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Towards Job Stress Recognition Based on Behavior and Physiological Features

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Ubiquitous Computing and Ambient Intelligence (UCAmI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10586))

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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Acknowledgments

This research work has been partially funded by European Commission and CONACYT, through the SmartSDK project.

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Authors and Affiliations

  1. National Technology of Mexico/CENIDET, Cuernavaca, Mexico

    Wendy Sanchez & Alicia Martinez

  2. Monterrey Institute of Technology and Higher Education, Monterrey, Mexico

    Miguel Gonzalez

Authors
  1. Wendy Sanchez
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  2. Alicia Martinez
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  3. Miguel Gonzalez
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Corresponding author

Correspondence to Alicia Martinez .

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Editors and Affiliations

  1. University of Santiago Chile, Santiago, Chile

    Sergio F. Ochoa

  2. Villanova University, Villanova, Pennsylvania, USA

    Pritpal Singh

  3. University of Castilla La Mancha, Ciudad Real, Spain

    José Bravo

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67584-8

  • Online ISBN: 978-3-319-67585-5

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