000098450 001__ 98450
000098450 005__ 20240731103308.0
000098450 0247_ $$2doi$$a10.1109/TAFFC.2021.3055294
000098450 0248_ $$2sideral$$a122226
000098450 037__ $$aART-2023-122226
000098450 041__ $$aeng
000098450 100__ $$aGreco, A.
000098450 245__ $$aAcute stress state classification based on electrodermal activity modeling
000098450 260__ $$c2023
000098450 5060_ $$aAccess copy available to the general public$$fUnrestricted
000098450 5203_ $$aAcute 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.
000098450 536__ $$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMON$$9info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON$$9info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group$$9info:eu-repo/grantAgreement/ES/DGA-FSE/LMP44-18$$9info:eu-repo/grantAgreement/ES/MICIU/RTI2018-097723-B-I00
000098450 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000098450 590__ $$a9.6$$b2023
000098450 591__ $$aCOMPUTER SCIENCE, CYBERNETICS$$b1 / 32 = 0.031$$c2023$$dQ1$$eT1
000098450 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b14 / 197 = 0.071$$c2023$$dQ1$$eT1
000098450 594__ $$a15.0$$b2023
000098450 592__ $$a2.645$$b2023
000098450 593__ $$aSoftware$$c2023$$dQ1
000098450 593__ $$aHuman-Computer Interaction$$c2023$$dQ1
000098450 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000098450 700__ $$aValenza, G.
000098450 700__ $$0(orcid)0000-0001-8742-0072$$aLázaro, J.$$uUniversidad de Zaragoza
000098450 700__ $$0(orcid)0000-0001-6178-6535$$aGarzón-Rey, J. M.
000098450 700__ $$aAguiló, J.
000098450 700__ $$aDe la Cámara, C.
000098450 700__ $$0(orcid)0000-0003-1272-0550$$aBailón, Raquel$$uUniversidad de Zaragoza
000098450 700__ $$aScilingo, Enzo Pascuale
000098450 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000098450 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000098450 773__ $$g14, 1 (2023), 788-799$$pIEEE trans. affect. comput.$$tIEEE transactions on affective computing$$x1949-3045
000098450 8564_ $$s1874776$$uhttps://zaguan.unizar.es/record/98450/files/texto_completo.pdf$$yPostprint
000098450 8564_ $$s3489843$$uhttps://zaguan.unizar.es/record/98450/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000098450 909CO $$ooai:zaguan.unizar.es:98450$$particulos$$pdriver
000098450 951__ $$a2024-07-31-09:38:31
000098450 980__ $$aARTICLE