Emotion Detection in Aging Adults Through Continuous Monitoring of Electro-Dermal Activity and Heart-Rate Variability

  • Luz Fernández-Aguilar
  • Arturo Martínez-Rodrigo
  • José Moncho-Bogani
  • Antonio Fernández-CaballeroEmail author
  • José Miguel Latorre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)


This paper introduces a system composed of hardware, control software, signal processing and classification for the deployment of a wearable with a high ability to discriminate among seven emotional states (neutral, affection, amusement, anger, disgust, fear and sadness). The study described in this proposal focuses on comparing the emotional states of young and older people by means of two physiological parameters, namely electro-dermal activity and heart-rate variability, both captured from the wearable. The wearable emotion detection system is trained by eliciting the desired emotions on eighty young (16 to 26 years old) and fifty older adults (aged 60 to 84) through a film mood induction procedure. Seventeen features are calculated on skin conductance response and heart-rate variability data. Then, these features are classified by a support vector machines. State amusement reached a high number of hits (87.4%), whilst affection received the lowest rate of hits (82.5%). The negative emotion with lowest value is anger (82.4%) and the highest is disgust (85.9%).


Electro-dermal activity Heart-rate variability Emotion detection Aging adults 



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. Arturo Martínez-Rodrigo holds 2018/11744 grant from European Regional Development Fund (FEDER, UE).


  1. 1.
    Serrano, J.P., Latorre, J.M., Gatz, M.: Spain: promoting the welfare of older adults in the context of population aging. Gerontologist 54(5), 733–740 (2014). Scholar
  2. 2.
    Castillo, J.C., Castro-Gonzalez, A., Fernandez-Caballero, A., Latorre, J.M., Pastor, J.M., Fernandez-Santos, A., Salichs, M.A.: Software architecture for smart emotion recognition and regulation of the ageing adult. Cogn. Comput. 8(2), 357–367 (2016). Scholar
  3. 3.
    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). Scholar
  4. 4.
    Fernández-Sotos, A., Fernández-Caballero, A., Latorre, J.M.: Influence of tempo and rhythmic unit in musical emotion regulation. Front. Comput. Neurosci. 10, 80 (2016). Scholar
  5. 5.
    Fernández-Caballero, A., et al.: Smart environment architecture for emotion detection and regulation. J. Biomed. Inform. 64, 55–73 (2016). Scholar
  6. 6.
    Fernández-Caballero, A., Latorre, J.M., Pastor, J.M., Fernández-Sotos, A.: Improvement of the elderly quality of life and care through smart emotion regulation. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 348–355. Springer, Cham (2014). Scholar
  7. 7.
    Fernández-Aguilar, L., et al.: Emotional induction through films: a model for the regulation of emotions. In: Chen, Y.-W., Tanaka, S., Howlett, R.J., Jain, L.C. (eds.) Innovation in Medicine and Healthcare 2016. SIST, vol. 60, pp. 15–23. Springer, Cham (2016). Scholar
  8. 8.
    Fernández-Aguilar, L., Ricarte, J.J., Ros, L., Latorre, J.M.: Emotional differences in young and older adults: films as mood induction procedure. Front. Psychol. 9, 1110 (2018). Scholar
  9. 9.
    Malik, M., et al.: Heart rate variability standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 17, 354–381 (1996)CrossRefGoogle Scholar
  10. 10.
    Zangróniz, R., Martínez-Rodrigo, A., López, M.T., Pastor, J.M., Fernández-Caballero, A.: Estimation of mental distress from photoplethysmography. Appl. Sci. 8, 69 (2018). Scholar
  11. 11.
    Zangróniz, R., Martínez-Rodrigo, A., Pastor, J.M., López, M.T., Fernández-Caballero, A.: Electrodermal activity sensor for classification of calm/distress condition. Sensors 17, 2324 (2017). Scholar
  12. 12.
    Martínez-Rodrigo, A., Alcaraz, R., Rieta, J.J.: Application of the phasor transform for automatic delineation of single lead ECG fiducial points. Physiol. Meas. 31, 1467 (2010). Scholar
  13. 13.
    Martínez-Rodrigo, A., Fernández-Caballero, A., Silva, F., Novais, P.: Monitoring electrodermal activity for stress recognition using a wearable. In: Ambient Intelligence and Smart Environments, pp. 416–425 (2016).
  14. 14.
    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). Scholar
  15. 15.
    Martínez-Rodrigo, A., Zangróniz, R., Pastor, J.M., Sokolova, M.V.: Arousal level classification of the aging adult from electro-dermal activity: from hardware development to software architecture. Pervasive Mob. Comput. 34, 46–59 (2017). Scholar
  16. 16.
    Fernández, C.F., Mateos, J.C.P., Ribaudi, J.S., Fernández-Abascal, E.G.: Spanish validation of an emotion-eliciting set of films. Psicothema 23, 778–785 (2011)Google Scholar
  17. 17.
    Gross, J.J., Levenson, R.W.: Emotion elicitation using films. Cogn. Emot. 9, 87–108 (1995). Scholar
  18. 18.
    Schaefer, A., Nils, F., Sanchez, X., Philippot, P.: Assessing the effectiveness of a large database of emotion-eliciting films: a new tool for emotion researchers. Cogn. Emot. 24, 1153–1172 (2010). Scholar
  19. 19.
    Boucsein, W.: Electrodermal Activity. Springer, Heidelberg (2012). Scholar
  20. 20.
    Benedek, M., Kaernbach, C.: A continuous measure of phasic electrodermal activity. J. Neurosci. Methods 190, 80–91 (2010). Scholar
  21. 21.
    Malik, M.: Heart rate variability. Ann. Noninvasive Electrocardiol. 1, 151–181 (1996). Scholar
  22. 22.
    Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28, R1 (2007). Scholar
  23. 23.
    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). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Luz Fernández-Aguilar
    • 1
  • Arturo Martínez-Rodrigo
    • 2
    • 3
  • José Moncho-Bogani
    • 1
  • Antonio Fernández-Caballero
    • 2
    • 4
    Email author
  • José Miguel Latorre
    • 1
  1. 1.Departamento de PsicologíaUniversidad de Castilla-La ManchaAlbaceteSpain
  2. 2.Departamento de Sistemas InformáticosUniversidad de Castilla-La ManchaAlbaceteSpain
  3. 3.Instituto de Tecnologías AudiovisualesUniversidad de Castilla-La ManchaCuencaSpain
  4. 4.Instituto de Investigación en Informática de AlbaceteUniversidad de Castilla-La ManchaAlbaceteSpain

Personalised recommendations