research-article

The SWELL Knowledge Work Dataset for Stress and User Modeling Research

Authors:

Saskia Koldijk,

Suzan Verberne,

Mark A. Neerincx,

Wessel KraaijAuthors Info & Claims

ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction

Pages 291 - 298

https://doi.org/10.1145/2663204.2663257

Published: 12 November 2014 Publication History

Abstract

This paper describes the new multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. The dataset made available not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is a valuable contribution to several research fields, such as work psychology, user modeling and context aware systems.

Supplementary Material

suppl.mov (icmi2189-file3.mp4)

Supplemental video

Download
15.09 MB

References

[1]

M. M. Bradley and P. J. Lang. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of behavior therapy and experimental psychiatry, 25(1):49--59, 1994.

[2]

E. Demerouti, A. B. Bakker, S. A. Geurts, and T. W. Taris. Daily recovery from work-related effort during non-work time. Research in Occupational Stress and Well-being, 7:85--123, 2009.

[3]

E. Demerouti, A. B. Bakker, F. Nachreiner, and W. B. Schaufeli. The job demands-resources model of burnout. Journal of Applied psychology, 86(3):499, 2001.

[4]

D. F. Dinges, R. L. Rider, J. Dorrian, E. L. McGlinchey, N. L. Rogers, Z. Cizman, S. K. Goldenstein, C. Vogler, S. Venkataraman, and D. N. Metaxas. Optical computer recognition of facial expressions associated with stress induced by performance demands. Aviation, space, and environmental medicine, 76(Supplement 1):B172--B182, 2005.

[5]

P. C. Duttweiler. The internal control index: A newly developed measure of locus of control. Educational and Psychological Measurement, 44(2):209--221, 1984.

[6]

S. G. Hart and L. E. Staveland. Development of nasa-tlx (task load index): Results of empirical and theoretical research. Advances in psychology, 52:139--183, 1988.

[7]

A. Kapoor and R. W. Picard. Multimodal affect recognition in learning environments. In Proceedings of the 13th annual ACM international conference on Multimedia, pages 677--682. ACM, 2005.

Digital Library

[8]

S. Koldijk. Automatic recognition of context and stress to support knowledge workers. In Proceedings of the 30th European Conference on Cognitive Ergonomics, pages D20--D23. ACM, 2012.

Digital Library

[9]

S. Koldijk, M. Sappelli, M. Neerincx, and W. Kraaij. Unobtrusive monitoring of knowledge workers for stress self-regulation. In User Modeling, Adaptation, and Personalization, pages 335--337. Springer, 2013.

[10]

S. Koldijk, M. van Staalduinen, M. Neerincx, and W. Kraaij. Real-time task recognition based on knowledge workers' computer activities. In Proceedings of the 30th European Conference on Cognitive Ergonomics, pages 152--159. ACM, 2012.

Digital Library

[11]

L. Koppes, E. d. Vroome, G. Mars, B. Janssen, M. Zwieten, and S. v. d. Bossche. Nationale enquete arbeidsomstandigheden 2012: Methodologie en globale resultaten, 2012.

[12]

K. Kraan, S. Dhondt, I. Houtman, R. Nelemans, and E. d. Vroome. Handleiding nova-weba: Hernieuwde versie., 2000.

[13]

G. Mark, D. Gudith, and U. Klocke. The cost of interrupted work: more speed and stress. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems, pages 107--110. ACM, 2008.

Digital Library

[14]

F. Mokhayeri, M.-R. Akbarzadeh-T, and S. Toosizadeh. Mental stress detection using physiological signals based on soft computing techniques. In Biomedical Engineering (ICBME), 2011 18th Iranian Conference of, pages 232--237. IEEE, 2011.

[15]

A. Riera, A. Soria-Frisch, A. Albajes-Eizagirre, P. Cipresso, C. Grau, S. Dunne, G. Ruffini, and S. aStarlab Barcelona. Electro-physiological data fusion for stress detection. Studies in health technology and informatics, 181:228--32, 2012.

[16]

J. Ruff. Information overload: Causes, symptoms and solutions. Harvard Graduate School of Education, pages 1--13, 2002.

[17]

M. Sappelli, S. Verberne, S. Koldijk, and W. Kraaij. Collecting a dataset of information behaviour in context. In Proceedings of the 4th Workshop on Context-awareness in Retrieval and Recommendation, 2014.

Digital Library

[18]

M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic. A multimodal database for affect recognition and implicit tagging. Affective Computing, IEEE Transactions on, 3(1):42--55, 2012.

Digital Library

[19]

L. M. Vizer, L. Zhou, and A. Sears. Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10):870--886, 2009.

Digital Library

[20]

D. Zapf. Stress-oriented analysis of computerized office work. The European Work and Organizational Psychologist, 3(2):85--100, 1993.

