Abstract

Hospital workers are known to work long hours in a highly stressful environment. The COVID-19 pandemic has increased this burden multi-fold. Pre-COVID statistics already showed that one in every three nurses reported burnout, thus affecting patient satisfaction and the quality of their provided service. Real-time monitoring of burnout, and other underlying factors, such as stress, could provide feedback not only to the clinical staff, but also to hospital administrators, thus allowing for supportive measures to be taken early. In this paper, we present a context-aware speech-based system for stress detection. We consider data from 144 hospital workers who were monitored during their daily shifts over a 10-week period; subjective stress readings were collected daily. Wearable devices measured speech features and physiological readings, such as heart rate. Environment sensors, in turn, were used to track staff …

Date

2021

Authors

Amr Gaballah, Abhishek Tiwari, Shrikanth Narayanan, Tiago H Falk

Conference

ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

8348-8352

Publisher

IEEE