Detecting emotions through non-invasive wearables

[EN] Current research on computational intelligence is being conducted in order to emulate and/or detect emotional states using specific devices such as wristbands or similar wearables. In this sense, this paper proposes the use of intelligent wristbands for the automatic detection of emotional states in order to develop an application which allows us to extract, analyse, represent and manage the social emotion of a group of entities. Nowadays, most of the existing approaches are centred in the emotion detection and management of a single entity. The designed system has been developed as a multi-agent system where each agent controls a wearable device and is in charge of detecting individual emotions based on bio-signals.Rincon, JA.; Julian Inglada, VJ.; Carrascosa Casamayor, C.; Araujo, A.; Novais, P. (2018). Detecting emotions through non-invasive wearables. Logic Journal of IGPL. 26(6):605-617. https://doi.org/10.1093/jigpal/jzy025605617266Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688-2710. doi:10.1016/j.comnet.2010.05.003Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49-59. doi:10.1016/0005-7916(94)90063-9J. C. Castillo , J.Serrano-Cuerda, A.Fernández-Caballero and A.Martínez-Rodrigo. Hierarchical architecture for robust people detection by fusion of infrared and visible video. InIntelligent Distributed Computing IX, pp. 343–351. Springer, Berlin, Heidelberg, 2015.J. Gratch and S.Marsella. Tears and fears: modeling emotions and emotional behaviors in synthetic agents. InProceedings of the Fifth International Conference on Autonomous Agents, pp. 278–285. ACM, 2001.Hristoskova, A., Sakkalis, V., Zacharioudakis, G., Tsiknakis, M., & De Turck, F. (2014). Ontology-Driven Monitoring of Patient’s Vital Signs Enabling Personalized Medical Detection and Alert. Sensors, 14(1), 1598-1628. doi:10.3390/s140101598Janssen, J. H., van den Broek, E. L., & Westerink, J. H. D. M. (2011). Tune in to your emotions: a robust personalized affective music player. User Modeling and User-Adapted Interaction, 22(3), 255-279. doi:10.1007/s11257-011-9107-7E. Maier and G.Kempter. ALADIN - a magic lamp for the elderly? In Handbook of Ambient Intelligence and Smart Environments, pp. 1201–1227. Springer, Berlin, Heidelberg, 2010.Mehrabian, A. (1997). Analysis of Affiliation-Related Traits in Terms of the PAD Temperament Model. The Journal of Psychology, 131(1), 101-117. doi:10.1080/00223989709603508Ramos, J., Oliveira, T., Satoh, K., Neves, J., & Novais, P. (2016). Orientation System Based on Speculative Computation and Trajectory Mining. Communications in Computer and Information Science, 250-261. doi:10.1007/978-3-319-39387-2_21V. Sanchez-Anguix , A.Espinosa, L.Hernandez and A.Garcia-Fornes. Mamsy: a management tool for multi-agent systems. In7th International Conference on Practical Applications of Agents and Multi-agent Systems (PAAMS 2009), pp. 130–139. Springer, 2009.Tao, J., Tan, T., & Picard, R. W. (Eds.). (2005). Affective Computing and Intelligent Interaction. Lecture Notes in Computer Science. doi:10.1007/11573548Tran, N., Coffman, J. M., Sumino, K., & Cabana, M. D. (2014). Patient reminder systems and asthma medication adherence: a systematic review. Journal of Asthma, 51(5), 536-543. doi:10.3109/02770903.2014.888572O. Tunalı , R.Aydoğan and V.Sanchez-Anguix. Rethinking frequency opponent modeling in automated negotiation. In International Conference on Principles and Practice of Multi-Agent Systems, pp. 263–279. Springer, 2017.Walter, M., Eilebrecht, B., Wartzek, T., & Leonhardt, S. (2011). The smart car seat: personalized monitoring of vital signs in automotive applications. Personal and Ubiquitous Computing, 15(7), 707-715. doi:10.1007/s00779-010-0350-