Physiological Signals and Affect as Predictors of Advertising Engagement.

Gregor Strle, Andrej Košir, Urban Burnik

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gregor StrleUser-Adapted Communication and Ambient Intelligence Lab, Faculty of Electrical Engineering, University of Ljubljana, SI 1000 Ljubljana, Slovenia.ORCID 0000-0003-1076-6707

Andrej KoširUser-Adapted Communication and Ambient Intelligence Lab, Faculty of Electrical Engineering, University of Ljubljana, SI 1000 Ljubljana, Slovenia.ORCID 0000-0001-6938-221X

Urban BurnikUser-Adapted Communication and Ambient Intelligence Lab, Faculty of Electrical Engineering, University of Ljubljana, SI 1000 Ljubljana, Slovenia.ORCID 0000-0002-8652-4977

Funding

Slovenian Research Agency P2-0246

6 · The paper itself

Abstract

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

Indexed as

AdvertisingHeart RateHumansYoung Adultadvertisementaffectclassificationengagementmachine learningphysiologyuser modeling

Identifiers

PMID37571700

DOI10.3390/s23156916

PMCPMC10422422

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.