Detecting moments of stress from measurements of wearable physiological sensors

Research output: Contribution to journalArticle

Standard

Detecting moments of stress from measurements of wearable physiological sensors. / Resch, Bernd; Kyriakou , Kalliopi ; Sagl, Gunther; Petutschnig , Andreas ; Werner , Christian ; Niederseer, David ; Liedlgruber , Michael ; Wilhelm, Frank ; osborne, tess; Pykett, Jessica.

In: Sensors, Vol. 19, No. 17, 3805, 03.09.2019.

Research output: Contribution to journalArticle

Harvard

Resch, B, Kyriakou , K, Sagl, G, Petutschnig , A, Werner , C, Niederseer, D, Liedlgruber , M, Wilhelm, F, osborne, T & Pykett, J 2019, 'Detecting moments of stress from measurements of wearable physiological sensors', Sensors, vol. 19, no. 17, 3805. https://doi.org/10.3390/s19173805

APA

Resch, B., Kyriakou , K., Sagl, G., Petutschnig , A., Werner , C., Niederseer, D., Liedlgruber , M., Wilhelm, F., osborne, T., & Pykett, J. (2019). Detecting moments of stress from measurements of wearable physiological sensors. Sensors, 19(17), [3805]. https://doi.org/10.3390/s19173805

Vancouver

Resch B, Kyriakou K, Sagl G, Petutschnig A, Werner C, Niederseer D et al. Detecting moments of stress from measurements of wearable physiological sensors. Sensors. 2019 Sep 3;19(17). 3805. https://doi.org/10.3390/s19173805

Author

Resch, Bernd ; Kyriakou , Kalliopi ; Sagl, Gunther ; Petutschnig , Andreas ; Werner , Christian ; Niederseer, David ; Liedlgruber , Michael ; Wilhelm, Frank ; osborne, tess ; Pykett, Jessica. / Detecting moments of stress from measurements of wearable physiological sensors. In: Sensors. 2019 ; Vol. 19, No. 17.

Bibtex

@article{dd02e9442d994d7987e7f620abb8696d,
title = "Detecting moments of stress from measurements of wearable physiological sensors",
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{\textquoteright}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{\textquoteright}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 = "stress detection, rule-based algorithm, physiological wearable sensors, real-world field studies, perceived stress",
author = "Bernd Resch and Kalliopi Kyriakou and Gunther Sagl and Andreas Petutschnig and Christian Werner and David Niederseer and Michael Liedlgruber and Frank Wilhelm and tess osborne and Jessica Pykett",
year = "2019",
month = sep,
day = "3",
doi = "10.3390/s19173805",
language = "English",
volume = "19",
journal = "Sensors",
issn = "1424-8220",
publisher = "MDPI",
number = "17",

}

RIS

TY - JOUR

T1 - Detecting moments of stress from measurements of wearable physiological sensors

AU - Resch, Bernd

AU - Kyriakou , Kalliopi

AU - Sagl, Gunther

AU - Petutschnig , Andreas

AU - Werner , Christian

AU - Niederseer, David

AU - Liedlgruber , Michael

AU - Wilhelm, Frank

AU - osborne, tess

AU - Pykett, Jessica

PY - 2019/9/3

Y1 - 2019/9/3

N2 - 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.

AB - 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.

KW - stress detection

KW - rule-based algorithm

KW - physiological wearable sensors

KW - real-world field studies

KW - perceived stress

U2 - 10.3390/s19173805

DO - 10.3390/s19173805

M3 - Article

VL - 19

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 17

M1 - 3805

ER -