Valenza, Gaetano and Citi, Luca and Lanatá, Antonio and Scilingo, Enzo Pasquale and Barbieri, Riccardo (2014) Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics. Scientific Reports, 4 (1). 4998-. DOI https://doi.org/10.1038/srep04998
Valenza, Gaetano and Citi, Luca and Lanatá, Antonio and Scilingo, Enzo Pasquale and Barbieri, Riccardo (2014) Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics. Scientific Reports, 4 (1). 4998-. DOI https://doi.org/10.1038/srep04998
Valenza, Gaetano and Citi, Luca and Lanatá, Antonio and Scilingo, Enzo Pasquale and Barbieri, Riccardo (2014) Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics. Scientific Reports, 4 (1). 4998-. DOI https://doi.org/10.1038/srep04998
Abstract
Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.
Item Type: | Article |
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Uncontrolled Keywords: | Applied mathematics; Biomedical engineering; Computational biophysics; Computational science |
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: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 23 Jan 2015 12:47 |
Last Modified: | 30 Oct 2024 19:50 |
URI: | http://repository.essex.ac.uk/id/eprint/12344 |
Available files
Filename: srep04998.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0