AI Golf: Golf Swing Analysis Tool for Self-Training
In the field of the acquisition of sports skills, a common way to improve sports skills, such as golf swings, is to imitate professional players’ motions. However, it is difficult for beginners to specify the keyframes on which they should focus and which part of the body they should corr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9913343/ |
| _version_ | 1828106640446980096 |
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| author | Chen-Chieh Liao Dong-Hyun Hwang Hideki Koike |
| author_facet | Chen-Chieh Liao Dong-Hyun Hwang Hideki Koike |
| author_sort | Chen-Chieh Liao |
| collection | DOAJ |
| description | In the field of the acquisition of sports skills, a common way to improve sports skills, such as golf swings, is to imitate professional players’ motions. However, it is difficult for beginners to specify the keyframes on which they should focus and which part of the body they should correct because of inconsistent timing and lack of knowledge. In this study, a golf swing analysis tool using neural networks is proposed to address this gap. The proposed system compares two motion sequences and specifies keyframes in which significant differences can be observed between the two motions. In addition, the system helps users intuitively understand the differences between themselves and professional players by using interpretable clues. The main challenge of this study is to target the fine-grained differences between users and professionals that can be used for self-training. Moreover, the significance of the proposed approach is the use of an unsupervised learning method without prior knowledge and labeled data, which will benefit future applications and research in other sports and skill training processes. In our approach, neural networks are first used to create a motion synchronizer to align motions with different phases and timing. Next, a motion discrepancy detector is implemented to find fine-grained differences between motions in latent spaces that are learned by the networks. Furthermore, we consider that learning intermediate motions may be feasible for beginners because, in this way, they can gradually change their pose to match the ideal form. Therefore, based on the synchronization and discrepancy detection results, we utilize a decoder to restore the intermediate human poses between two motions from the latent space. Finally, we suggest possible applications for analyzing and visualizing the discrepancy between the two input motions and interacting with the users. With the proposed application, users can easily understand the differences between their motions and those of various experts during self-training and learn how to improve their motions. |
| first_indexed | 2024-04-11T10:19:19Z |
| format | Article |
| id | doaj.art-4977455c1b4842cd9391f8c442956048 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T10:19:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4977455c1b4842cd9391f8c4429560482022-12-22T04:29:48ZengIEEEIEEE Access2169-35362022-01-011010628610629510.1109/ACCESS.2022.32102619913343AI Golf: Golf Swing Analysis Tool for Self-TrainingChen-Chieh Liao0https://orcid.org/0000-0002-9850-2468Dong-Hyun Hwang1https://orcid.org/0000-0001-5588-4366Hideki Koike2https://orcid.org/0000-0002-8989-6434Department of Computer Science, Tokyo Institute of Technology, Tokyo, JapanNAVER CLOVA Voice&Avatar, Seongnam, South KoreaDepartment of Computer Science, Tokyo Institute of Technology, Tokyo, JapanIn the field of the acquisition of sports skills, a common way to improve sports skills, such as golf swings, is to imitate professional players’ motions. However, it is difficult for beginners to specify the keyframes on which they should focus and which part of the body they should correct because of inconsistent timing and lack of knowledge. In this study, a golf swing analysis tool using neural networks is proposed to address this gap. The proposed system compares two motion sequences and specifies keyframes in which significant differences can be observed between the two motions. In addition, the system helps users intuitively understand the differences between themselves and professional players by using interpretable clues. The main challenge of this study is to target the fine-grained differences between users and professionals that can be used for self-training. Moreover, the significance of the proposed approach is the use of an unsupervised learning method without prior knowledge and labeled data, which will benefit future applications and research in other sports and skill training processes. In our approach, neural networks are first used to create a motion synchronizer to align motions with different phases and timing. Next, a motion discrepancy detector is implemented to find fine-grained differences between motions in latent spaces that are learned by the networks. Furthermore, we consider that learning intermediate motions may be feasible for beginners because, in this way, they can gradually change their pose to match the ideal form. Therefore, based on the synchronization and discrepancy detection results, we utilize a decoder to restore the intermediate human poses between two motions from the latent space. Finally, we suggest possible applications for analyzing and visualizing the discrepancy between the two input motions and interacting with the users. With the proposed application, users can easily understand the differences between their motions and those of various experts during self-training and learn how to improve their motions.https://ieeexplore.ieee.org/document/9913343/Computer visionmachine learningmotor skill traininggolf |
| spellingShingle | Chen-Chieh Liao Dong-Hyun Hwang Hideki Koike AI Golf: Golf Swing Analysis Tool for Self-Training IEEE Access Computer vision machine learning motor skill training golf |
| title | AI Golf: Golf Swing Analysis Tool for Self-Training |
| title_full | AI Golf: Golf Swing Analysis Tool for Self-Training |
| title_fullStr | AI Golf: Golf Swing Analysis Tool for Self-Training |
| title_full_unstemmed | AI Golf: Golf Swing Analysis Tool for Self-Training |
| title_short | AI Golf: Golf Swing Analysis Tool for Self-Training |
| title_sort | ai golf golf swing analysis tool for self training |
| topic | Computer vision machine learning motor skill training golf |
| url | https://ieeexplore.ieee.org/document/9913343/ |
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