Common Sports Injuries of Track and Field Athletes Using Cloud Computing and Internet of Things

Abstract

Abstract Cloud computing and the Internet of Things (IoT), are popular technologies on the Internet. They can connect everything with the Internet and have a huge role in promoting social development. This paper aimed to conduct an in-depth study on the common sports injuries of track and field athletes by studying the related algorithms of cloud computing and the IoT, and selected the cluster analysis method, so that it can better serve the analysis of human movement. The problem studied in this paper is to find out how to improve the efficiency of clustering algorithms, especially the ability to process high-dimensional data. A motion algorithm system that is suitable for analyzing human sports injuries. This paper gave a general introduction to the cluster analysis algorithm in cloud computing and IoT, made a detailed analysis of the common sports injuries of track and field athletes, and applied the cluster analysis method to the analysis of human sports injuries. The basic principle is to use mathematical methods to quantitatively determine the relationship between samples based on their own attributes and certain similarity or difference indicators, and cluster the samples according to the degree of this relationship. The introduction of this method greatly enhances the efficiency of clustering algorithms, especially the ability to process high-dimensional data, which is suitable for analyzing human sports injuries. Based on the experiments in this paper, it can see that this paper took 70 track and field athletes from a high school as the research object, and conducted a more detailed analysis of the nature, location and causes of their common sports injuries. The computational and Internet of Things (IoT) based research method for common athletic injuries among track and field athletes proposed in this article is higher than the multi-level model method, with a speed of about 10% faster and an accuracy of 18% higher than the multi-level model method. The experimental results in this paper showed that using cloud computing and IoT as the basic methods to study common sports injuries of track and field athletes can obtain richer experimental data and make the analysis of results more scientific and credible, which has practical significance for the study of human sports injuries.

Keywords

• Track and field athletes
• Sports injuries
• Cloud computing
• Internet of Things