering, Hebei University of Engineering, Handan 056038, Hebei, China.
Comput Intell Neurosci. 2022 May 16;2022:5604141. doi: 10.1155/2022/5604141. eCollection 2022.
Physical education (PE) is, in general, one of the most important skills developed for human healthiness. Many barriers exist in society to improve the performance in Chinese physical activities. Furthermore, the incorporation of 5G communication network technology is becoming a trend in the increase of physical activity in China on a daily basis. Physical exercise may assist Chinese people to enhance their mental abilities, self-concept, and goal orientation and avoid mental illnesses such as sadness and anxiety. Physical exercise without education is like having a body but no soul. There is no doubt about the value of physical education and other types of exercise in the educational system. In this paper, we propose refined physical education teaching based on 5G network technology to obtain everlasting data without termination. First, we preprocess the sports dataset using a stacked denoising autoencoder (SDAE), and a Gaussian Mixture Model (GMM) is utilized for the feature extraction process. A random forest approach (RFA) is then used in the selection of the features. Furthermore, we adopted a CNN-based upgraded classifier for classification and efficient data allocation (EDA) algorithm for storing data generated by the 5G network. Experimental results reveal that our proposed method outperforms the baseline methods by a huge margin.
体育教育（PE）通常是人类健康最重要的技能之一。社会中存在许多障碍，影响了中国体育活动的表现。此外，5G 通信网络技术的融入正成为中国日常体育活动增加的趋势。体育锻炼可以帮助中国人提高他们的心理能力、自我概念和目标定向，避免悲伤和焦虑等精神疾病。没有教育的体育锻炼就像有身体但没有灵魂。体育教育和其他类型的运动在教育系统中的价值是毋庸置疑的。在本文中，我们提出了基于 5G 网络技术的精细化体育教学，以获得无终止的永久数据。首先，我们使用堆叠去噪自编码器（SDAE）对运动数据集进行预处理，然后使用高斯混合模型（GMM）进行特征提取过程。然后，我们使用随机森林方法（RFA）选择特征。此外，我们采用基于 CNN 的升级分类器进行分类和高效数据分配（EDA）算法来存储 5G 网络生成的数据。实验结果表明，我们提出的方法比基线方法有很大的优势。
