Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics
Author(s)Valenza, Gaetano; Citi, Luca; Lanatá, Antonio; Scilingo, Enzo Pasquale; Barbieri, Riccardo
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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.
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Nature Publishing Group
Valenza, Gaetano, Luca Citi, Antonio Lanatá, Enzo Pasquale Scilingo, and Riccardo Barbieri. “Revealing Real-Time Emotional Responses: a Personalized Assessment Based on Heartbeat Dynamics.” Sci. Rep. 4 (May 21, 2014).
Final published version