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Volleyball Data Analysis System and Method Based on Machine Learning

机译:基于机器学习的排球数据分析系统和方法

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After the reform and the opening up, the economy of my country has grown rapidly and people’s lives have become better and better. As a result, there is a lot of time to pay attention to their health, which has promoted the rapid development of my country’s sports industry. Since the 2008 Beijing Olympics, the successful hosting of the Beijing Olympics has been further strengthened. With the rise of the development of sports in our country, the use of machine learning in a large amount of information can process this data and analyze it well. Based on this, this article is aimed at making volleyball players and coaches better understand the technical structure of hiking and the technique of hiking. The paper understands the characteristics of muscle activity over time and uses machine learning methods to analyze a large number of volleyball sports data. In this experiment, 12 volleyball players from a college of physical education were selected. According to the actual situation of the students’ physical fitness and skills, it is more reasonable to divide them into two arms with preswing technology (A type) group and two-arms without preswing technology (B type) group. Mainly study the volleyball spiking action, select the representative front-row 4th position strong attack and the back-row 6th position for comparison and analysis, and analyze the process from the take-off stage to the aerial shot stage in the four stages of the smash through the kinematics, dynamics, and surface electromyography parameters. Experiments have shown that for type A, the left gluteus maximus integral EMG sum value is significantly different between the front and rear rows ( ). The discharge volume of the left gluteus maximus during the front-row spiking process is greater than that of the back-row spiking. This difference is mainly reflected in the kicking stage and the air attack stage. It shows that volleyball data analysis has a very broad prospect of exploration and application, which can create huge social and economic benefits. How to analyze kinematics is also a very demanding research project and is also part of the future analysis of sports data. Academic value and broad practical significance are important.
机译:改革开放后,我国的经济迅速增长,人们的生活变得越来越好。因此,有很多时间要注意他们的健康,这促进了我国体育产业的快速发展。自2008年北京奥运会以来,进一步加强了北京奥运会的成功举办。随着我国体育发展的兴起,在大量信息中使用机器学习可以处理这些数据并进行分析。基于此,本文旨在制作排球运动员,教练更好地了解徒步旅行的技术结构和远足技术。本文了解随着时间的推移肌肉活动的特点,并使用机器学习方法来分析大量排球运动数据。在这个实验中,选择了来自体育学院的12名排球运动员。根据学生身体健康和技能的实际情况,将它们分成两个臂的实际情况,以预先打造技术(一种类型)组和双臂在没有预先打造技术(B型)组的两个臂中,更合理。主要研究排球尖刺行动,选择代表前排第4位强攻击和背部第6位进行比较和分析,并分析从起飞阶段到空中射击阶段的过程粉碎通过运动学,动态和表面电拍摄参数。实验表明,对于A型,左光晕Maximus积分EMG和值在前排和后行()之间有显着差异。前排尖峰过程中左耀眼最大值的放电体积大于背部尖峰的放电体积。这种差异主要反映在踢腿阶段和空袭阶段。它表明排球数据分析具有广泛的勘探和应用前景,可以造成巨大的社会和经济效益。如何分析运动学也是一个非常苛刻的研究项目,也是对体育数据的未来分析的一部分。学术价值和广泛的实际意义很重要。

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