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Emotional states detection approaches based on physiological signals for healthcare applications: A review

dc.creatorVallejo D.P.T.
dc.creatorEl Saddik A.
dc.date2019
dc.date.accessioned2021-02-05T14:59:06Z
dc.date.available2021-02-05T14:59:06Z
dc.identifier.isbn9783030278441; 9783030278434
dc.identifier.urihttp://hdl.handle.net/11407/6071
dc.descriptionMood disorders, anxiety, depression, and stress affect people’s quality of life and increase the vulnerability to diseases and infections. Depression, e.g., can carry undesirable consequences such as death. Hence, emotional states detection approaches using wearable technology are gaining interest in the last few years. Emerging wearable devices allow monitoring different physiological signals in order to extract useful information about people’s health status and provide feedback about their health condition. Wearable applications include e.g., patient monitoring, stress detection, fitness monitoring, wellness monitoring, and assisted living for elderly people, to name a few. This increased interests in wearable applications have allowed the development of new approaches to assist people in everyday activities and emergencies that can be incorporated into the smart city concept. Accurate emotional state detection approaches will allow an effective assistance, thus improving people’s quality of life and well-being. With these issues in mind, this chapter discusses existing emotional states’ approaches using machine and/or deep learning techniques, the most commonly used physiological signals in these approaches, existing physiological databases for emotion recognition, and highlights challenges and future research directions in this field. © Springer Nature Switzerland AG 2020.
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086979360&doi=10.1007%2f978-3-030-27844-1_4&partnerID=40&md5=1da48ba1048ed3f2c42f8636929c260b
dc.sourceConnected Health in Smart Cities
dc.subjectAffective recognitionspa
dc.subjectDeep learningspa
dc.subjectEmotional statesspa
dc.subjectEmotionsspa
dc.subjectMachine learningspa
dc.subjectPhysiological signalsspa
dc.subjectQuality of lifespa
dc.subjectSmart cityspa
dc.subjectWell-beingspa
dc.titleEmotional states detection approaches based on physiological signals for healthcare applications: A review
dc.typeBook Chaptereng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.identifier.doi10.1007/978-3-030-27844-1_4
dc.relation.citationstartpage47
dc.relation.citationendpage74
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationVallejo, D.P.T., Universidad de Medellin, Medellin, Colombia
dc.affiliationEl Saddik, A., Multimedia Communications Research Laboratory, University of Ottawa, Ottawa, ON, Canada
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