TY - JOUR
T1 - Real-time Stress Monitoring for Intensive Care Unit (ICU) Nurses
AU - Zhang, Qian
AU - Sasangohar, Farzan
AU - Saravanan, Pratima
AU - Ahmadi, Nima
AU - Nisar, Tariq
AU - Danesh, Valerie
AU - Masud, Faisal
N1 - Publisher Copyright:
© 2022 by Human Factors and Ergonomics Society. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The aim of this study is to explore real-time stress monitoring models (based on physiological features) for intensive care unit (ICU) nurses. The quantification of stress in ICU nurses has been limited to subjective ratings, with a general gap in continuous measurement; real-time stress monitoring based on continuous physiological measurement is needed to assess the negative outcome of stress. Electrod ermal activity, eye tracking, accelerometer data, and skin temperatures were recorded continuously through 12-hour shifts for ICU nurses (23participants). A machine learning algorithm was applied to identify stress over time based on physiological features. eX treme Gradient Boosting (XGBoost) was performed with an accuracy of 0.88. Skin temperature contributed the most to real-time stress identification for monitoring. Future work should investigate the efficacy of using skin temperature for stress identification in real-time for ICU nurses.
AB - The aim of this study is to explore real-time stress monitoring models (based on physiological features) for intensive care unit (ICU) nurses. The quantification of stress in ICU nurses has been limited to subjective ratings, with a general gap in continuous measurement; real-time stress monitoring based on continuous physiological measurement is needed to assess the negative outcome of stress. Electrod ermal activity, eye tracking, accelerometer data, and skin temperatures were recorded continuously through 12-hour shifts for ICU nurses (23participants). A machine learning algorithm was applied to identify stress over time based on physiological features. eX treme Gradient Boosting (XGBoost) was performed with an accuracy of 0.88. Skin temperature contributed the most to real-time stress identification for monitoring. Future work should investigate the efficacy of using skin temperature for stress identification in real-time for ICU nurses.
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U2 - 10.1177/1071181322661457
DO - 10.1177/1071181322661457
M3 - Conference article
AN - SCOPUS:85190944105
SN - 1071-1813
VL - 66
SP - 779
EP - 782
JO - Proceedings of the Human Factors and Ergonomics Society
JF - Proceedings of the Human Factors and Ergonomics Society
IS - 1
T2 - 66th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2022
Y2 - 10 October 2022 through 14 October 2022
ER -