Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

October 30, 2023


Pages


DOI

Article

Multimedia intelligent 3D images for automatic detection of sports injuries

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Authors

Hongyu Liu Affiliation:
College of Physical Education, Baicheng Normal University, Baicheng, Jilin, 137000, China.


Abstract

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.


Keywords

Sports injuries, Multimedia, Intelligent 3D images, Weight sharing network, Image feature fusion, 97P70


Citation

Liu, H. (2023). Multimedia intelligent 3D images for automatic detection of sports injuries. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.00882

Published by: Engineering Journals

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