Position Papers of the 19th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2024F9093

Citation: Position Papers of the 19th Conference on Computer Science and Intelligence Systems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 40, pages 61–68 (2024)

Full text

Abstract. In the evolving domain of occupational health, assessment of Work-related Quality of Life (WrQoL) has gained critical importance, particularly with recent expedited developments of decentralized and digital work. Conventional methods relying on subjective questionnaires are limited by high drop-out rates and potential biases. This paper introduces a novel approach to evaluating WrQoL by leveraging data generated from digital office environments, wearable devices, and smartphone applications. Our methodology includes the collection of physiological data, analysis of digital interactions, and prosody analysis to construct a comprehensive model of WrQoL influences. Initial and weekly questionnaires as well as multiple daily self-reports of valence and arousal levels will serve to initially validate this model. Prospectively utilizing machine learning, we aim to predict WrQoL scores from aggregated data. This method presents a non-invasive alternative for assessing WrQoL, providing significant implications for both research and industry with the potential to enhance workplace conditions and employee well-being.

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