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The article "A text classification approach to detect psychological stress combining a lexicon-based feature framework with distributional representations", by Sergio Muñoz andCarlos A. Iglesias has been published ...

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The article "Prediction of stress levels in the workplace using surrounding stress", by Sergio Muñoz, Carlos A. Iglesias, Oscar Mayora and Venet Osmani has been published in the Information Processing & Management journal (7.466 impact factor, JCR Q1 2021). This work is a product of the COGNOS Project.

The full paper can be found at this URL.

 

Abstract:

Occupational stress has a significant adverse effect on workers’ well-being, productivity, and performance and is becoming a major concern for both individual companies and the overall economy. To reduce negative consequences, early detection of stress is a key factor. In response several stress prediction methods have been proposed, whose primary aim is to analyse physiological and behavioural data. However, evidence suggests that solutions based on physiological and behavioural data alone might be challenging when implemented in real-world settings. These solutions are sensitive to data problems arising from losses in signal quality or alterations in body responses, which are common in everyday activities. The contagious nature of stress and its sensitivity to the surroundings can be used to improve these methods. In this study, we sought to investigate automatic stress prediction using both surrounding stress data, which we define as close colleagues’ stress levels and the stress level history of the individuals. We introduce a real-life, unconstrained study conducted with 30 workers monitored over 8 weeks. Furthermore, we propose a method to investigate the effect of stress levels of close colleagues on the prediction of an individual’s stress levels. Our method is also validated on an external, independent dataset. Our results show that surrounding stress can be used to improve stress prediction in the workplace, where we achieve 80% of F-score in predicting individuals’ stress levels from the surrounding stress data in a multiclass stress classification.