A machine learning model for emotion recognition from physiological signals
Emotions are affective states related to physiological responses. This study proposes a model for recognition of three emotions: amusement, sadness, and neutral from physiological signals with the purpose of developing a reliable methodology for emotion recognition using wearable devices. Target emo...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8721
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8721
- Palabra clave:
- Affective computing
Biosignal processing
Emotion recognition
Machine learning
Physiological signals
Decision trees
Electrophysiology
Feature extraction
Learning systems
Machine learning
Physiological models
Speech recognition
Statistical tests
Support vector machines
Time domain analysis
Affective computing
Bio-signal processing
Emotion recognition
Frequency and time domains
Machine learning models
Physiological response
Physiological signals
Random forest-recursive feature eliminations
Biomedical signal processing
Adult
Article
Clinical article
Electrodermal response
Feature selection
Female
Heart rate
Human
Human experiment
Male
Photoelectric plethysmography
Random forest
Recursive feature elimination
Sadness
Support vector machine
Videorecording
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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A machine learning model for emotion recognition from physiological signals |
| title |
A machine learning model for emotion recognition from physiological signals |
| spellingShingle |
A machine learning model for emotion recognition from physiological signals Affective computing Biosignal processing Emotion recognition Machine learning Physiological signals Decision trees Electrophysiology Feature extraction Learning systems Machine learning Physiological models Speech recognition Statistical tests Support vector machines Time domain analysis Affective computing Bio-signal processing Emotion recognition Frequency and time domains Machine learning models Physiological response Physiological signals Random forest-recursive feature eliminations Biomedical signal processing Adult Article Clinical article Electrodermal response Feature selection Female Heart rate Human Human experiment Male Photoelectric plethysmography Random forest Recursive feature elimination Sadness Support vector machine Videorecording |
| title_short |
A machine learning model for emotion recognition from physiological signals |
| title_full |
A machine learning model for emotion recognition from physiological signals |
| title_fullStr |
A machine learning model for emotion recognition from physiological signals |
| title_full_unstemmed |
A machine learning model for emotion recognition from physiological signals |
| title_sort |
A machine learning model for emotion recognition from physiological signals |
| dc.subject.keywords.none.fl_str_mv |
Affective computing Biosignal processing Emotion recognition Machine learning Physiological signals Decision trees Electrophysiology Feature extraction Learning systems Machine learning Physiological models Speech recognition Statistical tests Support vector machines Time domain analysis Affective computing Bio-signal processing Emotion recognition Frequency and time domains Machine learning models Physiological response Physiological signals Random forest-recursive feature eliminations Biomedical signal processing Adult Article Clinical article Electrodermal response Feature selection Female Heart rate Human Human experiment Male Photoelectric plethysmography Random forest Recursive feature elimination Sadness Support vector machine Videorecording |
| topic |
Affective computing Biosignal processing Emotion recognition Machine learning Physiological signals Decision trees Electrophysiology Feature extraction Learning systems Machine learning Physiological models Speech recognition Statistical tests Support vector machines Time domain analysis Affective computing Bio-signal processing Emotion recognition Frequency and time domains Machine learning models Physiological response Physiological signals Random forest-recursive feature eliminations Biomedical signal processing Adult Article Clinical article Electrodermal response Feature selection Female Heart rate Human Human experiment Male Photoelectric plethysmography Random forest Recursive feature elimination Sadness Support vector machine Videorecording |
