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.

Today, more and more Internet public media platforms allowing people to make donations or seek help are being founded in China. However, there are few specialized sports-related public welfare platforms. In this paper, a sports-related public welfare platform that aims to help people who were disabled due to participation in sports and those who are disabled but want to participate in sports was developed based on multi-sensor technology. A multi-sensor data fusion algorithm was developed, and its estimation performance was verified by comparing it with the existing Kalman consistent filtering algorithm in terms of average estimation and average consistency errors. Experimental results prove that the speed of the data collection and analysis of the sports-related public welfare platform using the algorithm established in this paper was greatly improved. Relevant data on how users used this platform showed that various factors affected users’ practical satisfaction with sports-related public welfare media platforms. It is suggested that a sports-related public welfare media platform should pay attention to the aid effect, and specific efforts should be devoted to improving the reliability and timeliness of public welfare aid information, and ensuring the stability of the platform system.

In this paper, we propose a method to evaluate the motor coordination of runners based on the analysis of amplitude and spatiotemporal dynamics of multichannel electromyography. A new diagnostic index for the coordination of runners was proposed, including the amplitude of electromyography, the spatiotemporal stability coefficient, and the symmetry coefficient of muscle force. The motor coordination of 13 professional runners was studied. Detailed anthropometric information was recorded about the professional runners. It has been found that professional athletes are characterized by the stability of movement repetition (more than 83%) and the high degree of symmetry of muscle efforts of the left and right legs (more than 81%) regardless of the changes in load during running at a speed of 8–12 km/hr. Scientific and technological means can support the scientific training of athletes. The end of the Winter Olympic Games has shown us the powerful power of a series of intelligent scientific equipment, including electro-magnetic gun, in sports training. We also look forward to the continuous innovation of these advanced technologies, which will contribute to the intelligent development of sports scientific research.