Estimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features
Abstract
Work stress impacts people’s daily lives. Their well-being can be improved if the stress is monitored and addressed in time. Attaching physiological sensors are used for such stress monitoring and analysis. Such approach is feasible only when the person is physically presented. Due to the transfer of the life from offline to online, caused by the COVID-19 pandemic, remote stress measurement is of high importance. This study investigated the feasibility of estimating participants’ stress levels based on remote physiological signal features (rPPG) and behavioral features (facial expression and motion) obtained from facial videos recorded during online video meetings. Remote physiological signal features provided higher accuracy of stress estimation (78.75%) as compared to those based on motion (70.00%) and facial expression (73.75%) features. Moreover, the fusion of behavioral and remote physiological signal features increased the accuracy of stress estimation up to 82.50%.
Main Authors
Format
Conferences
Conference paper
Published
2022
Subjects
Publication in research information system
Publisher
ACM
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202304262723Use this for linking
Parent publication ISBN
978-1-4503-9423-9
Review status
Peer reviewed
DOI
https://doi.org/10.1145/3544793.3563406
Conference
ACM International Joint Conference on Pervasive and Ubiquitous Computing
Language
English
Is part of publication
UbiComp/ISWC '22 Adjunct : Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers
Citation
- Sun, Z., Vedernikov, A., Kykyri, V.-L., Pohjola, M., Nokia, M., & Li, X. (2022). Estimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features. In UbiComp/ISWC '22 Adjunct : Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers (pp. 216-220). ACM. https://doi.org/10.1145/3544793.3563406
License
Funder(s)
Finnish Work Environment Fund
Funding program(s)
Others
Muut
Additional information about funding
The study was supported by the Finnish Work Environment Fund (Project 200414 and 200337) and the Academy of Finland (Project 323287 and 345948). The authors also acknowledge CSC-IT Center for Science, Finland, for providing computational resources.
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