Apply Machine Learning for Stress Detection in Office Work Environments

Abstract

Detecting stress in workplace environments represents an innovative element for promoting employee health and well-being, particularly in office settings. The use of machine learning in this context offers an effective approach to identifying early signs of stress, allowing organizations to intervene promptly to mitigate associated risks. Of particular interest is examining whether the results obtained through unsupervised learning methods can be comparable to those derived from supervised approaches, as highlighted in the research "Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection" by Iqbal et al. (2022) in the journal Frontiers in Medical Technology.

Thesis

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Credits

Important

If you use or reference this project, please cite it as follows:

@thesis{vicario2024apply,
    author = {Vicario, Roberto},
    title  = {Apply Machine Learning for Stress Detection in Office Work Environments},
    year   = {2024},
    url    = {https://raw.githubusercontent.com/robertovicario/BSc-Computer-Science-Thesis/main/Applicare_il_Machine_Learning_per_il_Rilevamento_dello_Stress_negli_Ambienti_di_Lavoro_di_Ufficio.pdf}
}

License

This project is distributed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. You can find the complete text of the license in the project repository.