Modeling personalized heart rate response to exercise and environmental factors with wearables data.

ABSTRACT: Heart rate (HR) response to workout intensity reflects fitness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory fitness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy-but ubiquitous-data from wearables. We propose a hybrid approach that combines a physiological model with flexible neural network components to learn a personalized, multidimensional representation of fitness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental

SUBMITTER: Nazaret A 

PROVIDER: S-EPMC10651837 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature