A DNN Model-Based Behavioral System for Injury Detection and Rehabilitation

Athletics injury prevention strategies are developing into an increasing amount of reliant on cutting edge methods like machine learning that assess injury risk. This essay aims to assess the risk of injury for 250 athletes. The athletes used a customized application to self-report their physical and psychological health every morning and evening, which functioned as a daily risk indicator. The output data matched the injuries that the athletes reported. To forecast the incidence of an injury, a DNN model was trained and optimized using the measured factors. Our model has a 99.70 accuracy performance score. Estimating the risk of injury is challenging due to the discrepancy between the number of injuries and observations. The prediction model indicated that positive emotional and physical components had the greatest influence.