| description |
Emotions are affective states related to physiological responses. This study proposes a model for recognition of three emotions: amusement, sadness, and neutral from physiological signals with the purpose of developing a reliable methodology for emotion recognition using wearable devices. Target emotions were elicited in 37 volunteers using video clips while two biosignals were recorded: photoplethysmography, which provides information about heart rate, and galvanic skin response. These signals were analyzed in frequency and time domains to obtain a set of features. Several feature selection techniques and classifiers were evaluated. The best model was obtained with random forest recursive feature elimination, for feature selection, and a support vector machine for classification. The results show that it is possible to detect amusement, sadness, and neutral emotions using only galvanic skin response features. The system was able to recognize the three target emotions with accuracy up to 100% when evaluated on the test data set. © 2019 Elsevier Ltd |
| publishDate |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2019-11-06T19:05:08Z |
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2019-11-06T19:05:08Z |
| dc.date.issued.none.fl_str_mv |
2020 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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Artículo |
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publishedVersion |
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Biomedical Signal Processing and Control; Vol. 55 |
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1746-8094 |
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https://hdl.handle.net/20.500.12585/8721 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.bspc.2019.101646 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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Biomedical Signal Processing and Control; Vol. 55 1746-8094 10.1016/j.bspc.2019.101646 Universidad Tecnológica de Bolívar Repositorio UTB |
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https://hdl.handle.net/20.500.12585/8721 |
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eng |
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eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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Recurso electrónico |
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application/pdf |
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Elsevier Ltd |
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Elsevier Ltd |
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2019-11-06T19:05:08Z2019-11-06T19:05:08Z2020Biomedical Signal Processing and Control; Vol. 551746-8094https://hdl.handle.net/20.500.12585/872110.1016/j.bspc.2019.101646Universidad Tecnológica de BolívarRepositorio UTBEmotions are affective states related to physiological responses. This study proposes a model for recognition of three emotions: amusement, sadness, and neutral from physiological signals with the purpose of developing a reliable methodology for emotion recognition using wearable devices. Target emotions were elicited in 37 volunteers using video clips while two biosignals were recorded: photoplethysmography, which provides information about heart rate, and galvanic skin response. These signals were analyzed in frequency and time domains to obtain a set of features. Several feature selection techniques and classifiers were evaluated. The best model was obtained with random forest recursive feature elimination, for feature selection, and a support vector machine for classification. The results show that it is possible to detect amusement, sadness, and neutral emotions using only galvanic skin response features. The system was able to recognize the three target emotions with accuracy up to 100% when evaluated on the test data set. © 2019 Elsevier LtdRecurso electrónicoapplication/pdfengElsevier Ltdhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85071578602&doi=10.1016%2fj.bspc.2019.101646&partnerID=40&md5=63d872ed4ecb63c81664f7c8671b3758Scopus 56682770100Scopus 57205565967Scopus 57210951365Scopus 57204201834Scopus 57210822856A machine learning model for emotion recognition from physiological signalsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Affective computingBiosignal processingEmotion recognitionMachine learningPhysiological signalsDecision treesElectrophysiologyFeature extractionLearning systemsMachine learningPhysiological modelsSpeech recognitionStatistical testsSupport vector machinesTime domain analysisAffective computingBio-signal processingEmotion recognitionFrequency and time domainsMachine learning modelsPhysiological responsePhysiological signalsRandom forest-recursive feature eliminationsBiomedical signal processingAdultArticleClinical articleElectrodermal responseFeature selectionFemaleHeart rateHumanHuman experimentMalePhotoelectric plethysmographyRandom forestRecursive feature eliminationSadnessSupport vector machineVideorecordingDomínguez-Jiménez, J.A.Campo Landines, KiaraMartínez-Santos, J.C.De la Hoz Domínguez, Enrique JoséContreras Ortiz, Sonia HelenaEkman, P., An argument for basic emotions (1992) Cogn. Emot., 6 (3-4), pp. 169-200Scherer, K.R., Ekman, P., Approaches to Emotion (1984), Psychology PressLang, P.J., The emotion probe: studies of motivation and attention (1995) Am. Psychol., 50 (5), p. 372Russell, J.A., A circumplex model of affect (1980) J. Pers. Soc. Psychol., 39 (6), p. 1161James, W., What is an emotion? (1884) Mind, 9 (34), pp. 188-205Tato, R., Santos, R., Kompe, R., Pardo, J.M., Emotional space improves emotion recognition (2002) Seventh International Conference on Spoken Language ProcessingCowie, R., Cornelius, R.R., Describing the emotional states that are expressed in speech (2003) Speech Commun., 40 (1-2), pp. 5-32Turk, M.A., Pentland, A.P., Face recognition using eigenfaces (1991) IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91, IEEE, pp. 586-591Atkinson, A.P., Tunstall, M.L., Dittrich, W.H., Evidence for distinct contributions of form and motion information to the recognition of emotions from body gestures (2007) Cognition, 104 (1), pp. 59-72Heraz, A., Clynes, M., Recognition of emotions conveyed by touch through force-sensitive screens: observational study of humans and machine learning techniques (2018) JMIR Mental Health, 5 (3)Sebe, N., Cohen, I., Huang, T.S., Multimodal emotion recognition (2005) Handbook of Pattern Recognition and Computer Vision, pp. 387-409. , World ScientificSoleymani, M., Pantic, M., Pun, T., Multimodal emotion recognition in response to videos (2012) IEEE Trans. Affect. Comput., 3 (2), pp. 211-223Alaoui-Ismaïli, O., Robin, O., Rada, H., Dittmar, A., Vernet-Maury, E., Basic emotions evoked by odorants: comparison between autonomic responses and self-evaluation (1997) Physiol. Behav., 62 (4), pp. 713-720Levenson, R.W., Carstensen, L.L., Friesen, W.V., Ekman, P., Emotion, physiology, and expression in old age (1991) Psychol. Aging, 6 (1), p. 28Christie, I.C., Friedman, B.H., Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate approach (2004) Int. J. Psychophysiol., 51 (2), pp. 143-153Picard, R.W., Healey, J., Affective wearables (1997) Pers. Technol., 1 (4), pp. 231-240Scheirer, J., Fernandez, R., Picard, R.W., Expression glasses: a wearable device for facial expression recognition (1999) CHI'99 Extended Abstracts on Human Factors in Computing Systems, ACM, pp. 262-263Haag, A., Goronzy, S., Schaich, P., Williams, J., Emotion recognition using bio-sensors: first steps towards an automatic system (2004) Tutorial and Research Workshop on Affective Dialogue Systems, Springer, pp. 36-48Hui, T., Sherratt, R., Coverage of emotion recognition for common wearable biosensors (2018) Biosensors, 8 (2), p. 30Bailon, C., Damas, M., Pomares, H., Sanabria, D., Perakakis, P., Goicoechea, C., Banos, O., Intelligent monitoring of affective factors underlying sport performance by means of wearable and mobile technology (2018) Proceedings, 2 (19), p. 1202Popat, K.A., Sharma, P., Wearable computer applications a future perspective (2013) Int. J. Eng. Innov. Technol., 3 (1), pp. 213-217Jhajharia, S., Pal, S., Verma, S., Wearable computing and its application (2014) Int. J. Comput. Sci. Inform. Technol., 5 (4), pp. 5700-5704Jeong, I.C., Bychkov, D., Searson, P., Wearable devices for precision medicine and health state monitoring (2018) IEEE Trans. Biomed. Eng., p. 1Gouizi, K., Bereksi Reguig, F., Maaoui, C., Emotion recognition from