Towards Participant-Independent Stress Detection Using Instrumented Peripherals
| Autor: | Zelun Wang, Ricardo Gutierrez-Osuna, Dennis Rodrigo Dacunhasilva |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Measure (data warehouse)
Computer science 020207 software engineering 02 engineering and technology Pressure sensor Task (project management) Human-Computer Interaction Keystroke dynamics Work stress Stress (linguistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Laboratory experiment Software Simulation |
| Zdroj: | IEEE Transactions on Affective Computing. 14:773-787 |
| ISSN: | 2371-9850 |
| DOI: | 10.1109/taffc.2021.3061417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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