Abstract
Nowadays, job stress is very common and it has a high cost in terms of employees’ health, absenteeism and lower performance. It is so big the impact of this psychological disease that the WHO recognizes it as one of the great epidemics of modern life. This paper presents a job stress predictive model from monitoring employees’ behavior and physiological features. The monitoring was carried out through their job computer and a wrist-worn sensor. The proposed model obtained an accuracy of 94%, a precision of 0.943, a recall and a F-Measure of 0.914. Also, the results obtained of the evaluation of the selected model are presented.
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This research work has been partially funded by European Commission and CONACYT, through the SmartSDK project.
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Sanchez, W., Martinez, A., Gonzalez, M. (2017). Towards Job Stress Recognition Based on Behavior and Physiological Features. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_33
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DOI: https://doi.org/10.1007/978-3-319-67585-5_33
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