Abstract
In this paper, we present a research study on the classification of emotions, through data gathered on a smartphone. To this end, we have developed a mobile application to elicit emotions in participants using memory tasks with success – failure manipulation and also using video clips. Interactions were recorded with accelerometer and gyroscope sensors records and keystroke on the device. We trained supervised classification models, with the records, to predict the nature of emotion elicited on two dimensions (pleasure and activation) and the success or failure related tasks memory tasks. In order to evaluate the emotion induction we have proposed a self-assessment procedure. We achieved interesting results on the pleasure dimension, by proposing a protocol with natural interactions on smartphone.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Picard, R.: Affective computing. Perceptual Computing Section, Media Laboratory, Massachusetts Institute of Technology (1995)
Hudlicka, E.: Guidelines for designing computational models of emotions. Int. J. Synth. Emot. (IJSE) 2, 26–79 (2011)
Ekman, P.: An argument for basic emotions. Cogn. Emot. 6, 169–200 (1992)
Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161 (1980)
Poria, S., Cambria, E., Bajpai, R.: A review of affective computing: from unimodal analysis to multimodal fusion. Inf. Fusion 37, 98–125 (2017)
Olsen, A.: Smartphone accelerometer data used for detecting human emotions, pp. 410–415 (2016)
Coutrix, C.: Identifying emotions expressed by mobile users through 2D surface and 3D motion gestures, pp. 311–320 (2012)
Ghosh, S., Ganguly, N., Mitra, B.: TapSense: combining self-report patterns and typing characteristics for smartphone based emotion detection (2017)
Cui, L., Li, S.: Emotion detection from natural walking, pp. 23–33 (2016)
Epp, C., Lippold, M.: Identifying emotional states using keystroke dynamics, pp. 715–724 (2011)
Ferrer, R.A., Grenen, E.G., Taber, J.M.: Effectiveness of internet-based affect induction procedures: a systematic review and meta-analysis. Emotion 15(6), 752 (2015)
Nummenmaa, L., Niemi, P.: Inducing affective states with success-failure manipulations: a meta-analysis. Emotion 4(2), 207 (2004)
Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49–59 (1994)
Anguita, D., Ghio, A., Oneto, L., Parra, X.: A public domain dataset for human activity recognition using smartphones (2013)
Khan, A., Lee, Y.-K., Lee, S.-Y.: Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis, pp. 1–6 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Simonazzi, N., Salotti, JM., Dubois, C., Seminel, D. (2021). Emotion Detection Based on Smartphone Using User Memory Tasks and Videos. In: Ahram, T., Taiar, R., Langlois, K., Choplin, A. (eds) Human Interaction, Emerging Technologies and Future Applications III. IHIET 2020. Advances in Intelligent Systems and Computing, vol 1253. Springer, Cham. https://doi.org/10.1007/978-3-030-55307-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-55307-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55306-7
Online ISBN: 978-3-030-55307-4
eBook Packages: EngineeringEngineering (R0)