Estimating stress in online meetings by remote physiological signal and behavioral features
Sun, Zhaodong; Vedernikov, Alexander; Kykyri, Virpi-Liisa; Pohjola, Mikko; Nokia, Miriam; Li, Xiaobai (2023-04-24)
Zhaodong Sun, Alexander Vedernikov, Virpi-Liisa Kykyri, Mikko Pohjola, Miriam Nokia, and Xiaobai Li. 2022. Estimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features. In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp/ISWC ’22 Adjunct), September 11–15, 2022, Cambridge, United Kingdom. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3544793.3563406
© 2022 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2023061956334
Tiivistelmä
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%.
Kokoelmat
- Avoin saatavuus [32110]