Detecting Work Stress in Offices by Combining Unobtrusive Sensors

Mark A. Neerincx, Saskia Koldijk, Wessel Kraaij

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)
66 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)227-239
Number of pages13
JournalIEEE Transactions on Affective Computing
Volume9
Issue number2
DOIs
Publication statusPublished - 2018

Keywords

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

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