Abstract
Stress has a big impact in the current society, being the cause or the incentive of several diseases. Therefore, its detection and monitorization has been the focus of a big number of investigations in the last decades. This work proposes the use of physiological variables such as the electrocardiogram (ECG), the galvanic skin response (GSR) and the respiration (RSP) in order to estimate the level and classify the type of stress. On that purpose, an algorithm based on fuzzy logic has been implemented. This computer-intelligent technique has been combined with a structured processing shaped in state machine. This processing classifies stress in 3 different phases or states: alarm, continued stress and relax. An improved estimation of stress level is obtained at the end, considering the last progresses made by different authors. All this is accompanied by stress classification, which is the novelty compared to other works.
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Acknowledgements
This work has been performed partially thanks to the support of the Foundation Jesús de Gangoiti Barrera, to which we are deeply grateful. It would not have been possible to perform it without the involvement of the biomedical investigation group of GICI, to which we also thank its effort and dedication.
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Zalabarria, U., Irigoyen, E., Martínez, R., Salazar-Ramirez, A. (2017). Detection of Stress Level and Phases by Advanced Physiological Signal Processing Based on Fuzzy Logic. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_29
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DOI: https://doi.org/10.1007/978-3-319-47364-2_29
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