Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization, in Ageing and Life Course (2011)
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 2012
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, 2009
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, 2014
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)
M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, V.C.M. Leung, Body area networks: a survey. Mobile Netw. Appl. 16, 171–193 (2011)
P. Remagnino, G.L. Foresti, Ambient intelligence: a new multidisciplinary paradigm. IEEE Trans. Syst. Man Cybern. Part A 35(1), 1–6 (2005)
J.A. Russell, A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)
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)
J.A. Veltman, A.W.K. Gaillard, Physiological indicies of workload in a simulated flight task. Biol. Psychol. 42, 323–342 (1996)
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)
J. Herbert, Fortnightly review: stress, the brain, and mental illness. British Med. J. 315, 530–535 (1997)
L. Lidberg, G. Wallin, Sympathhetic skin nerve discharges in relation to amplitude of skin resistance responses. Psychopysiology 18(3), 268–270 (1981)
P.H. Venables, M.J. Christie, Electrodermal activity, Techniques in, Psychophysiology (2012)
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)
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)
B. Hudgins, P. Parker, R.N. Scott, A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40, 8294 (1993)
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)
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, 2013
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)
Acknowledgments
This work was partially supported by Spanish Ministerio de Economía y Competitividad / FEDER under TIN2013-47074-C2-1-R grant.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Martínez-Rodrigo, A., Zangróniz, R., Pastor, J.M., Latorre, J.M., Fernández-Caballero, A. (2015). 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. Advances in Intelligent Systems and Computing, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-19695-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-19695-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19694-7
Online ISBN: 978-3-319-19695-4
eBook Packages: EngineeringEngineering (R0)