Abstract
Technology is driven by the objective of improving our lives, attending our needs and supporting our work and daily activities. Such guidelines are important to lead technological developments in many areas, whereas the most important result of such enabler is the enhancement of the users’ wellbeing. It is questionable how such technologies make us feel better. One can argue whether environmental conditions can be modified in order to enhance people’s wellbeing, and in what direction. One of the adopted methods in this work explores that thought, on whether the usage of a person’s physiological state can wield adequate sensorial stimulation to be usefully used thereafter. Another question considered in this work is whether it is possible to use such collected data to build a user’s musical playlists that tries to match a user’s physiological and psychological state with the stimuli evoked by the music that he or she is listening.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vieillard, S., Peretz, I., Gosselin, N., Khalfa, S., Gagnon, L., & Bouchard, B. (2008). Happy, sad, scary and peaceful musical excerpts for research on emotions. Cognition and Emotion, 22, 720–752. doi:10.1080/02699930701503567
Dingley, J. (2011). Processing, storage and display of physiological measurements. Anaesth Intensive Care Med, 12, 426–429. doi:10.1016/j.mpaic.2011.06.010
Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 1175–1191. doi:10.1109/34.954607
Takahashi, K., Namikawa, S. Y., & Hashimoto, M. (2012). Computational emotion recognition using multimodal physiological signals: Elicited using Japanese kanji words. In 35th International Conference Telecommunication on Signal Process (TSP) 2012 (pp. 615–20), Prague. doi:10.1109/TSP.2012.6256370
Wen, W., Liu, G., Cheng, N., Wei, J., Shangguan, P., & Huang, W. (2014). Emotion recognition based on multi-variant correlation of physiological signals. IEEE Transactions on Affective Computing, 5, 132–140.
Lin, Y. P., Wang, C. H., Jung, T. P., Wu, T. L., Jeng, S. K., Duann, J. R., et al. (2010). EEG-Based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering, 57, 1798–1806. doi:10.1109/TBME.2010.2048568
Canento, F., Fred, A., Silva, H., Gamboa, H., & Lourenço, A. (2011). Multimodal biosignal sensor data handling for emotion recognition. In Sensors (pp. 647–650). Limerick: IEEE. doi:10.1109/ICSENS.2011.6127029
Balteş, F. R., Avram, J., Miclea, M., & Miu, A. C. (2011). Emotions induced by operatic music: Psychophysiological effects of music, plot, and acting: A scientist’s tribute to Maria Callas. Brain and Cognition, 76, 146–157. doi:10.1016/j.bandc.2011.01.012
Russell, J. A., & Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant. Journal of Personality and Social Psychology, 76, 805–819. doi:10.1037/0022-3514.76.5.805
Feidakis, M., Daradoumis, T., & Caballé, S. (2012). A multi-fold time approach to address emotions in live and virtualized collaborative learning. In 2012 Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS) (pp. 881–886). Palermo: IEEE. doi:10.1109/CISIS.2012.197
Kleinginna, P. R., & Kleinginna, A. M. (1981). A categorized list of emotion definitions, with suggestions for a consensual definition. Motivation and Emotion, 5, 345–379. doi:10.1007/BF00993889
Ortony, A., Clore, G.L., & Collins A. (1990). The Cognitive Structure of Emotions. Cambridge Univ. Press
Ivan Akira. Plutchik’s eight primary emotions and how to use them (Part 1 of 2). https://dragonscanbebeaten.wordpress.com/2010/06/04/plutchiks-eight-primary-emotions-and-how-to-use-them-part-1/. Accessed October 1, 2015.
Yang, Y., Lin, Y., Su, Y., Chen, H.H. (2008). A regression approach to music emotion recognition. IEEE trans audio, speech, and language processing , 16, 448–457. doi:10.1109/TASL.2007.911513
Leung, C.H.C., Deng, J. (2015). Emotion-Based music retrieval. Encycl. Inf. Sci. Technol, IGI Global, 3, 143–53. doi:10.4018/978-1-60566-026-4
Nicolaou, M.A., Gunes, H., Pantic, M. (2011). Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space. IEEE trans affect comput, 2, 92–105. doi:10.1109/T-AFFC.2011.9
Apache Jena (2015). https://jena.apache.org/index.html. Accessed October 1, 2015
Acknowledgments
The research leading to this result has received funding from the European Union 7th Framework Programme (FP7-ICT) under grant agreement of the OSMOSE—OSMOsis applications for the Sensing Enterprise, project ID nr. 610905 (http://www.osmose-project.eu/). It has also received funding from the Portuguese-Serbian bilateral research initiative specifically related to the project entitled: Context-aware Smart Cyber—Physical Ecosystems.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Gião, J., Sarraipa, J., Francisco-Xavier, F., Ferreira, F., Jardim-Goncalves, R., Zdravkovic, M. (2016). Profiling Based on Music and Physiological State. In: Mertins, K., Jardim-Gonçalves, R., Popplewell, K., Mendonça, J. (eds) Enterprise Interoperability VII. Proceedings of the I-ESA Conferences, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-30957-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-30957-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30956-9
Online ISBN: 978-3-319-30957-6
eBook Packages: EngineeringEngineering (R0)