| Literature DB >> 33067468 |
Karel Mundnich1, Brandon M Booth2, Michelle L'Hommedieu2, Tiantian Feng2, Benjamin Girault2, Justin L'Hommedieu2, Mackenzie Wildman3, Sophia Skaaden4, Amrutha Nadarajan2, Jennifer L Villatte5, Tiago H Falk6, Kristina Lerman4, Emilio Ferrara4, Shrikanth Narayanan2,4.
Abstract
We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings. We collected behavioral and physiological data from n = 212 participants through Internet-of-Things Bluetooth data hubs, wearable sensors (including a wristband, a biometrics-tracking garment, a smartphone, and an audio-feature recorder), together with a battery of surveys to assess personality traits, behavioral states, job performance, and well-being over time. Besides the default use of the data set, we envision several novel research opportunities and potential applications, including multi-modal and multi-task behavioral modeling, authentication through biometrics, and privacy-aware and privacy-preserving machine learning.Entities:
Year: 2020 PMID: 33067468 PMCID: PMC7567859 DOI: 10.1038/s41597-020-00655-3
Source DB: PubMed Journal: Sci Data ISSN: 2052-4463 Impact factor: 6.444