a comparison of physiological signal analysis techniques and classifiers for automatic emotional evaluation of audiovisual contents
Clicks: 254
ID: 253252
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
254 views
27 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #63 of 128 articles by views in population health management
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 128 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a thirty-minutes audiovisual content. This content was composed by a submarine documentary and nine commercials (one of them the ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and Random Forest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.
| Reference Key |
granero2016frontiersa
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Adrián Colomer Granero;Félix Fuentes Hurtado;Valery Naranjo Ornedo;Jaime Guixeres Provinciale;Mariano Alcañiz Raya;Mariano Alcañiz Raya;Jose Manuel Ausín |
| Journal | population health management |
| Year | 2016 |
| DOI |
10.3389/fncom.2016.00074
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.