Abstract
When stress is improperly managed over time, it may lead to various medical, psychological, and behavioral problems. This chapter proposes applying artificial intelligence models in the workplace to control and manage workers’ stress through the heart rate. However, many of the devices used in the research literature to measure heart rate are too invasive, uncomfortable, or expensive. Therefore, we reviewed the state-of-the-art to find non-invasive wearables to measure the heart rate and the potential architectures to send the data to a server for stress detection in a workplace environment. This search identified a direct Wi-Fi connection as the most recommendable architecture and Samsung Galaxy Watch 3, Active 2, and Mobvoi TicWatch Pro 3 are the recommended wearables that allow this architecture. Moreover, we presented a methodology to configure, train, and evaluate different artificial intelligence models. We applied this methodology in the research dataset Smart Reasoning Systems for Well-being at Work and at Home-Knowledge Work (25 subjects) after performing a search of the literature of open biometrics dataset for stress detection. AdaBoost and Random Forest stand out with higher performances, with a macro F1 of 72.78% and 74.54% and balanced accuracy of 79.31% and 78.31%, respectively employing a leave-one-subject-out evaluation. We believe that this stress detection approach can be applied in the workplace in the future, helping to improve the overall employee well-being and their self-regulation capabilities in the future.
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Acknowledgements
This study was partially funded by the COBRA project (10032/20/0035/00), granted by the Spanish Ministry of Defense and by the SCORPION project (21661-PDC-21), granted by the Seneca Foundation of the Region of Murcia, Spain.
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Albaladejo-González, M., Ruipérez-Valiente, J.A. (2022). Supporting Stress Detection Via AI and Non-invasive Wearables in the Context of Work. In: Ifenthaler, D., Seufert, S. (eds) Artificial Intelligence Education in the Context of Work. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-14489-9_5
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