Sokolov Oleksandr, Meszyński Sebastian, Dreszer-Drogorób Joanna, Bałaj Bibianna, Duch Włodzisław, Grzelak Sławomir, Komendziński Tomasz, Mikołajewski Dariusz. Intelligent emotions stabilization system using standarized images, breath sensor and biofeedback – preliminary findings - short communication. Journal of Education, Health and Sport. 2015;5(2):260-268. ISSN 2391-8306. DOI: 10.5281/zenodo.16251 http://ojs.ukw.edu.pl/index.php/johs/article/view/2015%3B5%282%29%3A260-268 https://pbn.nauka.gov.pl/works/548026 http://dx.doi.org/10.5281/zenodo.16251 Formerly Journal of Health Sciences. ISSN 1429-9623 / 2300-665X. Archives 2011 – 2014 http://journal.rsw.edu.pl/index.php/JHS/issue/archive Deklaracja. Specyfika i zawartość merytoryczna czasopisma nie ulega zmianie. Zgodnie z informacją MNiSW z dnia 2 czerwca 2014 r., że w roku 2014 nie będzie przeprowadzana ocena czasopism naukowych; czasopismo o zmienionym tytule otrzymuje tyle samo punktów co na wykazie czasopism naukowych z dnia 31 grudnia 2014 r. The journal has had 5 points in Ministry of Science and Higher Education of Poland parametric evaluation. Part B item 1089. (31.12.2014). © The Author (s) 2015; This article is published with open access at Licensee Open Journal Systems of Kazimierz Wielki University in Bydgoszcz, Poland and Radom University in Radom, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 20.10.2014. Revised 18.01.2015. Accepted: 25.02.2015. Intelligent emotions stabilization system using standarized images, breath sensor and biofeedback – preliminary findings - short communication Oleksandr Sokolov1, 2, Sebastian Meszyński1, Joanna Dreszer-Drogorób3, 2, Bibianna Bałaj3, 2, Włodzisław Duch1, 2, Sławomir Grzelak4, Tomasz Komendziński3, 2, Dariusz Mikołajewski5, 1, 2 1 Department of Informatics, Nicolaus Copernicus University, Toruń, Poland 2 Neurocognitive Laboratory, Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland 3 Faculty of Humanities, Nicolaus Copernicus University, Toruń, Poland 4 Faculty of Physics, Nicolaus Copernicus University, Toruń, Poland 5 Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland Address for correspondence: Prof. dr hab. Oleksandr Sokolov Department of Informatics Nicolaus Copernicus University Grudziądzka 5 87-100 Toruń, Poland e-mail: osokolov@is.umk.pl Abstract Purpose: Currently there is no direct method available to automatically distinguish between emotions by real-time computational analysis and stabilize emotional state of the subject using series of standarized pictures or others stimuli. The aim of this study was to find out whether is true if our new concept of intelligent emotions stabilization system can constitute another step toward better analysis and understanding of the aformantioned processess. The aim of this study was to compare normative ratings of the Nencki Affective Picutre System standardized images database with the emotional respiratory patterns. Methods: The study group comprised of 150 healthy people (50% females; aged 20-26 years). Computational models of responses to the stimuli and reverse problem were created using the Matlab environment. Results: The models were able to predict the levels of arousal, valence and self-emotional state in 90%. Conclusions: Proposed approach can open a family of novel efficient methods for control of emotions by measure of breathing and appropriate sets of images (e.g. in affective computing applications). Keywords: emotion, affective state, patent-therapist cooperation, computational analysis, artificial intelligence.