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

Mood 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 i...

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Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/6071
Acceso en línea:
http://hdl.handle.net/11407/6071
Palabra clave:
Affective recognition
Deep learning
Emotional states
Emotions
Machine learning
Physiological signals
Quality of life
Smart city
Well-being
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http://purl.org/coar/access_right/c_16ec
id REPOUDEM2_fd18421e15cad2c95f31749b7de932d3
oai_identifier_str oai:repository.udem.edu.co:11407/6071
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv Emotional states detection approaches based on physiological signals for healthcare applications: A review
title Emotional states detection approaches based on physiological signals for healthcare applications: A review
spellingShingle Emotional states detection approaches based on physiological signals for healthcare applications: A review
Affective recognition
Deep learning
Emotional states
Emotions
Machine learning
Physiological signals
Quality of life
Smart city
Well-being
title_short Emotional states detection approaches based on physiological signals for healthcare applications: A review
title_full Emotional states detection approaches based on physiological signals for healthcare applications: A review
title_fullStr Emotional states detection approaches based on physiological signals for healthcare applications: A review
title_full_unstemmed Emotional states detection approaches based on physiological signals for healthcare applications: A review
title_sort Emotional states detection approaches based on physiological signals for healthcare applications: A review
dc.subject.spa.fl_str_mv Affective recognition
Deep learning
Emotional states
Emotions
Machine learning
Physiological signals
Quality of life
Smart city
Well-being
topic Affective recognition
Deep learning
Emotional states
Emotions
Machine learning
Physiological signals
Quality of life
Smart city
Well-being
description Mood 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.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2021-02-05T14:59:06Z
dc.date.available.none.fl_str_mv 2021-02-05T14:59:06Z
dc.date.none.fl_str_mv 2019
dc.type.eng.fl_str_mv Book Chapter
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_3248
http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.version.none.fl_str_mv info:eu-repo/semantics/bookPart
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.isbn.none.fl_str_mv 9783030278441; 9783030278434
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/6071
dc.identifier.doi.none.fl_str_mv 10.1007/978-3-030-27844-1_4
identifier_str_mv 9783030278441; 9783030278434
10.1007/978-3-030-27844-1_4
url http://hdl.handle.net/11407/6071
dc.language.iso.none.fl_str_mv eng
language eng
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dc.relation.references.none.fl_str_mv Taleb, T., Bottazzi, D., Nasser, N., A novel middleware solution to improve ubiquitous healthcare systems aided by affective information (2010) IEEE Trans. Inf. Technol. Biomed., 14 (2), pp. 335-349
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dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv Springer International Publishing
dc.publisher.program.spa.fl_str_mv Ingeniería de Telecomunicaciones
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingenierías
publisher.none.fl_str_mv Springer International Publishing
dc.source.none.fl_str_mv Connected Health in Smart Cities
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
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spelling 20192021-02-05T14:59:06Z2021-02-05T14:59:06Z9783030278441; 9783030278434http://hdl.handle.net/11407/607110.1007/978-3-030-27844-1_4Mood 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.engSpringer International PublishingIngeniería de TelecomunicacionesFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086979360&doi=10.1007%2f978-3-030-27844-1_4&partnerID=40&md5=1da48ba1048ed3f2c42f8636929c260b4774Taleb, T., Bottazzi, D., Nasser, N., A novel middleware solution to improve ubiquitous healthcare systems aided by affective information (2010) IEEE Trans. Inf. Technol. Biomed., 14 (2), pp. 335-349Tobón, D., Falk, T., Maier, M., Context awareness in WBANs: A survey on medical and nonmedical applications (2013) IEEE Wirel. Commun., 20 (4), pp. 30-37Bellavista, P., Bottazzi, D., Corradi, A., Montanari, R., Challenges, opportunities and solutions for ubiquitous eldercare (2007) Web Mobile-Based Applications for Healthcare Management, pp. 142-165. , in, IGI GlobalArnrich, B., Setz, C., La Marca, R., Tröster, G., Ehlert, U., What does your chair know about your stress level? IEEE Trans (2010) Inf. Technol. Biomed., 14 (2), pp. 207-214Bennett, T.R., Wu, J., Kehtarnavaz, N., Jafari, R., Inertial measurement unit-based wearable computers for assisted living applications: A signal processing perspective (2016) IEEE Signal Process. Mag., 33 (2), pp. 28-35Greene, S., Thapliyal, H., Caban-Holt, A., A survey of affective computing for stress detection:Evaluating technologies in stress detection for better health (2016) IEEE Consum. Electr. Mag., 5 (4), pp. 44-56Santos, O.C., Uria-Rivas, R., Rodriguez-Sanchez, M., Boticario, J.G., An open sensing and acting platform for context-aware affective support in ambient intelligent educational settings (2016) IEEE Sensors J., 16 (10), pp. 3865-3874Rebolledo-Mendez, G., Reyes, A., Paszkowicz, S., Domingo, M.C., Skrypchuk, L., Developing a body sensor network to detect emotions during driving (2014) IEEE Trans. Intell. Transp. Syst., 15 (4), pp. 1850-1854Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S., A survey of affect recognition methods: Audio, visual, and spontaneous expressions (2009) IEEE Trans. Pattern Anal. Mach. Intell., 31 (1), pp. 39-58Hariharan, A., Adam, M.T.P., Blended emotion detection for decision support (2015) IEEE Trans. Hum. Mac. Syst., 45 (4), pp. 510-517Al Osman, H., Falk, T.H., Multimodal affect recognition: Current approaches and challenges (2017) Emotion and Attention Recognition Based on Biological Signals and Images, pp. 59-86. , in, InTechHogg, M.A., Abrams, D., Social cognition and attitudes (2007) Psychology, pp. 684-721. , in, 3rd edn. ed. by G.N. Martin, N.R. Carlson, W. Buskist (Pearson Education LimitedEkman, P., About brows: Emotional and conversational signals (1979) Hum. 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Gener. Comput. Syst., 76, pp. 358-369Acharya, U.R., Joseph, K.P., Kannathal, N., Lim, C.M., Suri, J.S., Heart rate variability: A review (2006) Med. Biol. Eng. Comput., 44 (12), pp. 1031-1051Connected Health in Smart CitiesAffective recognitionDeep learningEmotional statesEmotionsMachine learningPhysiological signalsQuality of lifeSmart cityWell-beingEmotional states detection approaches based on physiological signals for healthcare applications: A reviewBook Chapterinfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_3248http://purl.org/coar/resource_type/c_2df8fbb1Vallejo, D.P.T., Universidad de Medellin, Medellin, ColombiaEl Saddik, A., Multimedia Communications Research Laboratory, University of Ottawa, Ottawa, ON, Canadahttp://purl.org/coar/access_right/c_16ecVallejo D.P.T.El Saddik A.11407/6071oai:repository.udem.edu.co:11407/60712021-02-05 09:59:07.136Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co