[21]

F. Zijlstra and L. van Doorn. The construction of a scale to measure subjective effort. Technical report, Delft University of Technology, 1985.

Cited By

Anwar AKhalifa YCoyle JSejdic E(2025)Transformers in biosignal analysis: A reviewInformation Fusion10.1016/j.inffus.2024.102697114(102697)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102697
Bolpagni MPardini SDianti MGabrielli S(2024)Personalized Stress Detection Using Biosignals from Wearables: A Scoping ReviewSensors10.3390/s2410322124:10(3221)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103221
Bahameish MStockman TRequena Carrión J(2024)Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability FeaturesSensors10.3390/s2410321024:10(3210)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103210
Show More Cited By

Index Terms

The SWELL Knowledge Work Dataset for Stress and User Modeling Research

Recommendations

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"
CVPRW '13: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Computer classification of facial expressions requires large amounts of data and this data needs to reflect the diversity of conditions seen in real applications. Public datasets help accelerate the progress of research by providing researchers with a ...
Self-efficacy & stress in senior computer interaction
ECCE '10: Proceedings of the 28th Annual European Conference on Cognitive Ergonomics

Motivation -- To gather an understanding about the relation between system complexity, self-efficacy and stress in senior computer interaction.

Research approach -- An empirical study was employed in which 30 seniors performed a number of tasks on a low ...
An integrated psychophysiological research on the intervention of red colour for the stress-induced bodily reaction

An integrated psychophysiological study was made for investigating the impact of 'red colour' on bodily reaction induced by stress. With regard to the psychophysiological measures, we assayed salivary-secreted substances such as hormones and immune ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMI '14: Proceedings of the 16th International Conference on Multimodal Interaction

November 2014

558 pages

ISBN:9781450328852

DOI:10.1145/2663204

General Chairs:
Albert Ali Salah
Boğaziçi University, Turkey
,
Jeffrey Cohn
University of Pittsburgh, USA
,
Björn Schuller
University of Passau, Germany and Imperial College London, UK
,
Program Chairs:
Oya Aran
Idiap Research Institute, Switzerland
,
Louis-Philippe Morency
University of Southern California, USA
,
Philip R. Cohen
Adapx, USA

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Dutch national program COMMIT

Conference

ICMI '14

Sponsor:

SIGCHI

ICMI '14: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 12 - 16, 2014

Istanbul, Turkey

Acceptance Rates

ICMI '14 Paper Acceptance Rate 51 of 127 submissions, 40%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

129
Total Citations
View Citations
1,193
Total Downloads

Downloads (Last 12 months)230
Downloads (Last 6 weeks)34

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Anwar AKhalifa YCoyle JSejdic E(2025)Transformers in biosignal analysis: A reviewInformation Fusion10.1016/j.inffus.2024.102697114(102697)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2024.102697
Bolpagni MPardini SDianti MGabrielli S(2024)Personalized Stress Detection Using Biosignals from Wearables: A Scoping ReviewSensors10.3390/s2410322124:10(3221)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103221
Bahameish MStockman TRequena Carrión J(2024)Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability FeaturesSensors10.3390/s2410321024:10(3210)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103210
Khomidov MLee DKim CLee J(2024)The Real-Time Image Sequences-Based Stress Assessment Vision System for Mental HealthElectronics10.3390/electronics1311218013:11(2180)Online publication date: 3-Jun-2024
https://doi.org/10.3390/electronics13112180
Li JWashington P(2024)A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning StudyJMIR AI10.2196/521713(e52171)Online publication date: 10-May-2024
https://doi.org/10.2196/52171
Vera Ortega PVázquez Martín RMandow AGarcía Cerezo A(2024)Bioseñales en entornos laborales y su aplicación en primeros intervinientesJornadas de Automática10.17979/ja-cea.2024.45.10841Online publication date: 12-Jul-2024
https://doi.org/10.17979/ja-cea.2024.45.10841
Alam SKhazaei SFaghih R(2024)Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote workPLOS ONE10.1371/journal.pone.030078619:5(e0300786)Online publication date: 15-May-2024
https://doi.org/10.1371/journal.pone.0300786
Zhang PJung GAlikhanov JAhmed ULee U(2024)A Reproducible Stress Prediction Pipeline with Mobile Sensor DataProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785788:3(1-35)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678578
Meelu LAssres G(2024)Explaining Predicted Stress Levels in employed IndividualsProceedings of the 2024 9th International Conference on Machine Learning Technologies10.1145/3674029.3674051(133-137)Online publication date: 24-May-2024
https://dl.acm.org/doi/10.1145/3674029.3674051
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
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents