Abstract
Despite over 20 years of research in affective computing, emotion prediction models that would be useful in real-life out-of-the-lab scenarios such as health care or intelligent assistants have still not been developed. The identification of the fundamental problems behind this concern led to the initiation of the BIRAFFE series of experiments, whose main goal is to develop a set of techniques, tools and good practices to introduce personalized context-based emotion processing modules in intelligent systems/assistants. The aim of this work is to present the work-in-progress concept of the third experiment in the BIRAFFE series and discuss the results of the pilot study. After all conclusions have been drawn up, actual study will be carried out, and then the collected data will be processed and made available under the creative commons license as BIRAFFE3 dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bhatt, P., et al.: Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions. Brain Inform. 10(1), 18 (2023). https://doi.org/10.1186/s40708-023-00196-6
Bradley, M.M., Lang, P.J.: The international affective digitized sounds (2nd edition; iads-2): affective ratings of sounds and instruction manual. technical report B-3. Technical report, University of Florida, Gainsville, FL (2007)
Costa, P., McCrae, R.: Revised NEO Personality Inventory (NEO-PI-R) and NEO Five Factor Inventory (NEO-FFI). Professional manual. Psychological Assessment Resources, Odessa, FL (1992)
Dan-Glauser, E.S., Scherer, K.R.: The geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance. Behav. Res. Methods 43(2), 468–477 (2011). https://doi.org/10.3758/s13428-011-0064-1
van Dooren, M., de Vries, J.J.G., Janssen, J.H.: Emotional sweating across the body: comparing 16 different skin conductance measurement locations. Physiol. Behav. 106(2), 298–304 (2012)
Dzedzickis, A., Kaklauskas, A., Bucinskas, V.: Human emotion recognition: review of sensors and methods. Sensors 20(3), 592 (2020). https://doi.org/10.3390/s20030592
Fanourakis, M., Chanel, G.: AMuCS: affective multimodal counter-strike video game dataset (2024). https://doi.org/10.36227/techrxiv.170630398.84528625/v1
Hasnul, M.A., Aziz, N.A.B.A., Alelyani, S., Mohana, M., Aziz, A.A.: Electrocardiogram-based emotion recognition systems and their applications in healthcare - a review. Sensors 21(15), 5015 (2021). https://doi.org/10.3390/s21155015
IJsselsteijn, W.A., de Kort, Y.A.W., Poels, K.: The Game Experience Questionnaire. Technische Universiteit Eindhoven (2013)
Katsis, C.D., Katertsidis, N.S., Ganiatsas, G., Fotiadis, D.I.: Toward emotion recognition in car-racing drivers: a biosignal processing approach. IEEE Trans. Syst. Man Cybern. Part A 38(3), 502–512 (2008). https://doi.org/10.1109/TSMCA.2008.918624
Khare, S.K., Blanes-Vidal, V., Nadimi, E.S., Acharya, U.R.: Emotion recognition and artificial intelligence: a systematic review (2014–2023) and research recommendations. Inf. Fusion 102, 102019 (2024). https://doi.org/10.1016/J.INFFUS.2023.102019
Kutt, K., Bobek, S., Nalepa, G.J.: BIRAFFE: bio-reactions and faces for emotion-based personalization. Zenodohttps://doi.org/10.5281/zenodo.3442143 (2020)
Kutt, K., Drążyk, D., Bobek, S., Nalepa, G.J.: Personality-based affective adaptation methods for intelligent systems. Sensors 21(1), 163 (2021). https://doi.org/10.3390/s21010163
Kutt, K., et al.: BIRAFFE: bio-reactions and faces for emotion-based personalization. In: AfCAI 2019. CEUR Workshop Proceedings, vol. 2609. CEUR-WS.org (2020)
Kutt, K., Drążyk, D., Żuchowska, L., Szelążek, M., Bobek, S., Nalepa, G.J.: BIRAFFE2, a multimodal dataset for emotion-based personalization in rich affective game environments. Sci. Data 9, 274 (2022). https://doi.org/10.1038/s41597-022-01402-6
