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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2403.08956 |
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| _version_ | 1866916159557730304 |
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| author | Toshniwal, Dhruv Patil, Arpit Vachhani, Nancy |
| author_facet | Toshniwal, Dhruv Patil, Arpit Vachhani, Nancy |
| contents | In the competitive realm of sports, optimal performance necessitates rigorous management of nutrition and physical conditioning. Specifically, in badminton, the agility and precision required make it an ideal candidate for motion analysis through video analytics. This study leverages advanced neural network methodologies to dissect video footage of badminton matches, aiming to extract detailed insights into player kinetics and biomechanics. Through the analysis of stroke mechanics, including hand-hip coordination, leg positioning, and the execution angles of strokes, the research aims to derive predictive models that can suggest improvements in stance, technique, and muscle orientation. These recommendations are designed to mitigate erroneous techniques, reduce the risk of joint fatigue, and enhance overall performance. Utilizing a vast array of data available online, this research correlates players' physical attributes with their in-game movements to identify muscle activation patterns during play. The goal is to offer personalized training and nutrition strategies that align with the specific biomechanical demands of badminton, thereby facilitating targeted performance enhancements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08956 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI coach for badminton Toshniwal, Dhruv Patil, Arpit Vachhani, Nancy Human-Computer Interaction Artificial Intelligence In the competitive realm of sports, optimal performance necessitates rigorous management of nutrition and physical conditioning. Specifically, in badminton, the agility and precision required make it an ideal candidate for motion analysis through video analytics. This study leverages advanced neural network methodologies to dissect video footage of badminton matches, aiming to extract detailed insights into player kinetics and biomechanics. Through the analysis of stroke mechanics, including hand-hip coordination, leg positioning, and the execution angles of strokes, the research aims to derive predictive models that can suggest improvements in stance, technique, and muscle orientation. These recommendations are designed to mitigate erroneous techniques, reduce the risk of joint fatigue, and enhance overall performance. Utilizing a vast array of data available online, this research correlates players' physical attributes with their in-game movements to identify muscle activation patterns during play. The goal is to offer personalized training and nutrition strategies that align with the specific biomechanical demands of badminton, thereby facilitating targeted performance enhancements. |
| title | AI coach for badminton |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2403.08956 |