Multimedia intelligent 3D images for automatic detection of sports injuries
This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusio...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00882 |
| _version_ | 1797340690359779328 |
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| author | Liu Hongyu |
| author_facet | Liu Hongyu |
| author_sort | Liu Hongyu |
| collection | DOAJ |
| description | This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusion detection framework for sports injury image features is created. Data analysis was conducted using an example to verify the detection framework’s effectiveness. The results show that the peak signal-to-noise ratio of acquiring athletes’ sports injury region imaging by the algorithm in this paper is 43 dB, and the average detection time is 5.91 s. The error control for sports injury detection was reduced from 0.102 to 0.011 after 600 iterations of the algorithm in this paper. |
| first_indexed | 2024-03-08T10:06:52Z |
| format | Article |
| id | doaj.art-83539cb7e5524538a6fc4fce163d9b27 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:06:52Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-83539cb7e5524538a6fc4fce163d9b272024-01-29T08:52:37ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00882Multimedia intelligent 3D images for automatic detection of sports injuriesLiu Hongyu01College of Physical Education, Baicheng Normal University, Baicheng, Jilin, 137000, China.This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusion detection framework for sports injury image features is created. Data analysis was conducted using an example to verify the detection framework’s effectiveness. The results show that the peak signal-to-noise ratio of acquiring athletes’ sports injury region imaging by the algorithm in this paper is 43 dB, and the average detection time is 5.91 s. The error control for sports injury detection was reduced from 0.102 to 0.011 after 600 iterations of the algorithm in this paper.https://doi.org/10.2478/amns.2023.2.00882sports injuriesmultimediaintelligent 3d imagesweight sharing networkimage feature fusion97p70 |
| spellingShingle | Liu Hongyu Multimedia intelligent 3D images for automatic detection of sports injuries Applied Mathematics and Nonlinear Sciences sports injuries multimedia intelligent 3d images weight sharing network image feature fusion 97p70 |
| title | Multimedia intelligent 3D images for automatic detection of sports injuries |
| title_full | Multimedia intelligent 3D images for automatic detection of sports injuries |
| title_fullStr | Multimedia intelligent 3D images for automatic detection of sports injuries |
| title_full_unstemmed | Multimedia intelligent 3D images for automatic detection of sports injuries |
| title_short | Multimedia intelligent 3D images for automatic detection of sports injuries |
| title_sort | multimedia intelligent 3d images for automatic detection of sports injuries |
| topic | sports injuries multimedia intelligent 3d images weight sharing network image feature fusion 97p70 |
| url | https://doi.org/10.2478/amns.2023.2.00882 |
| work_keys_str_mv | AT liuhongyu multimediaintelligent3dimagesforautomaticdetectionofsportsinjuries |