Kutt, K., Ściga, Ł., Nalepa, G.J.: Emotion-based dynamic difficulty adjustment in video games. In: DSAA 2023, pp. 1–5. IEEE (2023). https://doi.org/10.1109/DSAA60987.2023.10302578
Kutt, K., Sobczyk, P., Nalepa, G.J.: Evaluation of selected APIs for emotion recognition from facial expressions. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds.) IWINAC 2022. LNCS, vol. 13259, pp. 65–74. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06527-9_7
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPs): affective ratings of pictures and instruction manual. technical report B-3. Technical report, The Center for Research in Psychophysiology, University of Florida, Gainsville, FL (2008)
Lara-Cabrera, R., Camacho, D.: A taxonomy and state of the art revision on affective games. Futur. Gener. Comput. Syst. 92, 516–525 (2019)
Michałowski, J.M., Droździel, D., Matuszewski, J., Koziejowski, W., Jednoróg, K., Marchewka, A.: The set of fear inducing pictures (SFIP): development and validation in fearful and nonfearful individuals. Behav. Res. Methods 49(4), 1407–1419 (2017). https://doi.org/10.3758/s13428-016-0797-y
Milkowski, P., Saganowski, S., Gruza, M., Kazienko, P., Piasecki, M., Kocon, J.: Multitask personalized recognition of emotions evoked by textual content. In: PerCom 2022 Workshops, pp. 347–352. IEEE (2022). https://doi.org/10.1109/PerComWorkshops53856.2022.9767502
Nalepa, G.J., Kutt, K., Giżycka, B., Jemioło, P., Bobek, S.: Analysis and use of the emotional context with wearable devices for games and intelligent assistants. Sensors 19(11), 2509 (2019). https://doi.org/10.3390/s19112509
Park, C.Y., et al.: K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations. Sci. Data 7(1), 293 (2020). https://doi.org/10.1038/s41597-020-00630-y
Peirce, J., et al.: Psychopy2: experiments in behavior made easy. Behav. Res. Methods 51(1), 195–203 (2019). https://doi.org/10.3758/s13428-018-01193-y
Phan, L.V., Rauthmann, J.F.: Personality computing: New frontiers in personality assessment. Soc. Pers. Psychol. Compass 15(7) (2021). https://doi.org/10.1111/spc3.12624
Prokop, M., Pilar, L., Tichá, I.: Impact of think-aloud on eye-tracking: a comparison of concurrent and retrospective think-aloud for research on decision-making in the game environment. Sensors 20(10), 2750 (2020). https://doi.org/10.3390/s20102750
Saganowski, S., Perz, B., Polak, A.G., Kazienko, P.: Emotion recognition for everyday life using physiological signals from wearables: a systematic literature review. IEEE Trans. Affect. Comput. 12(1), 1–21 (2021). https://doi.org/10.1109/TAFFC.2022.3176135
Zawadzki, B., Strelau, J., Szczepaniak, P., Śliwińska, M.: Inwentarz osobowości NEO-FFI Costy i McCrae. Adaptacja polska. Pracownia Testów Psychologicznych, Warszawa (1998)
Zhao, S., Gholaminejad, A., Ding, G., Gao, Y., Han, J., Keutzer, K.: Personalized emotion recognition by personality-aware high-order learning of physiological signals. ACM Trans. Multim. Comput. Commun. Appl. 15(1s), 14:1–14:18 (2019). https://doi.org/10.1145/3233184
Acknowledgments
The research for this publication has been supported by a grant from the Priority Research Area DigiWorld under the Strategic Programme Excellence Initiative at Jagiellonian University. The research has been supported by a grant from the Faculty of Physics, Astronomy and Applied Computer Science under the Strategic Programme Excellence Initiative at Jagiellonian University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kutt, K., Nalepa, G.J. (2024). Emotion Prediction in Real-Life Scenarios: On the Way to the BIRAFFE3 Dataset. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_44
Download citation
DOI: https://doi.org/10.1007/978-3-031-61140-7_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61139-1
Online ISBN: 978-3-031-61140-7
eBook Packages: Computer ScienceComputer Science (R0)