Authors : Mohamed Ahmed Kamel; Rasha Ragheb Atallah

Volume/Issue : Volume 9 - 2024, Issue 8 - August

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Google Scholar : https://tinyurl.com/2c6r4z3

Scribd : https://tinyurl.com/2dztbwts

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG462

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : One specific component of the athletic performance management paradigm is sports injuries and their rehabilitation. It plays a major role in a competitor's good recuperation and long-term physical well-being. This study looks at athletes' histories of sports recovery and develops several machine learning models based on these findings. This paper aims to assess the current state of machine learning applications for sports injuries and determine how each injury element— extrinsic, intrinsic, and triggering events—should be analyzed. The current dearth of models and open-source data sets, as well as the effectiveness of ML in sports injury prediction, are the conclusions drawn.

Keywords : Sports Training, Rehabilitation. Reconditioning, Participation, Psychological, Injury.

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