98450 20241125101123.0 doi 10.1109/TAFFC.2021.3055294 sideral 122226 ART-2023-122226 eng Greco, A. Acute stress state classification based on electrodermal activity modeling 2023 Access copy available to the general public Unrestricted Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single- subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios. info:eu-repo/grantAgreement/ES/DGA-FSE/LMP44-18 info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMON info:eu-repo/grantAgreement/ES/MICIU/RTI2018-097723-B-I00 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 9.6 2023 COMPUTER SCIENCE, CYBERNETICS 1 / 32 = 0.031 2023 Q1 T1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 14 / 197 = 0.071 2023 Q1 T1 2.645 2023 Software 2023 Q1 Human-Computer Interaction 2023 Q1 15.0 2023 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Valenza, G. Lázaro, J. Universidad de Zaragoza (orcid)0000-0001-8742-0072 Garzón-Rey, J. M. (orcid)0000-0001-6178-6535 Aguiló, J. De la Cámara, C. Bailón, Raquel Universidad de Zaragoza (orcid)0000-0003-1272-0550 Scilingo, Enzo Pascuale 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 14, 1 (2023), 788-799 IEEE trans. affect. comput. IEEE transactions on affective computing 1949-3045 1874776 http://zaguan.unizar.es/record/98450/files/texto_completo.pdf Postprint 3489843 http://zaguan.unizar.es/record/98450/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:98450 articulos driver 2024-11-22-11:57:05 ARTICLE