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.

References

[1]

Bailenson, J.N., Pontikakis, E.D., Mauss, I.B., Gross, J.J., Jabon, M.E., Hutcherson, C.A.C., Nass, C. and John, O. 2008. Real-time classification of evoked emotions using facial feature tracking and physiological responses. International Journal of Human-Computer Studies. 66, 5 (May 2008), 303--317.

Digital Library

[2]

Cacioppo, J.T. and Tassinary, L.G. 1990. Inferring psychological significance from physiological signals. The American Psychologist. 45, 1 (Jan. 1990), 16--28.

[3]

Chanel, G., Rebetez, C., Bétrancourt, M. and Pun, T. 2011. Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans. 41, 6 (2011), 1052--1063.

Digital Library

[4]

Drachen, A., Nacke, L.E., Yannakakis, G. and Pedersen, A.L. 2010. Correlation Between Heart Rate, Electrodermal Activity and Player Experience in First-person Shooter Games. Proceedings of the 5th ACM SIGGRAPH Symposium on Video Games (New York, NY, USA, 2010), 49--54.

Digital Library

[5]

Gross, J.J. and Levenson, R.W. 1993. Emotional suppression: physiology, self-report, and expressive behavior. Journal of Personality and Social Psychology. 64, 6 (Jun. 1993), 970--986.

[6]

Healey, J.A. and Picard, R.W. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems. 6, 2 (Jun. 2005), 156--166.

Digital Library

[7]

J. Healey 2000. Wearable and Automotive Systems for the Recognition of Affect from Physiology. MIT.

[8]

Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A. and Patras, I. 2012. DEAP: A Database for Emotion Analysis; Using Physiological Signals. IEEE Transactions on Affective Computing. 3, 1 (2012), 18--31.

Digital Library

[9]

Kring, A.M. and Gordon, A.H. 1998. Sex differences in emotion: expression, experience, and physiology. Journal of Personality and Social Psychology. 74, 3 (Mar. 1998), 686--703.

[10]

Lang, P.J., Greenwald, M.K., Bradley, M.M. and Hamm, A.O. 1993. Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology. 30, 3 (May 1993), 261--273.

[11]

Lazar, D.J., Feng, D.J.H. and Hochheiser, D.H. 2010. Research Methods in Human-Computer Interaction. John Wiley & Sons.

Digital Library

[12]

Liapis, A., Karousos, N., Katsanos, C. and Xenos, M. 2014. Evaluating User's Emotional Experience in HCI: The PhysiOBS Approach. Human-Computer Interaction. Advanced Interaction Modalities and Techniques. M. Kurosu, ed. Springer International Publishing. 758--767.

[13]

Liapis, A., Katsanos, C., Sotiropoulos, D., Xenos, M. and Karousos, N. 2015. Recognizing emotions in human computer interaction: Studying stress using skin conductance. Human-Computer Interaction -- INTERACT 2015. Springer Berlin Heidelberg.

[14]

Lin, T., Omata, M., Hu, W. and Imamiya, A. 2005. Do Physiological Data Relate to Traditional Usability Indexes? Proceedings of the 17th Australia Conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future (Narrabundah, Australia, Australia, 2005), 1--10.

Digital Library

[15]

Lunn, D. and Harper, S. 2010. Using Galvanic Skin Response Measures to Identify Areas of Frustration for Older Web 2.0 Users. Proceedings of the 2010 International Cross Disciplinary Conference on Web Accessibility (W4A) (New York, NY, USA, 2010), 34:1--34:10.

Digital Library

[16]

Mandryk, R.L. and Atkins, M.S. 2007. A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies. 65, 4 (2007), 329--347.

Digital Library

[17]

Partala, T. and Surakka, V. 2003. Pupil size variation as an indication of affective processing. International Journal of Human-Computer Studies. 59, 1--2 (Jul. 2003), 185--198.

Digital Library

[18]

Russell, J.A., Weiss, A. and Mendelsohn, G.A. 1989. Affect Grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology. 57, 3 (1989), 493--502.

[19]

Scheirer, J., Fernandez, R., Klein, J. and Picard, R.W. 2001. Frustrating the User On Purpose: A Step Toward Building an Affective Computer.

[20]

Stone, A.A. and Shiffman, S. 1994. Ecological momentary assessment (EMA) in behavorial medicine. Annals of Behavioral Medicine. 16, 3 (1994), 199--202.

[21]

Vermeeren, A.P.O.S., Law, E.L.-C., Roto, V., Obrist, M., Hoonhout, J. and Väänänen-Vainio-Mattila, K. 2010. User Experience Evaluation Methods: Current State and Development Needs. Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries (New York, NY, USA, 2010), 521--530.

Digital Library

[22]

Ward, R.D. and Marsden, P.H. 2004. Affective computing: problems, reactions and intentions. Interacting with Computers. 16, 4 (Jan. 2004), 707--713.

[23]

Zhou, F., Qu, X., Helander, M.G. and Jiao, J. (Roger) 2011. Affect prediction from physiological measures via visual stimuli. International Journal of Human-Computer Studies. 69, 12 (Dec. 2011), 801--819.

Digital Library

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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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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