Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network
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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.
American Psychological Association (APA)
Guo, Yuliang. 2020. Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-8.
Modern Language Association (MLA)
Guo, Yuliang. Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network. Mobile Information Systems No. 2020 (2020), pp.1-8.
American Medical Association (AMA)
Guo, Yuliang. Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-8.
Includes bibliographical references
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