000125256 001__ 125256
000125256 005__ 20240731103331.0
000125256 0247_ $$2doi$$a10.3390/s23031608
000125256 0248_ $$2sideral$$a132986
000125256 037__ $$aART-2023-132986
000125256 041__ $$aeng
000125256 100__ $$0(orcid)0000-0002-9315-6391$$aBaldassarri, Sandra$$uUniversidad de Zaragoza
000125256 245__ $$aWearables and machine learning for improving runners’ motivation from an affective perspective
000125256 260__ $$c2023
000125256 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125256 5203_ $$aWearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners’ emotions during their training. The solution is based on the analysis of runners’ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners’ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training.
000125256 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/PDC2021-121072-C22$$9info:eu-repo/grantAgreement/ES/RTI2018-096986-B-C31$$9info:eu-repo/grantAgreement/EUR/MINECO/TED2021-130374B-C22
000125256 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125256 590__ $$a3.4$$b2023
000125256 592__ $$a0.786$$b2023
000125256 591__ $$aCHEMISTRY, ANALYTICAL$$b34 / 106 = 0.321$$c2023$$dQ2$$eT1
000125256 593__ $$aInstrumentation$$c2023$$dQ1
000125256 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b24 / 76 = 0.316$$c2023$$dQ2$$eT1
000125256 593__ $$aAnalytical Chemistry$$c2023$$dQ1
000125256 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b122 / 352 = 0.347$$c2023$$dQ2$$eT2
000125256 593__ $$aAtomic and Molecular Physics, and Optics$$c2023$$dQ1
000125256 593__ $$aInformation Systems$$c2023$$dQ2
000125256 593__ $$aMedicine (miscellaneous)$$c2023$$dQ2
000125256 593__ $$aBiochemistry$$c2023$$dQ2
000125256 593__ $$aElectrical and Electronic Engineering$$c2023$$dQ2
000125256 594__ $$a7.3$$b2023
000125256 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125256 700__ $$aGarcía de Quirós, Jorge$$uUniversidad de Zaragoza
000125256 700__ $$0(orcid)0000-0002-7500-4650$$aBeltrán, José Ramón$$uUniversidad de Zaragoza
000125256 700__ $$0(orcid)0000-0002-6584-7259$$aÁlvarez, Pedro$$uUniversidad de Zaragoza
000125256 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000125256 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000125256 773__ $$g23, 3 (2023), 1608 [16 pp]$$pSensors$$tSensors$$x1424-8220
000125256 8564_ $$s3529895$$uhttps://zaguan.unizar.es/record/125256/files/texto_completo.pdf$$yVersión publicada
000125256 8564_ $$s2695000$$uhttps://zaguan.unizar.es/record/125256/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125256 909CO $$ooai:zaguan.unizar.es:125256$$particulos$$pdriver
000125256 951__ $$a2024-07-31-09:46:36
000125256 980__ $$aARTICLE