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In order to help athletes avoid injuries, prevention strategies are gradually using contemporary technology such as machine learning, which lets an assessment danger of injury. We provide a CNN model in this study that predicts the probability of an injury. A dataset of 250 athletes was used. Participants expressed their mental and physical well-being each morning and night utilizing a personalized application, It acted as the day’s danger indicators. The injuries expressed by the athletes and the output data were matched. Training and optimization were done using the evaluated attributes. Our model’s performance score has an accuracy of 99.63. It is challenging to quantify the risk of harm due to the discrepancy between the quantity of observations and injuries. According to The forecast model, favorable physical and emotional traits were the most important variables.

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Injury and Rehabilitation Assessment Using Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Imen Chebbi,
  • Sarra Abidi,
  • Leila Ben Ayed

摘要

In order to help athletes avoid injuries, prevention strategies are gradually using contemporary technology such as machine learning, which lets an assessment danger of injury. We provide a CNN model in this study that predicts the probability of an injury. A dataset of 250 athletes was used. Participants expressed their mental and physical well-being each morning and night utilizing a personalized application, It acted as the day’s danger indicators. The injuries expressed by the athletes and the output data were matched. Training and optimization were done using the evaluated attributes. Our model’s performance score has an accuracy of 99.63. It is challenging to quantify the risk of harm due to the discrepancy between the quantity of observations and injuries. According to The forecast model, favorable physical and emotional traits were the most important variables.