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Detecting work stress in offices by combining unobtrusive sensors

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Author: Koldijk, S. · Neerincx, M.A. · Kraaij, W.
Type:article
Date:2016
Source:IEEE Transactions on Affective Computing, Electronic preprint, 1-27
Identifier: 745680
doi: doi:10.1109/TAFFC.2016.2610975
Article number: 7572141
Keywords: Computer logging · Facial expressions · Individual differences · Machine learning · Mental state inference · Physiology · Posture · Human & Operational Modelling ICT · PCS - Perceptual and Cognitive Systems DSC - Data Science · ELSS - Earth, Life and Social Sciences TS - Technical Sciences

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 classiers 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 dierences. A comparison of several classication approaches showed that, for our SWELL-KW dataset, neutral and stressful working conditions can be distinguished with 90% accuracy by means of SVM. Posture yields most valuable information, followed by facial expressions. Furthermore, we found that the subjective variable `mental eort' can be better predicted from sensor data than e.g. `perceived stress'. A comparison of several regression approaches showed that mental eort can be predicted best by a decision tree (correlation of 0.82). Facial expressions yield most valuable information, followed by posture. We nd that especially for estimating mental states it makes sense to address individual dierences. 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.