Modelling Naturalistic Work Stress Using Spectral HRV Representations and Deep Learning

Abstract

With the proliferation of wearable devices and the inflow of new health data, artificial intelligence is expected to revolutionize the field of wellness and health management by providing potential tools for analyzing harmful conditions like prolonged stress. Currently, one of the standard measurements used by medical practitioners to measure stress is heart rate variability (HRV), a set of numerical indices that reflect autonomic balance. However, recent advances in machine learning have shown that learned features tend to outperform hand-crafted features. In this work we propose a more expressive intermediate data representation based on Lomb-Scargle periodograms combined with the feature learning capabilities of deep learning. Using stress data from naturalistic work activities, we tested different shallow and deep learning architectures and show that significant improvements can be achieved compared to traditional HRV indices. Results show that models trained on our spectral-temporal representation significantly outperform models trained on traditional HRV indices for predicting naturalistic work stress.