The DOI matches its Crossref record; 16 of the 17 references it cites were found in a registry or an index.
Smart technologies for long-term stress monitoring at work
Abstract
Due to the growing pace of life, stress became one of the major factors causing health problems. We have developed a framework for measuring stress in real-life conditions continuously and unobtrusively. In order to provide meaningful, useful and actionable information, we present stress information, derived from sensor measurements, in the context of person's activities. In this paper, we describe our framework, discuss how we address arising challenges and evaluate our approach on basis of the field studies we have conducted. The main results of the evaluation are that the results of long-term measurements of stress reveal people information about their behavioral patterns that they perceive as meaningful and useful, and trigger their ideas about behavioral changes necessary to achieve a better stress balance.
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“Building on recent advances of machine learning, recent studies obtained promising results by means of model personalisation for stress recognition [88] and deep learning for mood prediction [89] using a combination of behavioural and physiological markers in non-clinical populations.”
From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [88]
Citations
Cites
Eysenck, H. J. (1985) · Behaviour Research and Therapy · doi:10.1016/0005-7967(85)90087-7 · Cited by 32,038
“Stress: appraisal and coping,”
Folkman, S. (2013) · Encyclopedia of Behavioral Medicine · doi:10.1007/978-1-4419-1005-9_215 · Cited by 27,833
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538
Boucsein, W. (2012) · doi:10.1007/978-1-4614-1126-0 · Cited by 2,526
Sharma, N., & Gedeon, T. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.07.003 · Cited by 522
Third European Survey on Working Conditions 2000
Paoli, P. (2001) · Cited by 303
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data
Bakker, J., Pechenizkiy, M., & Sidorova, N. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.178 · Cited by 286
Self-citationAffectAura: an intelligent system for emotional memory
McDuff, D., Karlson, A., Kapoor, A., Roseway, A., & Czerwinski, M. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208525 · Cited by 242
Real-Time Driver's Stress Event Detection
Rigas, G., Goletsis, Y., & Fotiadis, D. I. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2168215 · Cited by 148
Sanches, P., Höök, K., Vaara, E. K., Weymann, C., Bylund, M., Ferreira, P., Peira, N., & Sjölinder, M. (2010) · Designing Interactive Systems Conference · doi:10.1145/1858171.1858182 · Cited by 130
Bakker, J., Holenderski, L., Kocielnik, R., Pechenizkiy, M., & Sidorova, N. (2012) · International Health Informatics Symposium · doi:10.1145/2110363.2110439 · Cited by 63
Self-citationEmotion measurement platform for daily life situations
Westerink, J. H. D. M., Ouwerkerk, M., de Vries, G.-J., de Waele, S., van den Eerenbeemd, J. M. A., & van Boven, M. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349574 · Cited by 48
Self-citationConsistency of individual differences in non-specific electrodermal activity
O’Gorman, J. G., & Horneman, C. J. (1979) · Biological Psychology · doi:10.1016/0301-0511(79)90019-x · Cited by 30
Towards Multimodal Driver’s Stress Detection
Bořil, H., Boyraz, P., & Hansen, J. H. L. (2011) · Springer eBooks · doi:10.1007/978-1-4419-9607-7_1 · Cited by 19
Enabling Self-Reflection with LifelogExplorer: Generating Simple Views from Complex Data
Kocielnik, R., Maggi, F., & Sidorova, N. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/icst.pervasivehealth.2013.251934 · Cited by 13
Self-citationEnabling Self-Reflection with LifelogExplorer: Generating Simple Views from Complex Data
Kocielnik, R., Maggi, F., & Sidorova, N. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/pervasivehealth.2013.251934 · Cited by 2
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Research works printed but not linked: 1
- grings, 1974, Recording of electrodermal phenomena, Methods in Physilogical Psychology (Bioelectric Recording Techniques Part C Receptor and Effector Processes, 1, 273
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Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review
Posada-Quintero, H. F., & Chon, K. H. (2020) · Sensors · doi:10.3390/s20020479 · Cited by 525
Stress detection in daily life scenarios using smart phones and wearable sensors: A survey
Can, Y. S., Bert Arnrich, A., & Ersoy, C. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103139 · Cited by 542
Alberdi, A., Aztiria, A., & Basarab, A. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.11.007 · Cited by 496
The Ethics of Digital Well-Being: A Thematic Review
Burr, C., Taddeo, M., & Floridi, L. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00175-8 · Cited by 374
Large-scale wearable data reveal digital phenotypes for daily-life stress detection
Smets, E., Rios Velazquez, E., Schiavone, G., Chakroun, I., D’Hondt, E., De Raedt, W., Cornelis, J., Janssens, O., Van Hoecke, S., Claes, S., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0074-9 · Cited by 250
Metadata
- Authors
- Kocielnik, R., Sidorova, N., Maggi, F. M., Ouwerkerk, M., & Westerink, J. H. D. M.
- Affiliations
- Eindhoven University of Technology (NL)
- University of Tartu (EE)
- Philips (Netherlands) (NL)
- Venue
- Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 53-58
- ISSN
- 1063-7125 (ISSN-L)
- OpenAlex
- W2154145109
- Cited by
- 154
- References
- 17
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 14 · 2023: 15 · 2022: 8 · 2021: 10 · 2020: 15 · 2019: 21 · 2018: 12 · 2017: 10 · 2016: 11 · 2015: 15 · 2014: 4 · 2013: 5
- FWCI
- 15.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE Symposium on Computer-Based Medical Systems (publishedVersion)
- TU/e Research Portal (publishedVersion)
- TU/e Research Portal (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- occupational stress, stress monitoring, wearable sensors, unobtrusive sensing, long-term monitoring, behavioral patterns, workplace health, stress balance
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