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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Recent studies obtained promising results for stress recognition via model personalisation and for mood prediction via deep learning, using a combination of behavioural and physiological markers in non-clinical populations.
“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]
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Vila, G., Godin, C., Charbonnier, S., Labyt, É., Sakri, O., & Campagne, A. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00402 · Cited by 12 · see citing works
Finnerty, A. N., Lepri, B., & Pianesi, F. (2016) · Human-computer interaction series · doi:10.1007/978-3-319-31413-6_5 · Cited by 12 · see citing works
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Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
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A transparency statement improves trust in community-police interactions
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Enabling self-reflection with LifelogExplorer: generating simple views from complex data
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- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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