Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning
Coordinating dynamic interceptive actions in sports like badminton requires skilled performance in getting the racket into the right place at the right time. For this reason, the strategic movement and placement of one's feet, or footwork, is an important part of competitive performance. Develo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022013779 |
| _version_ | 1818472562297929728 |
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| author | Jiabei Luo Yujie Hu Keith Davids Di Zhang Cade Gouin Xiang Li Xianrui Xu |
| author_facet | Jiabei Luo Yujie Hu Keith Davids Di Zhang Cade Gouin Xiang Li Xianrui Xu |
| author_sort | Jiabei Luo |
| collection | DOAJ |
| description | Coordinating dynamic interceptive actions in sports like badminton requires skilled performance in getting the racket into the right place at the right time. For this reason, the strategic movement and placement of one's feet, or footwork, is an important part of competitive performance. Developing an automated, efficient, and economical method to record individual movement characteristics of players is critical and can benefit athletes and motor control specialists. Here, we propose new methods for recording data on the footwork of individual badminton players, in which deep learning is used to obtain image coordinates (2D) of their shoes and binocular positioning to reconstruct the 3D coordinates of the shoes. Results show that the final positioning accuracy is 74.7%. Using the proposed methods, we revealed inter-individual adaptations in the footwork of several participants during competitive performance. The data provided insights on how individual participants coordinated footwork to intercept the projectile, by varying the distance traveled on court and jump height. Compared with visual observations by biomechanists and motor control specialists, the proposed methods can obtain quantitative data, provide analysis and evaluation of each participant's performance, revealing personal characteristics that could be targeted to shape the individualized training programs of players to refine their badminton footwork. |
| first_indexed | 2024-04-14T04:10:13Z |
| format | Article |
| id | doaj.art-ffb3f782c5ea40448d4e02223d15bccc |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-14T04:10:13Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-ffb3f782c5ea40448d4e02223d15bccc2022-12-22T02:13:15ZengElsevierHeliyon2405-84402022-08-0188e10089Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioningJiabei Luo0Yujie Hu1Keith Davids2Di Zhang3Cade Gouin4Xiang Li5Xianrui Xu6School of Geographic Sciences, Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaDepartment of Geography, University of Florida, Gainesville, FL 32611, USASport & Human Performance Research Group, Sheffield Hallam University, Sheffield, UKSchool of Geographic Sciences, Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, ChinaDepartment of Geography, University of Florida, Gainesville, FL 32611, USASchool of Geographic Sciences, Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China; Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China; Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, East China Normal University, Shanghai 200241, China; Corresponding author.School of Economics and Management, Shanghai University of Sport, Shanghai 200438, China; Corresponding author.Coordinating dynamic interceptive actions in sports like badminton requires skilled performance in getting the racket into the right place at the right time. For this reason, the strategic movement and placement of one's feet, or footwork, is an important part of competitive performance. Developing an automated, efficient, and economical method to record individual movement characteristics of players is critical and can benefit athletes and motor control specialists. Here, we propose new methods for recording data on the footwork of individual badminton players, in which deep learning is used to obtain image coordinates (2D) of their shoes and binocular positioning to reconstruct the 3D coordinates of the shoes. Results show that the final positioning accuracy is 74.7%. Using the proposed methods, we revealed inter-individual adaptations in the footwork of several participants during competitive performance. The data provided insights on how individual participants coordinated footwork to intercept the projectile, by varying the distance traveled on court and jump height. Compared with visual observations by biomechanists and motor control specialists, the proposed methods can obtain quantitative data, provide analysis and evaluation of each participant's performance, revealing personal characteristics that could be targeted to shape the individualized training programs of players to refine their badminton footwork.http://www.sciencedirect.com/science/article/pii/S2405844022013779CoordinationBadminton player trajectoriesComputer visionBinocular positioningDeep learning |
| spellingShingle | Jiabei Luo Yujie Hu Keith Davids Di Zhang Cade Gouin Xiang Li Xianrui Xu Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning Heliyon Coordination Badminton player trajectories Computer vision Binocular positioning Deep learning |
| title | Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning |
| title_full | Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning |
| title_fullStr | Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning |
| title_full_unstemmed | Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning |
| title_short | Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning |
| title_sort | vision based movement recognition reveals badminton player footwork using deep learning and binocular positioning |
| topic | Coordination Badminton player trajectories Computer vision Binocular positioning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022013779 |
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