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Towards Participant-Independent Stress Detection Using Instrumented Peripherals

Authors :
Zelun Wang
Ricardo Gutierrez-Osuna
Dennis Rodrigo Dacunhasilva
Source :
IEEE Transactions on Affective Computing. 14:773-787
Publication Year :
2023
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2023.

Abstract

Methods to measure work stress generally rely on subjective measures from questionnaires or require dedicated sensors that are cumbersome to wear and interfere with the task. To address this problem, we propose a method to detect stress unobtrusively using commodity devices (keyboards, mice) instrumented with pressure sensors. We propose a minimalist design that can be easily replicated by other researchers using off-the-shelf and low-cost hardware. We validate the design in a laboratory experiment that simulates office tasks and mild stressors while avoiding methodological limitations of previous studies. We compare stress-detection performance when using conventional features reported in the literature (keystroke dynamics, mouse trajectories) augmented with information from pressure sensors. Our results indicate that pressure provides additional information for stress discrimination; adding pressure information to keystroke dynamics and mouse trajectories improves classification performance by 6% and 3%, respectively. These results show how devices that are already part of the modern workplace may be used and enhanced to automatically and unobtrusively detect stress.

Details

ISSN :
23719850
Volume :
14
Database :
OpenAIRE
Journal :
IEEE Transactions on Affective Computing
Accession number :
edsair.doi...........f8d7e39f4827566978a93f53ccbcb185