000086503 001__ 86503 000086503 005__ 20200716101510.0 000086503 0247_ $$2doi$$a10.1016/j.eswa.2019.01.021 000086503 0248_ $$2sideral$$a110347 000086503 037__ $$aART-2019-110347 000086503 041__ $$aeng 000086503 100__ $$0(orcid)0000-0002-2726-6760$$aGarcía-Magariño, I. 000086503 245__ $$aA mobile application to report and detect 3D body emotional poses 000086503 260__ $$c2019 000086503 5060_ $$aAccess copy available to the general public$$fUnrestricted 000086503 5203_ $$aMost research into automatic emotion recognition is focused on facial expressions or physiological signals, while the exploitation of body postures has scarcely been explored, although they can be useful for emotion detection. This paper first explores a mechanism for self-reporting body postures with a novel easy-to-use mobile application called EmoPose. The app detects emotional states from self-reported poses, classifying them into the six basic emotions proposed by Ekman and a neutral state. The poses identified by Schindler et al. have been used as a reference and the nearest neighbor algorithm used for the classification of poses. Finally, the accuracy in detecting emotions has been assessed by means of poses reported by a sample of users. 000086503 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/Construyendo Europa desde Aragón$$9info:eu-repo/grantAgreement/ES/DGA/T25-17D$$9info:eu-repo/grantAgreement/ES/MEC/CAS16-00075$$9info:eu-repo/grantAgreement/ES/MEC/CAS17-00005$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2015-67149-C3-1-R$$9info:eu-repo/grantAgreement/ES/UZ/IT24-16$$9info:eu-repo/grantAgreement/ES/UZ/IT6-15$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2017-TEC-03$$9info:eu-repo/grantAgreement/ES/UZ/UZ2017-TEC-02 000086503 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000086503 590__ $$a5.452$$b2019 000086503 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b21 / 136 = 0.154$$c2019$$dQ1$$eT1 000086503 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b2 / 83 = 0.024$$c2019$$dQ1$$eT1 000086503 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b32 / 266 = 0.12$$c2019$$dQ1$$eT1 000086503 592__ $$a1.494$$b2019 000086503 593__ $$aArtificial Intelligence$$c2019$$dQ1 000086503 593__ $$aEngineering (miscellaneous)$$c2019$$dQ1 000086503 593__ $$aComputer Science Applications$$c2019$$dQ1 000086503 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000086503 700__ $$0(orcid)0000-0003-4424-0770$$aCerezo, E.$$uUniversidad de Zaragoza 000086503 700__ $$0(orcid)0000-0001-7550-6688$$aPlaza, I.$$uUniversidad de Zaragoza 000086503 700__ $$aChittaro, L. 000086503 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica 000086503 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000086503 773__ $$g122 (2019), 207-216$$pExpert syst. appl.$$tExpert Systems with Applications$$x0957-4174 000086503 8564_ $$s3624497$$uhttps://zaguan.unizar.es/record/86503/files/texto_completo.pdf$$yPostprint 000086503 8564_ $$s198790$$uhttps://zaguan.unizar.es/record/86503/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000086503 909CO $$ooai:zaguan.unizar.es:86503$$particulos$$pdriver 000086503 951__ $$a2020-07-16-09:18:10 000086503 980__ $$aARTICLE