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基于无线网络的人工智能机器人在羽毛球教学与训练中的应用。

The Application of Wireless Network-Based Artificial Intelligence Robots in Badminton Teaching and Training.

机构信息

School of Physical Education and Health, Changsha Medical University, Changsha 410219, Hunan, China.

出版信息

Comput Intell Neurosci. 2022 Jun 2;2022:3910307. doi: 10.1155/2022/3910307. eCollection 2022.

Abstract

Artificial intelligence technology has already set its foot in various industries, including sports, to train athletes. In this research article, people will study the application of wireless networks based on artificial intelligence robots in badminton teaching and training. People propose a system that deploys intelligent robots to teach badminton to athletes. The robots will train the players with various moves and techniques required for the game. The wireless networking system allows the robot to connect to the network. Various sets of plays and players' movements were preprogrammed for the robot. The trainer has to select essential factors such as training mode and set height required for a particular player in the robot-these are the complexities in badminton training. Moreover, in the case of effective and efficient training, people need a robot that will aid in different training modes. The changing variables, such as speed, frequency, angle, height, and change in coordinates, are utilised in the training and teaching of robots, which are more efficient than the traditional training methods given by people. The decision tree algorithm (DTA) is used in this research and is compared with the existing sports motion segmentation method (SMSM). From the results, it is observed that the proposed DTA has given improved accuracy of 93% compared with the SMSM.

摘要

人工智能技术已经涉足各个领域,包括体育领域,以训练运动员。在这篇研究文章中,人们将研究基于人工智能机器人的无线网络在羽毛球教学和训练中的应用。人们提出了一个系统,该系统部署智能机器人来教授运动员打羽毛球。机器人将用比赛所需的各种动作和技术来训练运动员。无线网络系统允许机器人连接到网络。为机器人预先设定了各种动作和运动员的动作。训练员必须为特定运动员选择诸如训练模式和所需高度等基本要素——这是羽毛球训练的复杂性所在。此外,为了实现有效和高效的训练,人们需要一个能够辅助不同训练模式的机器人。在机器人的训练和教学中,使用了变化的变量,如速度、频率、角度、高度和坐标变化,这比人们提供的传统训练方法更有效。本研究使用了决策树算法 (DTA),并与现有的运动动作分割方法 (SMSM) 进行了比较。从结果可以看出,与 SMSM 相比,所提出的 DTA 具有 93%的改进精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9ac/9184189/5f87f94ade7d/CIN2022-3910307.001.jpg

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