research-article

Stress recognition in human-computer interaction using physiological and self-reported data: a study of gender differences

Authors:

Alexandros Liapis,

Christos Katsanos,

Dimitris Sotiropoulos,

Michalis Xenos,

Nikos KarousosAuthors Info & Claims

PCI '15: Proceedings of the 19th Panhellenic Conference on Informatics

Pages 323 - 328

https://doi.org/10.1145/2801948.2801964

Published: 01 October 2015 Publication History

Abstract

This paper investigates gender differences in stress recognition in human computer interaction (HCI) for both objective (i.e., skin conductance) and subjective (i.e., valence-arousal VA ratings) metrics. To this end, 31 healthy participants, 18 females, performed five HCI tasks, while their skin conductance was recorded. These selected HCI tasks were the ones listed as the most stressful, by a group of typical computer users, who were involved in a face to face pre-experiment interview for the identification of stressful cases in computer interaction. After each task, participants rated their interaction experience using the valence-arousal scale. The collected data were split based on participants' gender. Skin conductance signals were analyzed using seven popular machine learning classifiers. In both groups the best stress recognition accuracy for all tasks was achieved by Linear Discriminant Analysis LDA; Males: Mean=94.8% and SD=1.5%, Females: Mean=98.9% and SD=0.3%. Self-reported data analysis revealed a significant difference on how both genders communicate their emotions using the arousal scale. Our findings tend to suggest that gender does not affect skin conductance data during subtle HCI tasks. However subjective ratings such as arousal of emotional experience must be utilized carefully.

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Cited By

Onim MRhodus EThapliyal H(2024)A Review of Context-Aware Machine Learning for Stress DetectionIEEE Consumer Electronics Magazine10.1109/MCE.2023.327807613:4(10-16)Online publication date: Jul-2024
https://doi.org/10.1109/MCE.2023.3278076
Purps CHettmann WZylowski TSautchuk-Patrício NHepperle DWölfel M(2024)Exploring Perception and Preference in Public Human-Agent Interaction: Virtual Human Vs. Social RobotArtsIT, Interactivity and Game Creation10.1007/978-3-031-55312-7_25(342-358)Online publication date: 21-Mar-2024
https://doi.org/10.1007/978-3-031-55312-7_25
Barros De Amorim ISantos Dos Santos SMoreira Miranda Da Silva IRios Da Hora Rodrigues KPereira Mota M(2023)Gender Nuances in Human-Computer Interaction ResearchProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638077(1-12)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3638067.3638077
Show More Cited By

Index Terms

Stress recognition in human-computer interaction using physiological and self-reported data: a study of gender differences
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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Published In

cover image ACM Other conferences

PCI '15: Proceedings of the 19th Panhellenic Conference on Informatics

October 2015

438 pages

ISBN:9781450335515

DOI:10.1145/2801948

Editors:
Karanikolas Nikitas N.
Technological Educational Institute of Athens
,
Akoumianakis Demosthenes
Technological Educational Institute of Crete
,
Nikolaidou Mara
Harokopio University of Athens
,
Vergados Dimitris
University of Piraeus
,
Xenos Michalis
Hellenic Open University
,
General Chairs:
Giaglis George
Athens University of Economics and Business
,
Gritzalis Stefanos
University of the Aegean
,
Merakos Lazaros
University of Athens, Greek Universities Network
,
Tsanakas Panagiotis
National Technical University of Athens, Greek Research & Technology Network
,
Sgouropoulou Cleo
Technological Educational Institute of Athens

Copyright © 2015 ACM.

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Published: 01 October 2015

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European Union (Social Fund)
Greek national resources

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PCI '15

PCI '15: 19th Panhellenic Conference on Informatics

October 1 - 3, 2015

Athens, Greece

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PCI '15 Paper Acceptance Rate 64 of 148 submissions, 43%;

Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

Onim MRhodus EThapliyal H(2024)A Review of Context-Aware Machine Learning for Stress DetectionIEEE Consumer Electronics Magazine10.1109/MCE.2023.327807613:4(10-16)Online publication date: Jul-2024
https://doi.org/10.1109/MCE.2023.3278076
Purps CHettmann WZylowski TSautchuk-Patrício NHepperle DWölfel M(2024)Exploring Perception and Preference in Public Human-Agent Interaction: Virtual Human Vs. Social RobotArtsIT, Interactivity and Game Creation10.1007/978-3-031-55312-7_25(342-358)Online publication date: 21-Mar-2024
https://doi.org/10.1007/978-3-031-55312-7_25
Barros De Amorim ISantos Dos Santos SMoreira Miranda Da Silva IRios Da Hora Rodrigues KPereira Mota M(2023)Gender Nuances in Human-Computer Interaction ResearchProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638077(1-12)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3638067.3638077
Can YMahesh BAndré E(2023)Approaches, Applications, and Challenges in Physiological Emotion Recognition—A Tutorial OverviewProceedings of the IEEE10.1109/JPROC.2023.3286445111:10(1287-1313)Online publication date: Oct-2023
https://doi.org/10.1109/JPROC.2023.3286445
Díaz-Boladeras MLlanas XFernandez-Carmona MCatalà AUrdiales CMusté MPérez EBarco À(2023)Perceived distress in assisted gait with a four-wheeled rollator under stress induction conditionsCogent Engineering10.1080/23311916.2023.223374310:1Online publication date: 13-Jul-2023
https://doi.org/10.1080/23311916.2023.2233743
Melissourgos GKatsanos C(2023)Do Not Shoot the Messenger: Effect of System Critical Feedback on User-Perceived UsabilityHuman-Computer Interaction10.1007/978-3-031-35599-8_30(455-467)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35599-8_30
Mendel TSchuster RTromer EToch E(2022)Toward Proactive Support for Older AdultsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172496:1(1-25)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517249
Freihaut PGöritz ARockstroh CBlum J(2021)Tracking stress via the computer mouse? Promises and challenges of a potential behavioral stress markerBehavior Research Methods10.3758/s13428-021-01568-853:6(2281-2301)Online publication date: 5-Apr-2021
https://doi.org/10.3758/s13428-021-01568-8
Jaiswal DChatterjee DGavas RKumar Ramakrishnan RPal A(2021)Person and Stressor Independent Generic Model for Stress Detection Using GSR2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC46164.2021.9630615(7195-7198)Online publication date: 1-Nov-2021
https://doi.org/10.1109/EMBC46164.2021.9630615
Can YGokay DKılıç DEkiz DChalabianloo NErsoy C(2020)How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily LifeSensors10.3390/s2003083820:3(838)Online publication date: 4-Feb-2020
https://doi.org/10.3390/s20030838
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