{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T04:38:33Z","timestamp":1778215113651,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,21]],"date-time":"2020-06-21T00:00:00Z","timestamp":1592697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00127\/2020"],"award-info":[{"award-number":["UIDB\/00127\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/IC\/4255\/2020"],"award-info":[{"award-number":["UID\/IC\/4255\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/04810\/2020"],"award-info":[{"award-number":["UIDB\/04810\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["SFRH\/BD\/136815\/2018"],"award-info":[{"award-number":["SFRH\/BD\/136815\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["SFRH\/BD\/118244\/2016"],"award-info":[{"award-number":["SFRH\/BD\/118244\/2016"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples\u2019 emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.<\/jats:p>","DOI":"10.3390\/s20123510","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T09:05:33Z","timestamp":1592903133000},"page":"3510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification"],"prefix":"10.3390","volume":"20","author":[{"given":"Gisela","family":"Pinto","sequence":"first","affiliation":[{"name":"Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9829-7804","authenticated-orcid":false,"given":"Jo\u00e3o M.","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Institute of Electronics and Informatics Engineering of Aveiro (IEETA), 3810-193 Aveiro, Portugal"}]},{"given":"Filipa","family":"Barros","sequence":"additional","affiliation":[{"name":"Department of Education and Psychology, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"William James Center for Research, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Center for Health Technology and Services Research, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"given":"Sandra C.","family":"Soares","sequence":"additional","affiliation":[{"name":"Department of Education and Psychology, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"William James Center for Research, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Center for Health Technology and Services Research, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9164-0016","authenticated-orcid":false,"given":"Armando J.","family":"Pinho","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Institute of Electronics and Informatics Engineering of Aveiro (IEETA), 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-9219","authenticated-orcid":false,"given":"Susana","family":"Br\u00e1s","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Institute of Electronics and Informatics Engineering of Aveiro (IEETA), 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1177\/0539018405058216","article-title":"What are emotions? And how can they be measured?","volume":"44","author":"Scherer","year":"2005","journal-title":"Soc. Sci. Inf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1126\/science.1076358","article-title":"Emotion, cognition, and behavior","volume":"298","author":"Dolan","year":"2002","journal-title":"Science"},{"key":"ref_3","unstructured":"Lewis, M., Haviland-Jones, J.M., and Barrett, L.F. (2008). The evolutionary psychology of the emotions and their relationship to internal regulatory variables. Handbook of Emotions, The Guilford Press. [3rd ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1037\/a0030259","article-title":"Affective science and health: The importance of emotion and emotion regulation","volume":"32","author":"DeSteno","year":"2013","journal-title":"Heal. Psychol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.appsy.2007.09.001","article-title":"The role of discrete emotions in health outcomes: A critical review","volume":"12","author":"Consedine","year":"2007","journal-title":"Appl. Prev. Psychol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/02699930802204677","article-title":"Measures of emotion: A review","volume":"23","author":"Mauss","year":"2009","journal-title":"Cogn. Emot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2010.1","article-title":"Affect detection: An interdisciplinary review of models, methods, and their applications","volume":"1","author":"Calvo","year":"2010","journal-title":"Affect. Comput. IEEE Trans."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shu, L., Yu, Y., Chen, W., Hua, H., Li, Q., Jin, J., and Xu, X. (2020). Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet. Sensors, 20.","DOI":"10.3390\/s20030718"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dzedzickis, A., Kaklauskas, A., and Bucinskas, V. (2020). Human emotion recognition: Review of sensors and methods. Sensors, 20.","DOI":"10.3390\/s20030592"},{"key":"ref_10","unstructured":"Romeo, L., Cavallo, A., Pepa, L., Berthouze, N., and Pontil, M. (2019). Multiple instance learning for emotion recognition using physiological signals. IEEE Trans. Affect. Comput."},{"key":"ref_11","unstructured":"Cacioppo, J.T., Tassinary, L.G., and Berntson, G. (2007). Handbook of Psychophysiology, Cambridge University Press."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1111\/psyp.12808","article-title":"An automatic classifier of emotions built from entropy of noise","volume":"54","author":"Ferreira","year":"2017","journal-title":"Psychophysiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"467","DOI":"10.3389\/fpsyg.2018.00467","article-title":"Biometric and Emotion Identification: An ECG Compression Based Method","volume":"9","author":"Ferreira","year":"2018","journal-title":"Front. Psychol."},{"key":"ref_14","unstructured":"Cai, J., Liu, G., and Hao, M. (2009, January 25\u201326). The research on emotion recognition from ECG signal. Proceedings of the 2009 International Conference on Information Technology and Computer Science, Kiev, Ukraine."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bird, J.J., Manso, L.J., Ribeiro, E.P., Ekart, A., and Faria, D.R. (2018, January 25\u201327). A Study on Mental State Classification using EEG-based Brain-Machine Interface. Proceedings of the 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS, Madeira, Portugal.","DOI":"10.1109\/IS.2018.8710576"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/978-3-540-73078-1_36","article-title":"A user independent, biosignal based, emotion recognition method","volume":"4511","author":"Rigas","year":"2007","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mokhayeri, F., Akbarzadeh-T, M.R., and Toosizadeh, S. (2011, January 14\u201316). Mental stress detection using physiological signals based on soft computing techniques. Proceedings of the 2011 18th Iran. Conf. Biomed. Eng. ICBME 2011, Tehran, Iran.","DOI":"10.1109\/ICBME.2011.6168563"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gouizi, K., Reguig, F.B., and Maaoui, C. (2011, January 9\u201311). Analysis physiological signals for emotion recognition. Proceedings of the 7th International Workshop on Systems, Signal Processing and their Applications, Tipaza, Algeria.","DOI":"10.1109\/WOSSPA.2011.5931436"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: A systematic analysis for the Global Burden of Disease Study 2017","volume":"392","author":"James","year":"2018","journal-title":"Lancet"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C.S., and Ho, R.C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 Coronavirus Disease (COVID-19) epidemic among the general population in China. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17051729"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1177\/0020764020915212","article-title":"The outbreak of COVID-19 coronavirus and its impact on global mental health","volume":"66","author":"Torales","year":"2020","journal-title":"Int. J. Soc. Psychiatry"},{"key":"ref_22","unstructured":"Berntson, Q.K.S., and Lozano, D. (2007). Cardiovascular Psychophysiology, Cambridge University Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.patrec.2018.05.026","article-title":"Extended-alphabet finite-context models","volume":"112","author":"Carvalho","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Torrado, J.C., Gomez, J., and Montoro, G. (2017). Emotional self-regulation of individuals with autism spectrum disorders: Smartwatches for monitoring and interaction. Sensors, 17.","DOI":"10.3390\/s17061359"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3510\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:41:26Z","timestamp":1760175686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,21]]},"references-count":24,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123510"],"URL":"https:\/\/doi.org\/10.3390\/s20123510","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,21]]}}}