A multimodal sensor dataset for continuous stress detection of nurses in a hospital

TL;DR

This paper introduces a comprehensive multimodal sensor dataset collected from nurses in a hospital during COVID-19, capturing physiological signals and contextual data to facilitate stress detection research.

Contribution

It provides a unique, publicly available dataset of physiological and contextual data for stress detection in healthcare workers in natural settings.

Findings

Collected biometric data during COVID-19 pandemic

Captured physiological signals and stress-related context

Dataset is publicly available for research use

Abstract

Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specifc physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.