<scp>TransformHR</scp> : A Novel Approach to Athlete Heart Rate Monitoring With Transformer‐Based Wearable Technology

ABSTRACT This study presents an innovative Transformer‐based deep learning model for heart rate estimation in wearable devices, addressing accuracy challenges during high‐intensity activities. Our model, utilizing self‐attention mechanisms and energy‐efficient computational strategies, achieves a mean absolute error (MAE) of 4.0–4.2 beats per minute and correlation coefficients of 0.92–0.93 on public datasets PPG‐DaLiA and WESAD. On our more challenging proprietary athlete dataset, the model maintains excellent performance with an MAE of 4.3 beats per minute and a correlation coefficient of 0.91. Ablation studies demonstrate the importance of attention mechanisms, positional encoding, and Gaussian‐weighted loss functions to model performance. These results highlight the potential of our approach in improving heart rate monitoring accuracy in wearable technologies, particularly for high‐intensity athletic data, showcasing its adaptability and effectiveness in real‐world athletic scenarios.