Abstract
Today’s trend is to use IT in every area of our lives. Technology is primarily used to improve the standard of living. Emotions are the basis of human experience, even though it is difficult to define, recognize, and classify them. Nowadays, greater emphasis and attention is placed on the computer’s ability to evaluate emotional changes and conditions in humans. Proper assessment and recognition of the human emotions may lead to a better understanding of user behavior. Systems that are able to acquire data, evaluate user status and model them have a broad application in various spheres of human activity (neuro-marketing, automotive control, adaptive learning, mental health, etc.). The cognitive process is carried out at two fundamental levels in the level of sensory perception and intellectual perception. In humans, these two basic levels are progressively developed through age or by their own experience. The chapter describes a research study of individual emotional states that can be captured by various sensors, which can quantify and evaluate emotional states of users and thus adapt their surroundings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abramson, L., Marom, I., Petranker, R., Aviezer, H.: Is fear in your head? A comparison of instructed and real-life expressions of emotion in the face and body. Emotion 17(3), 557–565 (2017). https://doi.org/10.1037/emo0000252
Alberdi, A., Aztiria, A., Basarab, A.: Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. J. Biomed. Inform. 59, 49–75 (2016). https://doi.org/10.1016/J.JBI.2015.11.007
Bahreini, K., Nadolski, R., Westera, W.: Towards multimodal emotion recognition in e-learning environments. Interact. Learn. Environ 24(3), 590–605 (2016). https://doi.org/10.1080/10494820.2014.908927
Ben Henia Wiem, M., Lachiri, Z.: Emotion recognition system based on physiological signals with Raspberry Pi III implementation. In: 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP), (pp. 20–24), IEEE (2017) https://doi.org/10.1109/ICFSP.2017.8097053
Carneiro, D., Castillo, J.C., Novais, P., Fernández-Caballero, A., Neves, J.: Multimodal behavioral analysis for non-invasive stress detection. Expert. Syst. Appl. 39(18), 13376–13389 (2012). https://doi.org/10.1016/J.ESWA.2012.05.065
Caruso, D.: Emoční Inteligence. Grada Publishing, a. s, Praha (2015)
Cruz-Albarran, I.A., Benitez-Rangel, J.P., Osornio-Rios, R.A., Morales-Hernandez, L.A.: Human emotions detection based on a smart-thermal system of thermographic images. Infrared Phys. Technol. 81, 250–261 (2017). https://doi.org/10.1016/J.INFRARED.2017.01.002
Czako, M., Seemannova, M., Bratska, M.: Emócie. Slovenské pedagogické nakladateľstvo, Bratislava (1982)
Ekman, P., Friesen, W.: Facial Action Coding System: Investigator’s Guide. Consulting Psychologists Press, Palo Alto, CA (1978)
Gjoreski, M., Luštrek, M., Gams, M., Gjoreski, H.: Monitoring stress with a wrist device using context. J Biomed Inform. (2017)https://doi.org/10.1016/j.jbi.2017.08.006
Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G.: Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf. Fusion 35, 68–80 (2017). https://doi.org/10.1016/J.INFFUS.2016.09.005
Hasson, G.: Inteligenční emoce. Praha:: Grada Publishing, a. s (2015)
Kaklauskas, A., Zavadskas, E. K., Seniut, M., Dzemyda, G., Stankevic, V., Simkevičius, C., Gribniak, V.: Web-based biometric computer mouse advisory system to analyze a user’s emotions and work productivity. Eng. Appl. Artif Intell. 24(6), 928–945 (2011) https://doi.org/10.1016/J.ENGAPPAI.2011.04.006
Lövheim, H.: A new three-dimensional model for emotions and monoamine neurotransmitters. Med. Hypotheses 78(2), 341–348 (2012). https://doi.org/10.1016/j.mehy.2011.11.016
Magdin, M., Turcani, M., Hudec, L.: Evaluating the emotional state of a user using a webcam. Int. J. Interact. Multimedia. Artif. Intell. 4(1), 61 (2016). https://doi.org/10.9781/ijimai.2016.4112
Mattsson, S., Partini, J., Fast-Berglund.: Evaluating four devices that present operator emotions in real-time. Procedia CIRP. 50, 524–528 (2016) https://doi.org/10.1016/j.procir.2016.05.013
Mosciano, F., Mencattini, A., Ringeval, F., Schuller, B., Martinelli, E., Natale, C.Di.: An array of physical sensors and an adaptive regression strategy for emotion recognition in a noisy scenario. Sens. Actuators A 267, 48–59 (2017). https://doi.org/10.1016/j.sna.2017.09.056
Otsuka, T., Ohya, J.: A study of transformation of facial expressions based on expression recognition from temporal image sequences. Inst Electron. Inf. Commun. Eng (IEICE), Technical report. (1997)
Pantic, M., Rothkrantz, L.J.: Automatic analysis of facial expressions: the state of art. IEEE. Trans. Pattern. Recogn. Mach. Intell. (2000)
Plutchik, R.: The nature of emotions: Clinical implications. In: Clynes, M., Panksepp, J. (eds.) Emotions and Psychopathology, (pp. 1–20). Boston: Springer. (1988) https://doi.org/10.1007/978-1-4757-1987-1
Rosenblum, M., Yacoob, Y., Davis, L.: Human expression recognition from motion using a radial basis function network architecture. IEEE Trans Neural Netw. (1996)
Shalini, T.B., Vanitha, L.: Emotion detection in human beings using ECG signals. Int. J. Eng. Trends. Technol. (IJETT) 4(May), 1337–1342 (2013)
Sharma, N., Gedeon, T.: Modeling a stress signal. Appl. Soft Comput. 14, 53–61 (2014). https://doi.org/10.1016/J.ASOC.2013.09.019
Tian, Y., Kanade, T., Cohn, J.: Recognizing Action units for facial expression analysis. IEEE Trans. Pattern. Recogn. Mach. Intell. Carnegie_Mellon University (2001)
Vizer, L.M., Zhou, L., Sears, A.: Automated stress detection using keystroke and linguistic features: an exploratory study. Int. J. Hum. Comput. Stud. 67(10), 870–886 (2009). https://doi.org/10.1016/J.IJHCS.2009.07.005
Vo, M.L.-H., Jacobs, A.M., Kuchinke, L., Hofmann, M., Conrad, M., Schacht, A., Hutzler, F.: The coupling of emotion and cognition in the eye: introducing the pupil old/new effect. Psychophysiology, 0(0), 071003012229007–??? (2007) https://doi.org/10.1111/j.1469-8986.2007.00606.x
Acknowledgements
This research has been supported by University Grant Agency under the contract No. VII/6/2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Francisti, J., Balogh, Z. (2019). Identification of Emotional States and Their Potential. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-6861-5_58
Download citation
DOI: https://doi.org/10.1007/978-981-13-6861-5_58
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6860-8
Online ISBN: 978-981-13-6861-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)