<ul data-eligibleForWebStory="true"><li>Heart failure is a global health challenge, and early detection is crucial.</li><li>Abnormal heart rate during daily activities could indicate heart failure risk.</li><li>Current heart rate monitoring tools rely on population averages.</li><li>A novel method, PMB-NN, uses a physiological-model-based neural network for heart rate estimation.</li><li>The framework is trained on oxygen uptake data from 12 participants in various physical activities.</li><li>The PMB-NN model adheres to human physiological principles, achieving high accuracy.</li><li>It has a median R$^2$ score of 0.8 and an RMSE of 8.3 bpm.</li><li>Comparative analysis shows PMB-NN performs as well as benchmark models and outperforms traditional physiological models.</li><li>The PMB-NN can identify personalized parameters and provide accurate heart rate estimations.</li><li>The framework allows for personalized and real-time cardiac monitoring during daily physical activities.</li></ul>

Physiological-Model-Based Neural Network for Heart Rate Estimation during Daily Physical Activities

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