Federated Learning Approaches for Joint Biomechanics and Sports Injury Prediction in Recurrent Neural Model

Abstract

Conventional approaches to sports injury prevention and recovery mostly rely on standardized intervention regimens and subjective clinician-guided evaluations. These methods frequently lead to inadequate personalization, slow response times, and poor accuracy. Recent developments in wearable sensors, multimodal analytics, and AI offer new possibilities for achieving goals, real-time, and customized injury treatment techniques. This study proposes a Federated Learning-based Recurrent Neural Network (FL-RNN) framework for joint biomechanics analysis and sports injury prediction. The proposed approach enables decentralized model training across multiple data sources while preserving athlete data privacy. Biomechanical and historical injury data collected from wearable sensors and physical training records are processed to capture temporal movement patterns. A Recurrent Neural Network (RNN) is employed to model sequential dependencies in athlete movement data and learn temporal characteristics associated with injury risk and rehabilitation progress. The federated learning strategy aggregates locally trained model parameters without sharing sensitive athlete information, thereby enhancing data security and model generalization. The proposed framework also incorporates historical injury records and biomechanical indicators to provide personalized injury risk prediction and support rehabilitation monitoring. This study compares the RNN model with the current Support Vector Machine (SVM) with regard to the impact of physical education and sports therapy.