Stress Pattern Recognition Through Wearable Biosensors in the Workplace: Experimental Longitudinal Study on the Role of Motion Intensity


Ressource 1Download: SDS2019_VadymMozgovoy_Serval.pdf (395.89 [Ko])
State: Public
Version: Author's accepted manuscript
License: All rights reserved
Serval ID
Inproceedings: an article in a conference proceedings.
Stress Pattern Recognition Through Wearable Biosensors in the Workplace: Experimental Longitudinal Study on the Role of Motion Intensity
Title of the conference
2019 6th Swiss Conference on Data Science (SDS)
Mozgovoy Vadym
Publication state
Issued date
This research has been supported by the Swiss National Science Foundation (SNSF) grant no. 172740.
The author thanks Prof. Ronchetti, during whose class “Selected Topics in Statistics” this project has been developed and presented in the Fall 2018.
Stress is a current issue in the workplace, manifesting itself through both psychological and physiological reactions. Biosensors might improve stress monitoring in the workplace, when employees become wearable device users. Yet, it remains unclear how to identify stress patterns through biosensors without direct observation of the users’ activities. In particular, non-physiological aspects of employee activities altering physiological reactions, such as motion activity, may also be associated with stress measures. This longitudinal experimental study examines remote stress identification by testing whether a non-physiological signal of physical activity may improve the classification of stress-related physiological data collected through biosensors. The participants are 18 employees from Public Administration sector wearing biometric devices for around two months in the workplace. This study investigates the stress-related data classification, using established physiological measures (Galvanic Skin Response and Heart Rate) combined with a new non-physiological measure, associated with the user’s physical activity (Motion Activity). Stress-related patterns are explored through unsupervised learning approach with help of Gaussian Mixture Model and K-Means classification analysis, completed by the bootstrap confidence intervals for evaluating uncertainty of classification. The results demonstrate that complementing physiological signals with a non-physiological signal, such as a physical activity-related information, improves stress pattern recognition through detection of emotional overarousal, arousal, and relaxation. These findings are especially promising in the context of the use of wearable devices for stress management, when stress-monitoring is done remotely and user’ activity is not directly observed during measurements. Further research and cross-validation procedures should be used for building stress-identification algorithms for remote stress monitoring that include physiological and non-physiological signals. Better understanding of stress measures may enhance the quality of stress management data collection processes through Information Systems, involved in the use of wearable devices in the workplace, and strengthen the data governance.
data governance, stress, wearable biosensor, pattern recognition, classification
Publisher's website
Create date
18/08/2019 20:45
Last modification date
21/08/2019 7:09
Usage data