Emotion and attention: predicting electrodermal activity through video visual descriptors

Hernández García, Alejandro and Fernández Martínez, Fernando and Díaz de María, Fernando (2017). Emotion and attention: predicting electrodermal activity through video visual descriptors. In: "Proceedings of the International Conference on Web Intelligence (WI '17)", 23/08/2017 - 26/08/2017, Leipzig, Germany. pp. 914-923. https://doi.org/10.1145/3106426.3109418.

Description

Title: Emotion and attention: predicting electrodermal activity through video visual descriptors
Author/s:
  • Hernández García, Alejandro
  • Fernández Martínez, Fernando
  • Díaz de María, Fernando
Item Type: Presentation at Congress or Conference (Article)
Event Title: Proceedings of the International Conference on Web Intelligence (WI '17)
Event Dates: 23/08/2017 - 26/08/2017
Event Location: Leipzig, Germany
Title of Book: International Conference on Web Intelligence (WI '17)
Date: 2017
Subjects:
Freetext Keywords: Electrodermal activity; emotion; attention; affective video content analysis; visual descriptors
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This paper contributes to the field of affective video content analysis through the novel employment of electrodermal activity (EDA) measurements as ground truth for machine learning algorithms. The variation of the electrical properties of the skin, known as EDA, is a psychophysiological indicator widely used in medicine, psychology and neuroscience which can be considered a somatic marker of the emotional and attentional reaction of subjects towards stimuli. One of its main advantages is that the recorded information is not biased by the cognitive process of giving an opinion or a score to characterize the subjective perception. In this work, we predict the levels of emotion and attention, derived from EDA records, by means of a small set of low-level visual descriptors computed from the video stimuli. Linear regression experiments show that our descriptors predict significantly well the sum of emotion and attention levels, reaching a coefficient of determination R 2 = 0.25. This result sets a promising path for further research on the prediction of emotion and attention from videos using EDA.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTC-2016-5305-7UnspecifiedMinisterio de Economía y CompetitividadUnspecified
Government of SpainTEC2014-53390-PUnspecifiedMinisterio de Economía y CompetitividadUnspecified
Horizon 2020641805Marie Sklodowska-CurieUnspecifiedUnspecified

More information

Item ID: 50381
DC Identifier: http://oa.upm.es/50381/
OAI Identifier: oai:oa.upm.es:50381
DOI: 10.1145/3106426.3109418
Official URL: https://dl.acm.org/citation.cfm?doid=3106426.3109418
Deposited by: Memoria Investigacion
Deposited on: 26 Sep 2018 15:20
Last Modified: 26 Sep 2018 15:20
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