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...
- Autores:
- 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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- License
- http://purl.org/coar/access_right/c_16ec
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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 |
dc.relation.isversionof.none.fl_str_mv |
https://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.relation.citationstartpage.none.fl_str_mv |
47 |
dc.relation.citationendpage.none.fl_str_mv |
74 |
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 Tobón, D., Falk, T., Maier, M., Context awareness in WBANs: A survey on medical and nonmedical applications (2013) IEEE Wirel. Commun., 20 (4), pp. 30-37 Bellavista, 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 Global Arnrich, 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. 207-214 Bennett, 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. 28-35 Greene, 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. 44-56 Santos, 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. 3865-3874 Rebolledo-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. 1850-1854 Zeng, 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. 39-58 Hariharan, A., Adam, M.T.P., Blended emotion detection for decision support (2015) IEEE Trans. Hum. Mac. 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Ethol., 163-202 Ekman, P., Friesen, W.V., Ellsworth, P., (2013) Emotion in the Human Face: Guidelines for Research and an Integration of Findings, , Elsevier Go, H.-J., Kwak, K.-C., Lee, D.-J., Chun, M.-G., Emotion recognition from the facial image and speech signal (2003) SICE 2003 Annual Conference, , in Sander, D., Grandjean, D., Scherer, K.R., A systems approach to appraisal mechanisms in emotion (2005) Neural Netw., 18 (4), pp. 317-352 Kim, J., André, E., Emotion recognition based on physiological changes in music listening (2008) IEEE Trans. Pattern Anal. Mach. Intell., 30 (12), pp. 2067-2083 Schlosberg, H., Three dimensions of emotion (1954) Psychol. Rev., 61 (2), pp. 81-88 Fredrickson, B.L., Levenson, R.W., Positive emotions speed recovery from the cardiovascular sequelae of negative emotions (1998) Cognit. Emot., 12 (2), pp. 191-220 Greco, A., Valenza, G., Citi, L., Scilingo, E.P., Arousal and valence recognition of affective sounds based on electrodermal activity (2017) IEEE Sensors J., 17 (3), pp. 716-725 Valenza, G., Citi, L., Gentili, C., Lanata, A., Scilingo, E.P., Barbieri, R., Characterization of depressive states in bipolar patients using wearable textile technology and instantaneous heart rate variability assessment (2015) IEEE J. Biomed. Health Inform., 19 (1), pp. 263-274 Ressel, J., A circumplex model of affect (1980) J. Pers. Soc. Psychol., 39, pp. 1161-1178 Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z., Deep learning for health informatics (2017) IEEE J. Biomed. 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Cardiol., 87 (1), pp. 9-28 Kemp, A.H., Brunoni, A.R., Santos, I.S., Nunes, M.A., Dantas, E.M., Carvalho de Figueiredo, R., Pereira, A.C., Andreao, R.V., Effects of depression, anxiety, comorbidity, and antidepressants on resting-state heart rate and its variability: An ELSA-Brasil cohort baseline study (2014) Am. J. Psychiatr., 171 (12), pp. 1328-1334 Levenson, R.W., Ekman, P., Friesen, W.V., Voluntary facial action generates emotion-specific autonomic nervous system activity (1990) Psychophysiology, 27 (4), pp. 363-384 Kreibig, S.D., Autonomic nervous system activity in emotion: A review (2010) Biol. Psychol., 84 (3), pp. 394-421 Al Osman, H., Dong, H., El Saddik, A., Ubiquitous biofeedback serious game for stress management (2016) IEEE Access, 4, pp. 1274-1286 van der Zwaag, M.D., Janssen, J.H., Westerink, J.H., Directing physiology and mood through music: Validation of an affectivemusic player (2013) IEEE Trans. Affect. Comput., 4 (1), pp. 57-68 Khalfa, S., Isabelle, P., Jean-Pierre, B., Manon, R., Event-related skin conductance responses to musical emotions in humans (2002) Neurosci. Lett., 328 (2), pp. 145-149 Picard, R.W., Fedor, S., Ayzenberg, Y., Multiple arousal theory and daily-life electrodermal activity asymmetry (2016) Emot. Rev., 8 (1), pp. 62-75 Poh, M.-Z., Swenson, N.C., Picard, R.W., A wearable sensor for unobtrusive, long-term assessment of electrodermal activity (2010) IEEE Trans. Biomed. Eng., 57 (5), pp. 1243-1252 Fukushima, H., Kawanaka, H., Bhuiyan, M.S., Oguri, K., Estimating heart rate using wristtype photoplethysmography and acceleration sensor while running (2012) Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), , in Alian, A.A., Shelley, K.H., Photoplethysmography (2014) Best Pract. Res. Clin. 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Eng., 59 (12), pp. 3498-3510 Walter, S., Kim, J., Hrabal, D., Crawcour, S.C., Kessler, H., Traue, H.C., Transsituational individual-specific biopsychological classification of emotions (2013) IEEE Trans. Syst.Man Cybern. Syst., 43 (4), pp. 988-995 What is GSR (Galvanic Skin Response) and How Does It Work?, , https://imotions.com/blog/gsr/, 12 May 2015. [Online], Accessed 2 Aug 2017 Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Patras, I., Deap: A database for emotion analysis using physiological signals (2012) IEEE Trans. Affect. Comput., 3 (1), pp. 18-31 Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M., A multimodal database for affect recognition and implicit tagging (2012) IEEE Trans. Affect. Comput., 3 (1), pp. 42-55 Picard, R.W., Vyzas, E., Healey, J., Toward machine emotional intelligence: Analysis of affective physiological state (2001) IEEE Trans. Pattern Anal. Mach. Intell., 23 (10), pp. 1175-1191 Douglas-Cowie, E., Cowie, R., Sneddon, I., Cox, C., Lowry, O., McRorie, M., Martin, J.-C., Batliner, A., The HUMAINE database: Addressing the collection and annotation of naturalistic and induced emotional data (2007) Affective Computing and Intelligent Interaction, pp. 488-500. , in, Springer, Berlin Ringeval, F., Sonderegger, A., Sauer, J., Lalanne, D., Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions (2013) 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), , in, Shanghai, China Zhang, L., Walter, S., Ma, X., BioVid Emo DB”: A multimodal database for emotion analyses validated by subjective ratings (2016) IEEE Symposium Series on Computational Intelligence (SSCI), , in, Athens, Greece Multimedia and Human Understanding Group (MHUG), , http://mhug.disi.unitn.it/wp-content/ASCERTAIN/ascertain.html, [Online], Accessed 10 Aug 2017 Chanel, G., Kronegg, J., Grandjean, D., Pun, T., Emotion assessment: Arousal evaluation using EEG’s and peripheral physiological signals (2006) Multimedia Content Representation, Classification and Security, pp. 530-537. , in Afzal, S., Robinson, P., Natural affect data: Collection and annotation (2011) New Perspectives on Affect and Learning Technologies, pp. 55-70. , in, Springer Martínez, H.P., Yannakakis, G.N., Mining multimodal sequential patterns: A case study on affect detection (2011) Proceedings of the 13th international conference on multimodal interfaces Affective Computing, , http://affect.media.mit.edu/, MIT [Online], Accessed 5 June 2017 Hui, T., Simon, S.R., Daniel, D.S., Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies (2017) Futur. Gener. Comput. Syst., 76, pp. 358-369 Acharya, 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. 1031-1051 |
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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 |
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Springer International Publishing |
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Connected Health in Smart Cities |
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Universidad de Medellín |
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Repositorio Institucional Universidad de Medellin |
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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 |