<![CDATA[Running-related injuries are a common sports health issue that can impair athletic performance and potentially terminate an athlete’s career. Early injury detection is therefore critical, as injuries are cumulative in nature and influenced by training load patterns over time. Consequently, data-driven predictive approaches based on time-series analysis are required to support athlete monitoring systems with a safety-oriented focus. This study aims to develop an efficient, accurate, and safety-first injury prediction model for running athletes. The study utilizes daily running activity time-series data obtained from Kaggle. The proposed model is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM) architecture to capture bidirectional temporal dependencies, combined with Focal Loss to address extreme class imbalance. In addition, domain-specific feature engineering is applied through the Acute:Chronic Workload Ratio (ACWR). Model performance is evaluated against tabular-data-based models, namely XGBoost and Balanced Bagging, across multiple experimental configurations. Experimental results indicate that the lightweight Bi-LSTM configuration achieves a Recall of 90.7%, outperforming the benchmark models while maintaining a competitive AUC. These findings demonstrate that sequential modeling is more effective in detecting rare injury events. Overall, this study confirms that Bi-LSTM-based sequential modeling is well suited for early detection of running injuries and suggests its potential applicability in athlete monitoring systems that prioritize safety.]]>
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