Abstract

Healthcare workers often experience stress and burnout due to the demanding job responsibilities and long work hours. Ambulatory monitoring devices, such as wearable and environmental sensors, combined with machine learning algorithms can afford us a better understanding of the naturalistic onset and evolution of stress and emotional reactivity in real-life with valuable implications in behavioral interventions. However, the typically large degree of inter-subject variability, due to individual differences in responses and behaviors, makes it difficult for machine learning models to robustly learn behavioral signal patterns and adequately generalize to unseen individuals. In this study, we design group-specific models of well-being (i.e., stress, sleep, positive affect, negative affect) and contextual outcomes (i.e., type of activity) based on real-life multimodal longitudinal data collected in situ from healthcare workers in …

Date

2020

Authors

Vinesh Ravuri, Projna Paromita, Karel Mundnich, Amrutha Nadarajan, Brandon M Booth, Shrikanth S Narayanan, Theodora Chaspari

Conference

2020 Second International Conference on Transdisciplinary AI (TransAI)

Pages

115-118

Publisher

IEEE