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Discriminating stress from cognitive load using a wearable EDA device

Authors:

Johannes Schumm,

Roberto La Marca,

Gerhard Tröster,

Ulrike EhlertAuthors Info & Claims

IEEE Transactions on Information Technology in Biomedicine, Volume 14, Issue 2

Pages 410 - 417

https://doi.org/10.1109/TITB.2009.2036164

Published: 01 March 2010 Publication History

Abstract

The inferred cost of work-related stress call for prevention strategies that aim at detecting early warning signs at the workplace. This paper goes one step towards the goal of developing a personal health system for detecting stress. We analyze the discriminative power of electrodermal activity (EDA) in distinguishing stress from cognitive load in an office environment. A collective of 33 subjects underwent a laboratory intervention that included mild cognitive load and two stress factors, which are relevant at the workplace: mental stress induced by solving arithmetic problems under time pressure and psychosocial stress induced by social-evaluative threat. During the experiments, a wearable device was used to monitor the EDA as a measure of the individual stress reaction. Analysis of the data showed that the distributions of the EDA peak height and the instantaneous peak rate carry information about the stress level of a person. Six classifiers were investigated regarding their ability to discriminate cognitive load from stress. A maximum accuracy of 82.8% was achieved for discriminating stress from cognitive load. This would allow keeping track of stressful phases during a working day by using a wearable EDA device.

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Cited By

Ghose DGitelson OScassellati B(2024)Integrating Multimodal Affective Signals for Stress Detection from Audio-Visual DataProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685717(22-32)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685717
Neigel PVargo ATag BKise KKostakos VKay JHoang T(2024)Using Wearables to Unobtrusively Identify Periods of Stress in a Real University EnvironmentProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676608(17-24)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676608
Mukhopadhyay SSharma VJaiswal DDey SGhose AKostakos VKay JHoang T(2024)TinyStressNAS: Automated Feature Selection and Model Generation for On-device Stress DetectionCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678497(430-436)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678497
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Published In

cover image IEEE Transactions on Information Technology in Biomedicine

IEEE Transactions on Information Technology in Biomedicine Volume 14, Issue 2

Special section on affective and pervasive computing for healthcare

March 2010

358 pages

ISSN:1089-7771

Issue’s Table of Contents

Copyright © 2010.

Publisher

IEEE Press

Publication History

Published: 01 March 2010

Revised: 17 August 2009

Received: 21 January 2009

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Cited By

Ghose DGitelson OScassellati B(2024)Integrating Multimodal Affective Signals for Stress Detection from Audio-Visual DataProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685717(22-32)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685717
Neigel PVargo ATag BKise KKostakos VKay JHoang T(2024)Using Wearables to Unobtrusively Identify Periods of Stress in a Real University EnvironmentProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676608(17-24)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676608
Mukhopadhyay SSharma VJaiswal DDey SGhose AKostakos VKay JHoang T(2024)TinyStressNAS: Automated Feature Selection and Model Generation for On-device Stress DetectionCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678497(430-436)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678497
Bhatti ABehinaein BHungler PEtemad A(2024)AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion RecognitionACM Transactions on Computing for Healthcare10.1145/36537225:3(1-24)Online publication date: 18-Sep-2024
https://dl.acm.org/doi/10.1145/3653722
Atiq ZBatra R(2024)How Do First-Year Engineering Students’ Emotions Change while Working on Programming Problems?ACM Transactions on Computing Education10.1145/364386524:2(1-30)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3643865
Alchieri LAbdalazim NAlecci LGashi SGjoreski MSantini S(2024)Lateralization Effects in Electrodermal Activity Data Collected Using Wearable DevicesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435418:1(1-30)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643541
Xiao YSharma HZhang ZBergen-Cico DRahman TSalekin A(2024)Reading Between the HeatProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314417:4(1-30)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631441
Vatsal RMishra SThareja RChakrabarty MSharma OShukla J(2024)An Analysis of Physiological and Psychological Responses in Virtual Reality and Flat Screen GamingIEEE Transactions on Affective Computing10.1109/TAFFC.2024.336870315:3(1696-1710)Online publication date: 1-Jul-2024
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Awada MBecerik-Gerber BLucas GRoll SLiu R(2024)A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and DistressIEEE Transactions on Affective Computing10.1109/TAFFC.2023.332491015:3(1153-1165)Online publication date: 1-Jul-2024
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Zammouri AMoussa AChevallier S(2024)Use of cognitive load measurements to design a new architecture of intelligent learning systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121253237:PAOnline publication date: 27-Feb-2024
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