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A nonlinear heartbeat dynamics model approach for personalized emotion recognition

Valenza, Gaetano and Citi, Luca and Lanata, Antonio and Scilingo, Enzo Pasquale and Barbieri, Riccardo (2013) 'A nonlinear heartbeat dynamics model approach for personalized emotion recognition.' 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013. pp. 2579-2582. ISSN 1557-170X

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Abstract

Emotion recognition based on autonomic nervous system signs is one of the ambitious goals of affective computing. It is well-accepted that standard signal processing techniques require relative long-time series of multivariate records to ensure reliability and robustness of recognition and classification algorithms. In this work, we present a novel methodology able to assess cardiovascular dynamics during short-time (i.e. < 10 seconds) affective stimuli, thus overcoming some of the limitations of current emotion recognition approaches. We developed a personalized, fully parametric probabilistic framework based on point-process theory where heartbeat events are modelled using a 2nd-order nonlinear autoregressive integrative structure in order to achieve effective performances in short-time affective assessment. Experimental results show a comprehensive emotional characterization of 4 subjects undergoing a passive affective elicitation using a sequence of standardized images gathered from the international affective picture system. Each picture was identified by the IAPS arousal and valence scores as well as by a self-reported emotional label associating a subjective positive or negative emotion. Results show a clear classification of two defined levels of arousal, valence and self-emotional state using features coming from the instantaneous spectrum and bispectrum of the considered RR intervals, reaching up to 90% recognition accuracy. © 2013 IEEE.

Item Type: Article
Uncontrolled Keywords: Humans; Emotions; Pattern Recognition, Physiological; Heart Rate; Algorithms; Nonlinear Dynamics; Signal Processing, Computer-Assisted; Young Adult
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 23 Jan 2015 12:51
Last Modified: 23 Sep 2022 18:26
URI: http://repository.essex.ac.uk/id/eprint/12345

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