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A Multimodal Approach to Improve the Robustness of Physiological Stress Prediction During Physical Activity
- Source :
- SMC
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- Stress is well known to have negative effects on health and workplace performance. Physiological sensing using wearables shows in turn great potential for realtime stress monitoring. While some off-the-shelf consumer products (e.g. smartwatches) already feature stress detection, there is still a pressing need to improve the robustness of these models in ecological settings where physical activity can hamper detection accuracy. In this paper, we show that using a multimodal physiological stress model can not only improve model accuracy, but can increase robustness to physical activity inference. To do so, we propose a video game based method to elicit emotional responses. More specifically, 48 participants played video games in which psychological stress and physical activity were jointly modulated. Physiological features showing robustness to stress are analyzed in order to guide further research.
- Subjects :
- business.industry
Computer science
0206 medical engineering
Physical activity
Wearable computer
Inference
02 engineering and technology
Machine learning
computer.software_genre
medicine.disease_cause
020601 biomedical engineering
03 medical and health sciences
0302 clinical medicine
Robustness (computer science)
Task analysis
medicine
Psychological stress
Artificial intelligence
business
computer
Video game
030217 neurology & neurosurgery
Physiological stress
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
- Accession number :
- edsair.doi...........e9567f5838e89ef63d97b43620a399ba
- Full Text :
- https://doi.org/10.1109/smc.2019.8914254