From Laboratory to Everyday Life: Personalized Stress Prediction via Smartwatches

Abstract

Accurate prediction of stress in everyday life is essential to prevent chronic stress and maintain health and well-being through early and personalized intervention. With the goal of enabling reliable prediction suitable for everyday life, we present MuStP, a two-stage machine learning pipeline designed to predict stress from low-resolution heart rate (HR) and high-resolution electrocardiography (ECG) measurements from commercial smartwatches. Our model is pre-trained with labeled data collected in a controlled laboratory stress study. Subsequently, we transfer the model for everyday use, enabling it to operate with everyday smartwatch data in various environments. The model transfer strategy effectively addresses the domain shift from laboratory data to highly imbalanced smartwatch data and allows personalization. The empirical results on smartwatch data show that MuStP can predict stress everyday with an F1 score of $0.52$, despite the measurements having sparse labels for stress.