Physiological Signals and Affect as Predictors of Advertising Engagement.
advertisement
affect
classification
engagement
machine learning
physiology
user modeling
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
03 Aug 2023
03 Aug 2023
Historique:
received:
10
06
2023
revised:
27
07
2023
accepted:
28
07
2023
medline:
14
8
2023
pubmed:
12
8
2023
entrez:
12
8
2023
Statut:
epublish
Résumé
This study investigated the use of affect and physiological signals of heart rate, electrodermal activity, pupil dilation, and skin temperature to classify advertising engagement. The ground truth for the affective and behavioral aspects of ad engagement was collected from 53 young adults using the User Engagement Scale. Three gradient-boosting classifiers, LightGBM (LGBM), HistGradientBoostingClassifier (HGBC), and XGBoost (XGB), were used along with signal fusion to evaluate the performance of different signal combinations as predictors of engagement. The classifiers trained on the fusion of skin temperature, valence, and tiredness (features
Identifiants
pubmed: 37571700
pii: s23156916
doi: 10.3390/s23156916
pmc: PMC10422422
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Slovenian Research Agency
ID : P2-0246
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