Predicting athletic injuries with deep Learning: Evaluating CNNs and RNNs for enhanced performance and Safety

Abstract

Identifying and predicting sports injuries is crucial for managing athletes’ performance and health. Recent advancements in deep learning have emerged as powerful tools for analyzing complex data and detecting injury patterns. This study investigates the effectiveness of deep learning algorithms, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), in identifying and predicting injury patterns in athletes. Biometric data and motion videos from training sessions were collected and analyzed, focusing on RNN architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The models were trained on diverse datasets and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The results indicate that the LSTM model achieved the highest accuracy at 91.5%, outperforming both the GRU model (90.8%) and the CNN model (89.2%). The precision and recall rates for the LSTM model were 89.7% and 88.3%, respectively, solidifying its superiority in the precise identification of potential injury patterns compared to CNNs. These findings highlight the capability of deep learning algorithms, particularly RNNs, in effectively predicting and managing sports injuries. This research emphasizes the importance of leveraging deep learning techniques for injury prevention and suggests future studies should focus on enhancing model accuracy through diverse and comprehensive datasets.