Dance sport is a competition of athletes for qualities such as speed, strength, endurance, flexibility, and coordination, requiring a high level of overall physical fitness. As China's sports fever continues to heat up, especially among students, the active nature of young people can lead to overenthusiasm for sports, dance, and other sports, which can lead to various physical discomforts during sports. Especially in the current sports habits of students, there is a great hidden danger of sports injuries. It is mainly due to excessive exercise, irregular exercise methods, and incomplete sports protective equipment. On this basis, this study proposes a model to further assess the health status of individuals and groups from the rate of energy consumption during exercise by building a real-time motion detection system. The development of sports health assessment systems, including the application of imaging techniques, is also analyzed. The experimental results in this article show that when the error threshold is set to 10%, the accuracy of the model is 85%, and the obtained results are ideal.

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Health Detection System for Sports Dancers during Training Based on an Image Processing Technology

With the development of the Internet platform, the traditional economic model has been gradually abandoned by the times due to its unilateral sex transactions and underdeveloped information dissemination characteristics. A new economic model was born with the development of the information age. The network economic platform is a new productivity organization platform and a pillar industry for the development of the new economy. Taking advantage of the wide spread of the Internet and the participation of individuals or organizations, network economic platforms play an important role in promoting multi-platform economic growth and mass innovation. However, with the development of the network platform economy, the network platform appears saturated. The growth trend of the online platform economy is not as good as before, and online platforms are mainly monopolized by several major online platforms, so it is difficult for small businesses or companies to have high economic returns. In the face of these shortcomings of the network platform, it is necessary to analyze the economic status of the network platform through big data and develop new development strategies. Faced with these defects of the network platform, it analyzes the economic status of the network platform through big data and develops new development strategies to make the network economy grow effectively. This paper draws conclusions by analyzing the economic growth ability, real-time resource allocation ability, customer mining ability, and flexible price adjustment ability of the network platform through technologies such as big data. The network platform for big data analysis has an average efficiency of 93.8% in allocating resources in real time. It is also far superior to traditional network platforms in mining customers and flexibly adjusting prices. The growth economy of online platforms under big data analysis is 65.7 times than that of traditional platforms. To this end, a new strategy is needed: vigorously adjusting the resource allocation of the network platform and in-depth mining of platform information can effectively grow the platform economy.

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New Development Strategy for Economic Platform Using Big Data Analysis

Big data is the result of balancing computing power with the need for large, rapidly updated, and comprehensive datasets and is now widely used in urban planning, medicine, and other fields. With the advent of the era of big data, the sports industry must also adapt to this change. The era of big data brings new ideas to the development of sports resource interaction. At the same time, the demand for a high-efficiency and high-performance sports big data integration system is imminent. The interaction between college sports and community sports realizes the improvement of the practical ability of the two institutions and departments through the satisfaction of people’s own needs. And it affects people’s lifelong sports and health by improving external conditions to internal factors. It improves the quality of life and maintains social stability and peace, thereby ensuring the better development of the university and the community. At present, the speed of data integration in the sports big data integration system is relatively slow. In order to solve this problem, this study introduces BP neural network in the artificial neural network. It introduces the concept of the neural network and its related formulas in detail. By using the BP neural network classification algorithm for data classification integration, this study conducts training and performance testing of the network. It links the extraction, transformation, and loading of data. The experimental results show that when the number of databases is 2, 4, 6, and 8, the time taken to load data by the system in this study is 113 s, 87 s, 64 s, and 42 s, respectively. It can be seen that the system in this study has obvious advantages in data loading compared with the data integration system of the existing system data warehouse. The system data query designed in this study is more efficient and consumes less time.

Integrating the Big Data in Sports and Resource Interaction Using Artificial Neural Network

OBJECTIVES The purpose of the study is to explore the changing trend of research keywords in the field of sports medicine the contributions of outstanding authors, institutions, and countries participating in the research, and the technologies directly related to the 4th Industrial Revolution (4IR) in the field of 4IR sports medicine before and after 4IR.METHODS The sample data of this study was retrieved from all the bibliographic information of the top two journals in the sports medicine field (i.e. Sports Medicine and British Journal of Sports Medicine). A bibliometric analysis was carried out by using CiteSpace, and python was used for further text mining on the key nodes in the analysis results.RESULTS The study has found that cardiovascular disease has always been popular in the research field of sports medicine; improving people’s quality of life and avoiding the risk of disease through physical activity, as long as research related to cruciate ligament may appear frequently again in the next few years. After 4IR, the field of sports medicine began to shift from a single study of epidemiology to multidimensional research of epidemiology, preventive medicine and psychology; the population characteristics of the researches began to be more segmented. Also, more collaboration between authors, institutions and institutions involved in sports medicine research has found. Germany and hospitals have become emerging forces after 4IR, and meta-analysis and systematic review are common concerns of Germany and hospitals. The impact of 4IR on sports medicine is mainly manifested in the application of sensors, intelligent monitoring, metabolomics and genomics, which having a great prospect in application.CONCLUSIONS The study sorted out the changes in research trends in the field of sports medicine before and after 4IR, and at the same time proved that the impact of 4IR on sports medicine has already existed, and more scholars need to pay attention. 4IR has brought new forces and new trends to the field of sports medicine, which deepened the research cooperation in this field. Wearable devices, intelligent monitoring, metabolomics, and genomics have applied and had a positive developing trend in sports medicine field.

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A Comparison Study of Sport Medicine Research Trend Before and After the 4th Industrial Revolution: A Systematic Review

Big data is the result of balancing computing power with the need for large, rapidly updated, and comprehensive datasets and is now widely used in urban planning, medicine, and other fields.With the advent of the era of big data, the sports industry must also adapt to this change. The era of big data brings new ideas to the development of sports resource interaction. At the same time, the demand for a high-efficiency and high-performance sports big data integration system is imminent. The interaction between college sports and community sports realizes the improvement of the practical ability of the two institutions and departments through the satisfaction of people’s own needs. And it affects people’s lifelong sports and health by improving external conditions to internal factors. It improves the quality of life and maintains social stability and peace, thereby ensuring the better development of the university and the community. At present, the speed of data integration in the sports big data integration system is relatively slow. In order to solve this problem, this study introduces BP neural network in the artificial neural network. It introduces the concept of the neural network and its related formulas in detail. By using the BP neural network classification algorithm for data classification integration, this study conducts training and performance testing of the network. It links the extraction, transformation, and loading of data.The experimental results show that when the number of databases is 2, 4, 6, and 8, the time taken to load data by the system in this study is 113 s, 87 s, 64 s, and 42 s, respectively. It can be seen that the system in this study has obvious advantages in data loading compared with the data integration system of the existing system data warehouse. The system data query designed in this study is more efficient and consumes less time.

Integrating the BigData in Sports andResource InteractionUsing Artificial Neural Network

In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm's research and simulation based on the human eye are carried out on the PC. The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.

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Yahui He

Papers

Using Big Data-Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning.