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Using Non-invasive Wearables for Detecting Emotions with Intelligent Agents

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International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 (SOCO 2016, CISIS 2016, ICEUTE 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

Abstract

This paper proposes the use of intelligent wristbands for the automatic detection of emotional states in order to develop an application which allows to extract, analyze, represent and manage the social emotion of a group of entities. Nowadays, the detection of the joined emotion of an heterogeneous group of people is still an open issue. Most of the existing approaches are centered in the emotion detection and management of a single entity. Concretely, the application tries to detect how music can influence in a positive or negative way over individuals’ emotional states. The main goal of the proposed system is to play music that encourages the increase of happiness of the overall patrons.

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Notes

  1. 1.

    http://www.samsung.com.

  2. 2.

    http://www.apple.com.

  3. 3.

    http://www.intel.la/content/www/xl/es/do-it-yourself/edison.html.

  4. 4.

    https://www.yoctoproject.org/.

  5. 5.

    https://github.com/javipalanca/spade.

References

  1. Alemdar, H., Ersoy, C.: Wireless sensor networks for healthcare: a survey. Comput. Netw. 54(15), 2688–2710 (2010). http://dx.doi.org/10.1016/j.comnet.2010.05.003

    Article  Google Scholar 

  2. Baig, M.M., GholamHosseini, H., Connolly, M.J., Kashfi, G.: Real-time vital signs monitoring and interpretation system for early detection of multiple physical signs in older adults. In: IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE (2014). http://dx.doi.org/10.1109/BHI.2014.6864376

  3. 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, Heidelberg (2014). doi:10.1007/978-3-319-13105-4_46

    Chapter  Google Scholar 

  4. Castillo, J.C., Serrano-Cuerda, J., Fernández-Caballero, A., Martínez-Rodrigo, A.: Hierarchical architecture for robust people detection by fusion of infrared and visible video. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds.) Intelligent Distributed Computing IX. SCI, vol. 616, pp. 343–351. Springer, Heidelberg (2016). doi:10.1007/978-3-319-25017-5_32

    Chapter  Google Scholar 

  5. Chuang, Z.J., Wu, C.H.: Multi-modal emotion recognition from speech and text. J. Comput. Linguist. Chin. 9(2), 45–62 (2004). http://www.aclweb.org/anthology/O/O04/O04-3004.pdf

    Google Scholar 

  6. Colby, B.N., Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions, vol. 18. Cambridge University Press, Cambridge (1989)

    Google Scholar 

  7. Coutinho, E., Cangelosi, A.: Musical emotions: Predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. Emotion 11(4), 921–937 (2011). (Washington, D.C.)

    Article  Google Scholar 

  8. Escriva, M., Palanca, J., Aranda, G., García-Fornes, A., Julian, V., Botti, V.: A Jabber-based multi-agent system platform. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), pp. 1282–1284. Association for Computing Machinery, Inc. (ACM Press) (2006)

    Google Scholar 

  9. Fensli, R., Pedersen, P.E., Gundersen, T., Hejlesen, O.: Sensor acceptance model - measuring patient acceptance of wearable sensors. Method Inf. Med. 47, 89–95 (2008). http://dx.doi.org/10.3414/ME9106

    Article  Google Scholar 

  10. Fishkin, K.P., Jiang, B., Philipose, M., Roy, S.: I sense a disturbance in the force: unobtrusive detection of interactions with RFID-tagged objects. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 268–282. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30119-6_16

    Chapter  Google Scholar 

  11. Gratch, J., Marsella, S.: Tears and fears: modeling emotions and emotional behaviors in synthetic agents. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 278–285. ACM (2001). http://dl.acm.org/citation.cfm?id=376309

  12. Han, K., Yu, D., Tashev, I.: Speech emotion recognition using deep neural network and extreme learning machine. In: Fifteenth Annual Conference of Interspeech, pp. 223–227, September 2014. http://research.microsoft.com/pubs/230136/IS140441.PDF

  13. Hert, P., Gutwirth, S., Moscibroda, A., Wright, D., Fuster, G.G.: Legal safeguards for privacy and data protection in ambient intelligence. Pers. Ubiquit. Comput. 13(6), 435–444 (2008). http://www.springerlink.com/index/10.1007/s00779-008-0211-6

    Article  Google Scholar 

  14. Hristoskova, A., Sakkalis, V., Zacharioudakis, G., Tsiknakis, M., Turck, F.D.: Ontology-driven monitoring of patient’s vital signs enabling personalized medical detection and alert. Sensors 14(1), 1598–1628 (2014). http://dx.doi.org/10.3390/s140101598

    Article  Google Scholar 

  15. Karthigayan, M., Rizon, M., Nagarajan, R., Yaacob, S.: Genetic algorithm and neural network for face emotion recognition. In: Affective Computing, pp. 57–68 (2008). http://cdn.intechopen.com/pdfs-wm/5178.pdf

  16. Koelstra, S., Mühl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)

    Article  Google Scholar 

  17. Kuo, C.H., Chen, C.T., Chen, T.S., Kuo, Y.C.: A wireless sensor network approach for rehabilitation data collections. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics. Institute of Electrical & Electronics Engineers (IEEE) (2011). http://dx.doi.org/10.1109/ICSMC.2011.6083773

  18. Maaoui, C., Pruski, A.: Emotion recognition through physiological signals for human-machine communication. In: Cutting Edge Robotics, pp. 317–333 (2010). http://www.intechopen.com/source/pdfs/12200/InTech-Emotion_recognition_through_physiological_signals_for_human_machine_communication.pdf

  19. Maier, E., Kempter, G.: ALADIN - a magic lamp for the elderly? In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of Ambient Intelligence and Smart Environments, pp. 1201–1227. Springer, Berlin, Heidelberg (2010). http://dx.doi.org/10.1007/978-0-387-93808-0_44

    Chapter  Google Scholar 

  20. Mehrabian, A.: Analysis of affiliation-related traits in terms of the PAD temperament model. J. Psychol. 131(1), 101–117 (1997). http://dx.doi.org/10.1080/00223989709603508

    Article  Google Scholar 

  21. Piana, S., Odone, F., Verri, A., Camurri, A.: Real-time Automatic Emotion Recognition from Body Gestures. arXiv preprint arXiv:1402.5047, pp. 1–7 (2014). http://xxx.tau.ac.il/pdf/1402.5047.pdf

  22. Ramos, J., Oliveira, T., Satoh, K., Neves, J., Novais, P.: Orientation system based on speculative computation and trajectory mining. In: Bajo, J., et al. (eds.) PAAMS 2016. CCIS, vol. 616, pp. 250–261. Springer, Heidelberg (2016). doi:10.1007/978-3-319-39387-2_21

    Chapter  Google Scholar 

  23. Rincon, J.A., Julian, V., Carrascosa, C.: Social emotional model. In: Demazeau, Y., Decker, K.S., Bajo Pérez, J., de la Prieta, F. (eds.) PAAMS 2015. LNCS (LNAI), vol. 9086, pp. 199–210. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18944-4_17

    Chapter  Google Scholar 

  24. Salmeron, J.L.: Fuzzy cognitive maps for artificial emotions forecasting. Appl. Soft Comput. J. 12(12), 3704–3710 (2012). http://dx.doi.org/10.1016/j.asoc.2012.01.015

    Article  Google Scholar 

  25. Scherer, K.R., Zentner, M.R.: Emotional effects of music: production rules. In: Music and Emotion: Theory and Research, pp. 361–392 (2001). http://icquran.persiangig.com/weblog/schererzentner.pdf

  26. Stawarz, K., Cox, A.L., Blandford, A.: Don’t forget your pill! In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, CHI 2014. Association for Computing Machinery (ACM) (2014). http://dx.doi.org/10.1145/2556288.2557079

  27. Thayer, R.: The Biopsychology of Mood and Arousal. Oxford University Press, Oxford (1989)

    Google Scholar 

  28. Tran, N., Coffman, J.M., Sumino, K., Cabana, M.D.: Patient reminder systems and asthma medication adherence: a systematic review. J. Asthma 51(5), 536–543 (2014). http://dx.doi.org/10.3109/02770903.2014.888572

    Article  Google Scholar 

  29. Villarejo, M.V., Zapirain, B.G., Zorrilla, A.M.: A stress sensor based on galvanic skin response (GSR) controlled by ZigBee. Sensors 12(5), 6075–6101 (2012). (Switzerland)

    Article  Google Scholar 

  30. Walter, M., Eilebrecht, B., Wartzek, T., Leonhardt, S.: The smart car seat: personalized monitoring of vital signs in automotive applications. Pers. Ubiquit. Comput. 15(7), 707–715 (2011). http://dx.doi.org/10.1007/s00779-010-0350-4

    Article  Google Scholar 

  31. Whitman, B., Smaragdis, P.: Combining musical and cultural features for intelligent style detection. In: Ismir, pp. 5–10, Paris, France (2002). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.8383&rep=rep1&type=pdf

  32. Yik, M., Russell, J.A., Steiger, J.H.: A 12-point circumplex structure of core affect. Emotion 11(4), 705–731 (2011)

    Article  Google Scholar 

  33. van der Zwaag, M.D., Westerink, J.H.D.M., van den Broek, E.L.: Emotional and psychophysiological responses to tempo, mode, and percussiveness. Musicae Scientiae 15(2), 250–269 (2011). http://msx.sagepub.com/content/15/2/250.short

    Article  Google Scholar 

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Acknowledgements

This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon. This work is supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the projects UID/CEC/00319/2013 and Post-Doc scholarship SFRH/BPD/102696/2014 (A. Costa)

Author information

Authors and Affiliations

  1. D. Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain

    Jaime Andres Rincon, Vicente Julian & Carlos Carrascosa

  2. Centro ALGORITMI, Escola de Engenharia, Universidade do Minho, Guimarães, Portugal

    Ângelo Costa & Paulo Novais

Authors
  1. Jaime Andres Rincon
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  2. Ângelo Costa
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  3. Paulo Novais
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  4. Vicente Julian
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  5. Carlos Carrascosa
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Corresponding author

Correspondence to Jaime Andres Rincon .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

  2. University of the Basque Country, Vitoria, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Rincon, J.A., Costa, Â., Novais, P., Julian, V., Carrascosa, C. (2017). Using Non-invasive Wearables for Detecting Emotions with Intelligent Agents. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-47364-2_8

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