Department of Physical Education, Guizhou University of Finance and Economics, Guiyang 550025, Guizhou, China.
Comput Intell Neurosci. 2022 Jun 14;2022:1429042. doi: 10.1155/2022/1429042. eCollection 2022.
Basketball is one of the popular sports in colleges. Basketball injuries are a common thing, and the use of machine learning and other technologies can effectively reduce basketball injuries, which should start with prevention. Nonstandard basketball movements and lack of physical coordination will not only reduce sports efficiency for athletes but also increase the probability of injury. Therefore, effective reduction and targeted prevention of nonstandard actions are of great significance to college basketball. With the development of science and technology, artificial intelligence technology is closer to our lives. Based on the machine learning platform, this paper studies basketball injuries from the perspective of the integration of sports and medicine. Research on what aspects cause college students' basketball injuries is needed for the future. Effectively preventing college students from being injured in basketball is an urgent problem in the field of sports medicine. To find the most suitable machine learning platform for college basketball injury research, this article will introduce three different methods for comparative analysis. The techniques used in the experiment in this paper are traditional BP neural network technology, SCG neural network technology, and RBF neural network technology. Through experiments, it is known that, through experiments, RBF neural network technical prediction accuracy rate is as high as 95.4%, which is a relatively good neural network algorithm for studying the basketball loss of college students.
篮球是高校中流行的运动之一。篮球损伤是常见的事情，使用机器学习和其他技术可以有效地减少篮球损伤，这应该从预防开始。非标准的篮球动作和缺乏身体协调性不仅会降低运动员的运动效率，还会增加受伤的概率。因此，有效减少和有针对性地预防非标准动作对高校篮球运动具有重要意义。随着科学技术的发展，人工智能技术越来越接近我们的生活。本文基于机器学习平台，从体育与医学融合的角度研究篮球损伤。需要研究哪些方面会导致大学生篮球受伤，以便为未来提供参考。有效预防大学生在篮球运动中受伤是运动医学领域的一个紧迫问题。为了找到最适合高校篮球损伤研究的机器学习平台，本文将介绍三种不同的方法进行对比分析。本文实验中使用的技术是传统的 BP 神经网络技术、SCG 神经网络技术和 RBF 神经网络技术。通过实验可知，通过实验，RBF 神经网络技术的预测准确率高达 95.4%，是研究大学生篮球损伤的一种较好的神经网络算法。
