Detecting Work Stress in Offices by Combining Unobtrusive Sensors

Detecting Work Stress in Offices by Combining Unobtrusive Sensors

Neerincx, M.A. (TU Delft Interactive Intelligence)
Koldijk, Saskia (Radboud Universiteit Nijmegen)
Kraaij, Wessel (Radboud Universiteit Nijmegen; TNO)

2018

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.

computer logging
facial expressions
individual differences
Machine learning
mental state inference
physiology
posture
stress

http://resolver.tudelft.nl/uuid:c1ceb001-56d5-4a98-b6e8-69d136fbc2d5

https://doi.org/10.1109/TAFFC.2016.2610975

1949-3045

IEEE Transactions on Affective Computing, 9 (2), 227-239

Accepted Author Manuscript

Institutional Repository

journal article

© 2018 M.A. Neerincx, Saskia Koldijk, Wessel Kraaij