Title
Detecting Work Stress in Offices by Combining Unobtrusive Sensors
Author
Koldijk, S.
Neerincx, M.A.
Kraaij, W.
Publication year
2018
Abstract
Employees often report the experience of stress at work. In the SWELL project we investigate how new context aware pervasive systems can support knowledge workers to diminish stress. The focus of this paper is on developing automatic classifiers to infer working conditions and stress related mental states from a multimodal set of sensor data (computer logging, facial expressions, posture and physiology). We address two methodological and applied machine learning challenges: 1) Detecting work stress using several (physically) unobtrusive sensors, and 2) Taking into account individual differences. A comparison of several classification approaches showed that, for our SWELL-KW dataset, neutral and stressful working conditions can be distinguished with 90 percent accuracy by means of SVM. Posture yields most valuable information, followed by facial expressions. Furthermore, we found that the subjective variable 'mental effort' can be better predicted from sensor data than, e.g., 'perceived stress'. A comparison of several regression approaches showed that mental effort can be predicted best by a decision tree (correlation of 0.82). Facial expressions yield most valuable information, followed by posture. We find that especially for estimating mental states it makes sense to address individual differences. When we train models on particular subgroups of similar users, (in almost all cases) a specialized model performs equally well or better than a generic model. © 2010-2012 IEEE.
Subject
Computer logging
Facial expressions
Individual differences
Machine learning
Physiology
Stress
Artificial intelligence
Classification (of information)
Decision trees
Learning systems
Applied machine learning
Automatic classifiers
Classification approach
Context-aware pervasive systems
Facial Expressions
Mental state
Posture
Occupational risks
To reference this document use:
http://resolver.tudelft.nl/uuid:49dd3954-f4fb-435b-a272-8d30b5f50f5c
TNO identifier
810160
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISSN
1949-3045
Source
IEEE Transactions on Affective Computing, 9 (2), 227-239
Document type
article