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. 2021 Mar 12:15:621196.
doi: 10.3389/fnbot.2021.621196. eCollection 2021.

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

Jun Xie  1 , Guohua Chen  1 , Shuang Liu  2
Affiliations

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

Jun Xie et al. Front Neurorobot. .

Abstract

This study was developed to explore the role of the intelligent badminton training robot (IBTR) to prevent badminton player injuries based on the machine learning algorithm. An IBTR is designed from the perspectives of hardware and software systems, and the movements of the athletes are recognized and analyzed with the hidden Markov model (HMM) under the machine learning. After the design was completed, it was simulated with the computer to analyze its performance. The results show that after the HMM is optimized, the recognition accuracy or data pre-processing algorithm, based on the sliding window segmentation at the moment of hitting reaches 96.03%, and the recognition rate of the improved HMM to the robot can be 94.5%, showing a good recognition effect on the training set samples. In addition, the accuracy rate is basically stable when the total size of the training data is 120 sets, after the accuracy of the robot is analyzed through different data set sizes. Therefore, it was found that the designed IBTR has a high recognition rate and stable accuracy, which can provide experimental references for injury prevention in athlete training.

Keywords: athlete injury; hidden markov model; intelligent badminton training robot; machine learning; motion recognition.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The hardware framework of the intelligent badminton training robot.
Figure 2
Figure 2
The flowchart of the software control system of the intelligent badminton training robot.
Figure 3
Figure 3
The data pre-processing process.
Figure 4
Figure 4
Analysis on recognition rate of the intelligent badminton training robot in the data pre-processing dimension.
Figure 5
Figure 5
Comparison on recognition rates of the intelligent badminton training robot with the three training algorithms.
Figure 6
Figure 6
Analysis on recognition rates when the object is a same athlete or different athletes.
Figure 7
Figure 7
Comparative analysis on accuracy of the model in different data set sizes.
See this image and copyright information in PMC

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