Trusted Injury Prediction in High-Risk Settings via Verified Hybrid AI Models
Abstract
In many sports disciplines, artificial intelligence (AI) has emerged as a crucial tool for injury detection and prevention. In order to minimize negative events, maximize athletic performance, and improve rehabilitation outcomes, modern sports medicine is placing a greater emphasis on the significance of predicting injury risks. Tailored training and recovery
plans that are based on precise injury risk assessments have been demonstrated to lower injury rates and speed up recovery times. In this study, we tackle the crucial problem of increasing the precision of injury detection and the effectiveness of reaction time. Our suggested approach consists of two steps:
first, we use Recurrent Neural Networks (RNNs) to train a model, and then we use the Random Forest algorithm to choose features. In our trials, we assessed five machine learning models: AdaBoost, Random Forest, CatBoost, XGBoost, and Decision Tree. The accuracy of the suggested method was a noteworthy 99 Using the Z3 SMT solver, we used formal verification approaches to make sure the injury prediction system was accurate and reliable. We verified that the model’s predictions
stayed within predetermined safety bounds after encoding limitations over physiological data inputs. In order to confirm that risk classification rules cannot result in unsafe states, we also used Alloy to evaluate the decision logic.
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