Next Article in Journal
Development and Application of Aptamer-Based Surface-Enhanced Raman Spectroscopy Sensors in Quantitative Analysis and Biotherapy
Previous Article in Journal
Selected Papers from the 9th World Congress on Industrial Process Tomography
Previous Article in Special Issue
An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work
Open AccessArticle

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

1
Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria
2
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
3
Department of Cardiology, University Hospital Zurich, 8091 Zurich, Switzerland
4
Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
5
Department of Demography, Faculty of Spatial Sciences, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands
6
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3805; https://doi.org/10.3390/s19173805
Received: 29 July 2019 / Revised: 28 August 2019 / Accepted: 31 August 2019 / Published: 3 September 2019
(This article belongs to the Special Issue Sensors for Affective Computing and Sentiment Analysis)
There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans. View Full-Text
Keywords: stress detection; rule-based algorithm; physiological wearable sensors; real-world field studies; perceived stress stress detection; rule-based algorithm; physiological wearable sensors; real-world field studies; perceived stress
Show Figures

Figure 1

MDPI and ACS Style

Kyriakou, K.; Resch, B.; Sagl, G.; Petutschnig, A.; Werner, C.; Niederseer, D.; Liedlgruber, M.; Wilhelm, F.H.; Osborne, T.; Pykett, J. Detecting Moments of Stress from Measurements of Wearable Physiological Sensors. Sensors 2019, 19, 3805.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop