Emotion Detection in Ageing Adults from Physiological Sensors

  • Arturo Martínez-Rodrigo
  • Roberto Zangróniz
  • José Manuel Pastor
  • José Miguel Latorre
  • Antonio Fernández-Caballero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 376)


The increasing life expectancy is causing a fast ageing population around the globe, which is raising the demand on assistive systems based on ambient intelligence. While numerous papers have focused on the physical aspects in elderly, only a few works have attempted to regulate their emotional state. In this work, a new approach for monitoring and detecting the emotional state in elderly is presented. First, different physiological signals are acquired by means of wearable sensors, and data are transmitted to the embedded system. Next, noise and artifacts are removed by applying different signal processing techniques, depending on the signal behavior. Finally, several temporal and statistical markers are extracted and used to feed the classification model. In this very first version, a logistic regression model is used to detect two possible emotional states. In order to calibrate the model and adjust the boundary decision, twenty volunteers have agreed to be monitored and recorded to train the model. Finally, a decision maker regulates the environment, acting directly upon the elderly’s emotional state.


Emotion detection Ageing adults Physiological sensors 



This work was partially supported by Spanish Ministerio de Economía y Competitividad / FEDER under TIN2013-47074-C2-1-R grant.


  1. 1.
    World Health Organization, in Ageing and Life Course (2011)Google Scholar
  2. 2.
    S. Mowafey, S. Gardner, A novel adaptive approach for home care ambient intelligent environments with an emotion-aware system, in UKACC International Conference on Control (Cardiff, UK, 2012), pp. 771–777, 3–5 Sept 2012Google Scholar
  3. 3.
    M.A. Hanson, H.C. Powell Jr., A.T. Barth, K. Ringgenberg, B.H. Calhoun, J.H. Aylor, J. Lach, Body area sensors networks: challenges and opportunities, in IEEE Computer Society, pp. 58–65, 2009Google Scholar
  4. 4.
    A. Fernández-Caballero, J.M. Latorre, J.M. Pastor, A. Fernández-Sotos, Improvement of the elderly quality of life and care through smart emotion regulation, in Ambient Assisted Living and Daily Activities, pp. 348–355, 2014Google Scholar
  5. 5.
    S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras, DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)Google Scholar
  6. 6.
    M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, V.C.M. Leung, Body area networks: a survey. Mobile Netw. Appl. 16, 171–193 (2011)CrossRefGoogle Scholar
  7. 7.
    P. Remagnino, G.L. Foresti, Ambient intelligence: a new multidisciplinary paradigm. IEEE Trans. Syst. Man Cybern. Part A 35(1), 1–6 (2005)CrossRefGoogle Scholar
  8. 8.
    J.A. Russell, A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)CrossRefGoogle Scholar
  9. 9.
    J.A. Healey, R.W. Picard, Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Trans. Syst. 6(2), 156–166 (2005)CrossRefGoogle Scholar
  10. 10.
    J.A. Veltman, A.W.K. Gaillard, Physiological indicies of workload in a simulated flight task. Biol. Psychol. 42, 323–342 (1996)CrossRefGoogle Scholar
  11. 11.
    K. Nagamine, A. Nozawa, H. Ide, Evaluation of emotions by Nasal Skin temperature on auditory stimulus and olfactory stimulus. IEEJ Trans. EIS 124(9), 1914–1915 (2004)CrossRefGoogle Scholar
  12. 12.
    J. Herbert, Fortnightly review: stress, the brain, and mental illness. British Med. J. 315, 530–535 (1997)CrossRefGoogle Scholar
  13. 13.
    L. Lidberg, G. Wallin, Sympathhetic skin nerve discharges in relation to amplitude of skin resistance responses. Psychopysiology 18(3), 268–270 (1981)CrossRefGoogle Scholar
  14. 14.
    P.H. Venables, M.J. Christie, Electrodermal activity, Techniques in, Psychophysiology (2012)Google Scholar
  15. 15.
    R. Chowdhury, M. Reaz, A.M. Mohd, A. Bakar, K. Chellappan, T. Chang, Surface electromyography signal processing and classification techniques. Sensors 13, 12431–12466 (2013)CrossRefGoogle Scholar
  16. 16.
    G. Wei, F. Tian, G. Tang, C. Wang, A wavelet-based method to predict muscle forces from surface electromyography signals in weightlifting. J. Bionic Eng. 9, 48–58 (2012)CrossRefGoogle Scholar
  17. 17.
    B. Hudgins, P. Parker, R.N. Scott, A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40, 8294 (1993)CrossRefGoogle Scholar
  18. 18.
    M. Malik, J.T. Bigger, A.J. Camm, R.E. Kleiger, A. Malliani, A.J. Moss, P.J. Schwartz, Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 17(2), 1043–1065 (1996)Google Scholar
  19. 19.
    P. Leijdekkers, V. Gay, W. Frederick, CaptureMyEmotion: a mobile app to improve emotion learning for autistic children using sensors, in 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 381–384, 2013Google Scholar
  20. 20.
    P.J. Lang, M.M. Bradley, B.N. Cuthbert, International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual (Technical Report A-8. University of Florida, Gainesville, 2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arturo Martínez-Rodrigo
    • 1
  • Roberto Zangróniz
    • 1
  • José Manuel Pastor
    • 1
  • José Miguel Latorre
    • 2
  • Antonio Fernández-Caballero
    • 3
  1. 1.Universidad de Castilla-La ManchaInstituto de Tecnologías AudiovisualesCuencaSpain
  2. 2.Universidad de Castilla-La ManchaInstituto de Investigacin En Discapacidades NeurolgicasAlbaceteSpain
  3. 3.Universidad de Castilla-La ManchaInstituto de Investigacin En Informitica de AlbaceteAlbaceteSpain

Personalised recommendations