A multi-modal sensor dataset for continuous stress detection of nurses in a hospital

Advances in wearable technologies provide the opportunity to monitor many
physiological variables continuously. Stress detection has gained
increased attention in recent years, especially 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 the influence of many social, cultural, and individuals
experience in dealing with stressful conditions. In order to address these
concerns, we captured both the physiological data and associated context
pertaining to the stress events. We monitored specific physiological
variables, including electrodermal activity, heart rate, skin temperature,
and accelerometer data 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 available upon request.