The Role of Cloud Computing Platform in Improving Resource Allocation Efficiency of Training for Male Badminton Players
| Autor: | Gao Fei, Wang Ziya |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2444-8656 21219362 |
| DOI: | 10.2478/amns-2024-1597 |
| Popis: | In recent years, the deployment of computer vision technology across various applications for recognizing human posture and behavior has significantly advanced the field of sports training research and development. This study introduces the application of the OpenPose algorithm to estimate badminton training postures and identify critical points on the human skeleton. Following the identification of these points, we construct a human posture estimation model, which involves preprocessing the data through steps such as detecting key human points, removing redundant points, establishing a two-dimensional coordinate system, and substituting missing key points. The implementation of this algorithm is hosted on a cloud computing platform. Subsequently, we collect relevant data and execute feature extraction using the coordinate normalization technique. This paper analyzes the training postures and suggests an optimization strategy for the allocation of training resources aimed at enhancing the efficacy of training plans. The application of these optimized training schemes resulted in significant improvements in the players’ badminton skills, particularly in forehand high ball hits and serves. Notably, in the experimental tests of forehand high ball hits and serves, the skill levels were recorded at 16.56 and 15.29, respectively—outperforming the control group’s scores of 14.98 and 13.65. |
| Databáze: | Directory of Open Access Journals |
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