Internet Football Training Teaching Data Analysis Based on an Embedded Sensor Network

Abstract

Football is recognized as the “first sport” with the largest number of players and the highest concentration in the world, and it also plays a pivotal role in the development of Chinese sports. Whether football can be fully developed is the key to reflecting the strength of the national sports industry. Based on the embedded sensor network, this article analyzes the internet football training and teaching data. This article is aimed at analyzing the effects of the combination of embedded sensor networks and various indexes of football sports and at making full use of this technology to provide guidance for the update of football players’ training concepts. The article first analyzes the parameters of the embedded sensor and analyzes the specific details of the football players’ actions, such as skill, strength, and continuity of the action, based on the structure of the captured image. Then, this article selects 40 football players from a certain place and analyzes their concepts of football sports competition education and training. With the help of sensor network technology to carry out experimental comparison, this article focuses on the analysis of the experimental group and the control group, respectively, around movement stretching and dynamic integration, athletes’ training willingness, etc. The experimental training data shows that the experimental group has more advantages in training results whether it is muscle activation or nerve activation. In terms of nerve activation, the maximum parameter of the experimental group members was 8.74, and that of the control group was only 7.03. The gap between the two shows that through the application of embedded sensor network technology, Internet football training and teaching can provide a significant auxiliary role for the development of football players in all aspects.