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. 2019 Sep 3;19(17):3805.
doi: 10.3390/s19173805.

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Kalliopi Kyriakou  1 , Bernd Resch  2 , Günther Sagl  3 , Andreas Petutschnig  3 , Christian Werner  3 , David Niederseer  4 , Michael Liedlgruber  5 , Frank Wilhelm  5 , Tess Osborne  6 , Jessica Pykett  7
Affiliations

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Kalliopi Kyriakou et al. Sensors (Basel). .

Abstract

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.

Keywords: perceived stress; physiological wearable sensors; real-world field studies; rule-based algorithm; stress detection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Methodology flowchart for the development of the algorithm for MOS detection.
Figure 2
Figure 2
Schematic GSR to a hypothetical stimulus.
Figure 3
Figure 3
A typical example of a time series plot for a participant.
Figure 4
Figure 4
Participants’ self-report perceived stress for ten stressors.
Figure 5
Figure 5
Validation of detected “stressful” areas by the mixed-methods approach.
Figure 6
Figure 6
Hotspot maps of detected MOS, phase 1 (on the left), and phase 2 (on the right), direction to the city.
Figure 7
Figure 7
Hotspot maps of detected MOS, phase 1 (on the left), and phase 2 (on the right), direction from the city.
Figure 8
Figure 8
Hotspots of pedestrians’ MOS in different cities: (a) Salzburg; (b) Cologne.
Figure 8
Figure 8
Hotspots of pedestrians’ MOS in different cities: (a) Salzburg; (b) Cologne.
Figure 9
Figure 9
Validation of self-reported stress by the algorithm’s results.
See this image and copyright information in PMC

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