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10.1186_s13677-022-00355-w.pdf (2.21 MB)

Cloud-based deep learning-assisted system for diagnosis of sports injuries

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journal contribution
submitted on 2023-12-25, 07:40 and posted on 2024-01-10, 08:14 authored by Xiaoe Wu, Jincheng Zhou, Maoxing Zheng, Shanwei Chen, Dan Wang, Joseph Anajemba, Guangnan Zhang, Maha Abdelhaq, Raed Alsaqour, Mueen Uddin

At both clinical and diagnostic levels, machine learning technologies could help facilitate medical decision-making. Prediction of sports injuries, for instance, is a key component of avoiding and minimizing injury in motion. Despite significant attempts to forecast sports injuries, the present method is limited by its inability to identify predictors. When designing measures for the avoidance of work-related accidents and the reduction of associated risks, the risk of injury to athletes is a crucial consideration. Various indicators are being evaluated to identify injury risk factors in a number of different methods. Consequently, this paper proposes a Deep Learning-assisted System (DLS) for diagnosing sports injuries using the Internet of Things (IoT) and the concept of cloud computing. The IoT sensors that compose the body area network collect crucial data for the diagnosis of sports injuries, while cloud computing makes available flexible computer system resources and computing power. This research examines the brain injury monitoring framework, uses an optimal neural network to forecast brain injury, and enhances the medical rehabilitation system for sports. Using the metrics accuracy, precision, recall, and F1-score, the performance of the proposed model is assessed and compared with current models.

Other Information

Published in: Journal of Cloud Computing
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1186/s13677-022-00355-w

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • University of Doha for Science and Technology
  • College of Computing and Information Technology - UDST
  • College of the North Atlantic Qatar (-2022)