Physiological-Model-Based Neural Network for Heart Rate Estimation during Daily Physical Activities
This work addresses the need for personalized and interpretable heart rate monitoring for early heart failure detection, though it is incremental as it combines existing physiological and neural network approaches.
The study tackled the problem of unreliable heart rate estimation for heart failure detection by developing a physiological-model-based neural network (PMB-NN) that embeds physiological constraints, achieving a median R² score of 0.8 and RMSE of 8.3 bpm on individual datasets from 12 participants during daily activities.
Heart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved outcomes. Abnormalities in heart rate (HR), particularly during daily activities, may serve as early indicators of HF risk. However, existing HR monitoring tools for HF detection are limited by their reliability on population-based averages. The estimation of individualized HR serves as a dynamic digital twin, enabling precise tracking of cardiac health biomarkers. Current HR estimation methods, categorized into physiologically-driven and purely data-driven models, struggle with efficiency and interpretability. This study introduces a novel physiological-model-based neural network (PMB-NN) framework for HR estimation based on oxygen uptake (VO2) data during daily physical activities. The framework was trained and tested on individual datasets from 12 participants engaged in activities including resting, cycling, and running. By embedding physiological constraints, which were derived from our proposed simplified human movement physiological model (PM), into the neural network training process, the PMB-NN model adheres to human physiological principles while achieving high estimation accuracy, with a median R$^2$ score of 0.8 and an RMSE of 8.3 bpm. Comparative statistical analysis demonstrates that the PMB-NN achieves performance on par with the benchmark neural network model while significantly outperforming traditional physiological model (p=0.002). In addition, our PMB-NN is adept at identifying personalized parameters of the PM, enabling the PM to generate reasonable HR estimation. The proposed framework with a precise VO2 estimation system derived from body movements enables the future possibilities of personalized and real-time cardiac monitoring during daily life physical activities.