Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning
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 a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2021.621196/full |
| _version_ | 1818914836598226944 |
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| author | Jun Xie Guohua Chen Shuang Liu |
| author_facet | Jun Xie Guohua Chen Shuang Liu |
| author_sort | Jun Xie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T23:52:43Z |
| format | Article |
| id | doaj.art-e57f8357a12649d4a591e721b3a7bbe2 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-19T23:52:43Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-e57f8357a12649d4a591e721b3a7bbe22022-12-21T20:01:06ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182021-03-011510.3389/fnbot.2021.621196621196Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine LearningJun Xie0Guohua Chen1Shuang Liu2School of Physical Education, East China University of Technology, Nanchang, ChinaSchool of Physical Education, East China University of Technology, Nanchang, ChinaCollege of Physical Education, Jinggangshan University, Ji'an, ChinaThis 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.https://www.frontiersin.org/articles/10.3389/fnbot.2021.621196/fullintelligent badminton training robotmachine learninghidden markov modelathlete injurymotion recognition |
| spellingShingle | Jun Xie Guohua Chen Shuang Liu Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning Frontiers in Neurorobotics intelligent badminton training robot machine learning hidden markov model athlete injury motion recognition |
| title | Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning |
| title_full | Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning |
| title_fullStr | Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning |
| title_full_unstemmed | Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning |
| title_short | Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning |
| title_sort | intelligent badminton training robot in athlete injury prevention under machine learning |
| topic | intelligent badminton training robot machine learning hidden markov model athlete injury motion recognition |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2021.621196/full |
| work_keys_str_mv | AT junxie intelligentbadmintontrainingrobotinathleteinjurypreventionundermachinelearning AT guohuachen intelligentbadmintontrainingrobotinathleteinjurypreventionundermachinelearning AT shuangliu intelligentbadmintontrainingrobotinathleteinjurypreventionundermachinelearning |