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dc.contributor.authorMartínez Rodríguez, Raquel ORCID
dc.contributor.authorSalazar Ramírez, Asier
dc.contributor.authorArruti Illarramendi, Andoni ORCID
dc.contributor.authorIrigoyen Gordo, Eloy
dc.contributor.authorMartín Aramburu, José Ignacio ORCID
dc.contributor.authorMuguerza Rivero, Javier Francisco
dc.date.accessioned2024-02-08T11:23:41Z
dc.date.available2024-02-08T11:23:41Z
dc.date.issued2019-04-02
dc.identifier.citationIEEE Access 7 : 43730-43741 (2019)
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10810/65571
dc.description.abstractRelaxation helps to reduce physical, mental, and emotional pressure. Relaxation techniques generally enable a person to obtain calmness and well-being by reducing stress, anxiety, or anger. When a person becomes calm the body reacts physiologically, producing the so-called Relaxation Response (RResp) which affects the organism in a positive manner, no matter if it is during a state of relaxation or in the middle of a stressful period. The goal of this paper is to design a system capable of identifying automatically the RResps of a subject by analyzing a single physiological signal, the galvanic skin response (GSR). To do so, a team composed of psychologists, neurologists, and engineers designed two experiments for inducing RResps in the participants while their GSR signals were being collected. The team analyzed the data and identified three different levels of RResp that can be quantified using only two easily calculated GSR features. Moreover, the use of the surface produced by GSR and its linear approximation is totally novel. Finally, the data were classified using decision tree strategies for each of the experiments and, after seeing that the obtained trees were similar, the team synthesized them in a single classification system. The F1 values obtained by the generalized classifier scored between 0.966 and 1.000 for the data collected in both experiments.es_ES
dc.description.sponsorshipThis work was supported in part by the Department of Education, Universities and Research of the Basque Government (ADIAN Research Group) under Grant IT980-16, in part by the Ministry of Economy and Competitiveness of the Spanish Government, and in part by the European Regional Development Fund—ERDF (PhysComp Project), under Grant TIN2017-85409-P.
dc.language.isoenges_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/TIN2017-85409-P
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectaffective computinges_ES
dc.subjectdecision treeses_ES
dc.subjectelectrodermal activityes_ES
dc.subjectgalvanic skin responsees_ES
dc.subjectmachine learninges_ES
dc.subjectrelaxation responsees_ES
dc.titleA Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysises_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder(c) 2019 IEEE*
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8680625
dc.identifier.doi10.1109/ACCESS.2019.2908445
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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