A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning.

ABSTRACT: The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and the inability to automate feature extraction have made it challenging to deploy research results in the real world. Therefore, this study attempts to build an injury risk prediction model using a combination of time-series image encoding and deep learning algorithms to address this issue better. This study used the time-series image encoding approach for feature construction to represent relationships between values at different moments, including Gramian Angular Summation Field (GASF), Gramian

SUBMITTER: Ye X 

PROVIDER: S-EPMC10773721 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature