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International Work-Conference on the Interplay Between Natural and Artificial Computation

IWINAC 2019: Understanding the Brain Function and Emotions pp 202–211Cite as

Stress Identification from Electrodermal Activity by Support Vector Machines

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11486))

Abstract

Continuous atmosphere of competitiveness, job pressure, economic status and social judgment in modern societies leads many people to a frenetic life rhythm, thus favoring the appearance of stress. Consequently, early detection of calm and negative stress is useful to prevent long-term mental illness as depression or anxiety. This paper describes the acquisition of electrodermal activity (EDA) signals from a commercial wearable, and their storage and processing. Several time-domain, frequency-domain and morphological features are extracted over the skin conductance response component of the EDA signals. Afterwards, classification is undergone by using several support vector machines (SVMs). The International Affective Pictures System has been used to evoke calmness and distress to validate the classification results. The best results obtained during training and validation for each of the SVMs report around 87.7% accuracy for Gaussian and cubic kernels.

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References

  1. Boucsein, W., et al.: Publication recommendations for electrodermal measurements. Psychophysiology 49(8), 1017–1034 (2012)

    Article  Google Scholar 

  2. Bradley, M., Lang, P.: The international affective picture system (IAPS) in the study of emotion and attention. In: Handbook of Emotion Elicitation and Assessment, pp. 29–46. Oxford University Press (2007)

    Google Scholar 

  3. Carneiro, D., Castillo, J.C., Novais, P., Fernández-Caballero, A., Neves, J., López, M.T.: Stress monitoring in conflict resolution situations. In: Novais, P., Hallenborg, K., Tapia, D., Rodríguez, J. (eds.) Ambient Intelligence - Software and Applications, pp. 137–144. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28783-1_17

    Chapter  Google Scholar 

  4. Castillo, J.C., et al.: Software architecture for smart emotion recognition and regulation of the ageing adult. Cogn. Comput. 8(2), 357–367 (2016)

    Article  Google Scholar 

  5. Castillo, J.C., Fernández-Caballero, A., Castro-González, Á., Salichs, M.A., López, M.T.: A framework for recognizing and regulating emotions in the elderly. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 320–327. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13105-4_46

    Chapter  Google Scholar 

  6. Eisenbarth, H., Chang, L.J., Wager, T.D.: Multivariate brain prediction of heart rate and skin conductance responses to social threat. J. Neurosci. 36(47), 11987–11998 (2016)

    Article  Google Scholar 

  7. empatica: E4 wristband from empatica (2019). https://www.empatica.com/en-eu/research/e4/

  8. Fernández-Caballero, A., et al.: Smart environment architecture for emotion recognition and regulation. J. Biomed. Inform. 64, 55–73 (2016)

    Article  Google Scholar 

  9. Fernández-Sotos, A., Fernández-Caballero, A., Latorre, J.M.: Elicitation of emotions through music: the influence of note value. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9107, pp. 488–497. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18914-7_51

    Chapter  Google Scholar 

  10. Gola, J., et al.: Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels. Comput. Mater. Sci. 160, 186–196 (2019)

    Article  Google Scholar 

  11. Hernandez, J., Riobo, I., Rozga, A., Abowd, G.D., Picard, R.W.: Using electrodermal activity to recognize ease of engagement in children during social interactions. In: 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 307–317. ACM (2014)

    Google Scholar 

  12. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683

    Chapter  Google Scholar 

  13. Lang, P.J.: Behavioral treatment and bio-behavioral assessment: Computer applications. In: Technology in Mental Health Care Delivery Systems, pp. 119–137. Ablex (1980)

    Google Scholar 

  14. Lang, P., Bradley, M., Cuthbert, B.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Center for the Study of Emotion & Attention, NIMH (2005)

    Google Scholar 

  15. Martínez-Rodrigo, A., Fernández-Caballero, A., Silva, F., Novais, P.: Monitoring electrodermal activity for stress recognition using a wearable. In: Intelligent Environments, pp. 416–425. IOS Press (2016). https://doi.org/10.3233/978-1-61499-690-3-416

  16. Martínez-Rodrigo, A., Zangróniz, R., Pastor, J.M., Fernández-Caballero, A.: Arousal level classification in the ageing adult by measuring electrodermal skin conductivity. In: Bravo, J., Hervás, R., Villarreal, V. (eds.) AmIHEALTH 2015. LNCS, vol. 9456, pp. 213–223. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26508-7_21

    Chapter  Google Scholar 

  17. Martínez-Rodrigo, A., Zangróniz, R., Pastor, J.M., Latorre, J.M., Fernández-Caballero, A.: Emotion detection in ageing adults from physiological sensors. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia González, G., Fernández-Caballero, A. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 376, pp. 253–261. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19695-4_26

    Chapter  Google Scholar 

  18. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)

    Article  Google Scholar 

  19. Salai, M., Vassányi, I., Kósa, I.: Stress detection using low cost heart rate sensors. J. Healthc. Eng. (2016). https://doi.org/10.1155/2016/5136705. Article no. 5136705

  20. Sánchez-Reolid, R., et al.: Artificial neural networks to assess emotional states from brain-computer interface. Electronics 7(12), 384 (2018)

    Article  Google Scholar 

  21. Schneider, W., Eschman, A., Zuccolotto, A.: E-Prime: User’s Guide. Psychology Software Incorporated (2002)

    Google Scholar 

  22. Sokolova, M.V., Fernández-Caballero, A.: A review on the role of color and light in affective computing. Appl. Sci. 5(3), 275–293 (2015)

    Article  Google Scholar 

  23. Zangróniz, R., Martínez-Rodrigo, A., Pastor, J., López, M., Fernández-Caballero, A.: Electrodermal activity sensor for classification of calm/distress condition. Sensors 17(10), 2324 (2017)

    Article  Google Scholar 

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Acknowledgments

This work has been partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant, and by CIBERSAM of the Instituto de Salud Carlos III. Roberto Sánchez-Reolid holds BES-2017-081958 scholarship from Spanish Ministerio de Educación y Formación Profesional. Arturo Martínez-Rodrigo holds 2018/11744 grant from European Regional Development Fund (FEDER, UE).

Author information

Authors and Affiliations

  1. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071, Albacete, Spain

    Roberto Sánchez-Reolid, Arturo Martínez-Rodrigo & Antonio Fernández-Caballero

  2. Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071, Albacete, Spain

    Roberto Sánchez-Reolid & Antonio Fernández-Caballero

  3. Instituto de Tecnologías Audiovisuales, Universidad de Castilla-La Mancha, 16002, Cuenca, Spain

    Arturo Martínez-Rodrigo

  4. CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain

    Antonio Fernández-Caballero

Authors
  1. Roberto Sánchez-Reolid
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  2. Arturo Martínez-Rodrigo
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  3. Antonio Fernández-Caballero
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Corresponding author

Correspondence to Antonio Fernández-Caballero .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Madrid, Spain

    Félix de la Paz López

  4. Universidad Politécnica de Cartagena, Cartagena, Spain

    Javier Toledo Moreo

  5. The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Sánchez-Reolid, R., Martínez-Rodrigo, A., Fernández-Caballero, A. (2019). Stress Identification from Electrodermal Activity by Support Vector Machines. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-19591-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19590-8

  • Online ISBN: 978-3-030-19591-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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