physiological signals (2011) J. Med. Eng. Technol., 35 (6-7), pp. 300-307Udovičić, G., erek, J., Russo, M., Sikora, M., Wearable emotion recognition system based on GSR and PPG signals (2017) Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, ACM, pp. 53-59Liu, M., Fan, D., Zhang, X., Gong, X., Human emotion recognition based on galvanic skin response signal feature selection and SVM (2016) 2016 International Conference on Smart City and Systems Engineering (ICSCSE), pp. 157-160. , IEEE Hunan, ChinaAyata, D., Yaslan, Y., Kamasak, M.E., Emotion based music recommendation system using wearable physiological sensors (2018) IEEE Trans. Consumer Electron., 64 (2), pp. 196-203Balasubramanian, G., Kanagasabai, A., Mohan, J., Seshadri, N.G., Music induced emotion using wavelet packet decomposition – An EEG study (2018) Biomed. Signal Process. Control, 42, pp. 115-128Domínguez-Jiménez, J., Campo-Landines, K., Martínez-Santos, J., Contreras-Ortiz, S., Emotion detection through biomedical signals: a pilot study (2018) 14th International Symposium on Medical Information Processing and Analysis, vol. 10975, International Society for Optics and Photonics, p. 1097506Schaefer, A., Nils, F., Sanchez, X., Philippot, P., Assessing the effectiveness of a large database of emotion-eliciting films: a new tool for emotion researchers (2010) Cogn. Emot., 24 (7), pp. 1153-1172Collet, C., Vernet-Maury, E., Delhomme, G., Dittmar, A., Autonomic nervous system response patterns specificity to basic emotions (1997) J. Auton. Nerv. Syst., 62 (1-2), pp. 45-57McCleary, R.A., The nature of the galvanic skin response (1950) Psychol. Bull., 47 (2), p. 97Mundy-Castle, A., McKiever, B., The psychophysiological significance of the galvanic skin response (1953) J. Exp. Psychol., 46 (1), p. 15Lang, P.J., Bradley, M.M., Cuthbert, B.N., A motivational analysis of emotion: reflex–cortex connections (1992) Psychol. Sci., 3 (1), pp. 44-49Bradley, M.M., Lang, P.J., Affective reactions to acoustic stimuli (2000) Psychophysiology, 37 (2), pp. 204-215Appelhans, B.M., Luecken, L.J., Heart rate variability as an index of regulated emotional responding (2006) Rev. Gen. Psychol., 10 (3), p. 229Ayata, D.D., Yaslan, Y., Kamaşak, M., Emotion recognition via galvanic skin response: Comparison of machine learning algorithms and feature extraction methods (2017) Istanbul Univ.-J. Electr. Electron. Eng., 17 (1), pp. 3147-3156Chen, H.-K., Hu, Y.-F., Lin, S.-F., Methodological considerations in calculating heart rate variability based on wearable device heart rate samples (2018) Comput. Biol. Med., 102, pp. 396-401Shaffer, F., Ginsberg, J., An overview of heart rate variability metrics and norms (2017) Front. Public Health, 5, p. 258von Rosenberg, W., Chanwimalueang, T., Adjei, T., Jaffer, U., Goverdovsky, V., Mandic, D.P., Resolving ambiguities in the lf/hf ratio: lf-hf scatter plots for the categorization of mental and physical stress from hrv (2017) Front. Physiol., 8, p. 360Tsai, C.-F., Feature selection in bankruptcy prediction (2009) Knowl.-Based Syst., 22 (2), pp. 120-127Niu, X., Chen, L., Chen, Q., Research on genetic algorithm based on emotion recognition using physiological signals (2011) 2011 International Conference on Computational Problem-Solving (ICCP), IEEE, pp. 614-618Zvarevashe, K., Olugbara, O.O., Gender voice recognition using random forest recursive feature elimination with gradient boosting machines (2018) 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), IEEE, pp. 1-6Gen, M., Cheng, R., (2000) Genetic Algorithms and Engineering Optimization, 7. , John Wiley & SonsGoldberg, D.E., Holland, J.H., Genetic algorithms and machine learning (1988) Mach. Learn., 3 (2), pp. 95-99Miguel, F.K., Psicologia das emoç oes: uma proposta integrativa para compreender a express ao emocional (2015) Psico-usf, 20 (1), pp. 153-162Reeve, J., Understanding Motivation and Emotion (1993), p. 13Ekman, P., Friesen, W.V., The repertoire of nonverbal behavior: Categories, origins, usage, and coding (1969) Semiotica, 1 (1), pp. 49-98Akosa, J., Predictive accuracy: a misleading performance measure for highly imbalanced data (2017) Proceedings of the SAS Global ForumSun, Y., Wong, A.K., Kamel, M.S., Classification of imbalanced data: a review (2009) Int. J. Pattern Recogn. Artif. 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