Developers Build Transformer-Based AI System to Predict Overtraining Before It Strikes

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A tutorial published on DEV Community outlines how to build a deep learning pipeline that predicts Overtraining Syndrome (OTS) in athletes before symptoms fully emerge. The system uses Heart Rate Variability (HRV) data — specifically R-R intervals extracted from Apple HealthKit — to detect early signs of physiological stress. Raw biometric data is preprocessed using Pandas to calculate RMSSD, a standard HRV metric, and then fed into a Transformer Encoder model built with PyTorch. The Transformer's self-attention mechanism is used because it can identify long-term dependencies in sequential heartbeat data more effectively than traditional RNN or LSTM models. The end result is a fatigue threshold classifier that generates an early warning signal when overtraining risk is detected.

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ProgrammingDEV Community ·

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