Roller skating is an important and international physical exercise, which has beautiful body movements to be watched. However, the falling of roller athletes also happens frequently. Upon the roller athletes’ fall, it means that the whole competition is over and even the roller athletes are perhaps injured. In order to stave off the tragedy, the roller track can be analyzed and be notified the roller athlete to terminate the competition. With such consideration, this paper analyzes the roller track by using two advanced technologies, i.e., pattern recognition and neural network, in which each roller athlete is equipped with an automatic movement identifier (AMI). Meanwhile, AMI is connected with the remote video monitor referee via the transmission of 5G network. In terms of AMI, its function is realized by pattern recognition, including data collection module, data processing module, and data storage module. Among them, the data storage module considers the data classification based on roller track. In addition, the neural network is used to train the roller tracks stored at AMI and give the further analysis results for the remote video monitor referee. Based on NS3, the devised AMI is simulated and the experimental results reveal that the prediction accuracy can reach 100% and the analyzed results can be used for the falling prevention timely.

中文翻译：

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通过5G网络进行模式识别和神经网络驱动的滚子轨迹分析

轮滑是一项重要的国际体育活动，它具有令人注目的优美的身体动作。但是，轮滑运动员的摔倒也经常发生。当轮滑运动员摔倒时，这意味着整个比赛已经结束，甚至轮滑运动员也可能受伤。为了避免悲剧发生，可以分析轮滑赛道并通知轮滑运动员终止比赛。出于这种考虑，本文通过使用两种先进技术（即模式识别和神经网络）来分析滚子的轨迹，其中每个滚子运动员都配备有自动运动识别器（AMI）。同时，AMI通过5G网络的传输与远程视频监控裁判连接。就AMI而言，其功能是通过模式识别来实现的，包括数据收集模块，数据处理模块和数据存储模块。其中，数据存储模块考虑基于滚子轨迹的数据分类。另外，神经网络用于训练存储在AMI处的滚子轨道，并为远程视频监控裁判提供进一步的分析结果。在NS3的基础上，对所设计的AMI进行了仿真，实验结果表明，该算法的预测精度可以达到100％，分析的结果可以及时用于防摔。