Physiological Signals and Affect as Predictors of Advertising Engagement
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/15/6916 |
| _version_ | 1797585977269551104 |
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| author | Gregor Strle Andrej Košir Urban Burnik |
| author_facet | Gregor Strle Andrej Košir Urban Burnik |
| author_sort | Gregor Strle |
| collection | DOAJ |
| description | 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 <i>n</i> = 5) performed better than those trained on all signals (features n = 30). The average AUC ROC scores for the fusion set were XGB = 0.68 (0.10), LGBM = 0.69 (0.07), and HGBC = 0.70 (0.11), compared to the lower scores for the set of all signals (XGB = 0.65 (0.11), LGBM = 0.66 (0.11), HGBC = 0.64 (0.10)). The results also show that the signal fusion set based on skin temperature outperforms the fusion sets of the other three signals. The main finding of this study is the role of specific physiological signals and how their fusion aids in more effective modeling of ad engagement while reducing the number of features. |
| first_indexed | 2024-03-11T00:16:38Z |
| format | Article |
| id | doaj.art-943a96e8371b47c1958899e76a8b6bef |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:38Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-943a96e8371b47c1958899e76a8b6bef2023-11-18T23:36:12ZengMDPI AGSensors1424-82202023-08-012315691610.3390/s23156916Physiological Signals and Affect as Predictors of Advertising EngagementGregor Strle0Andrej Košir1Urban Burnik2User-Adapted Communication and Ambient Intelligence Lab, Faculty of Electrical Engineering, University of Ljubljana, SI 1000 Ljubljana, SloveniaUser-Adapted Communication and Ambient Intelligence Lab, Faculty of Electrical Engineering, University of Ljubljana, SI 1000 Ljubljana, SloveniaUser-Adapted Communication and Ambient Intelligence Lab, Faculty of Electrical Engineering, University of Ljubljana, SI 1000 Ljubljana, SloveniaThis 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 <i>n</i> = 5) performed better than those trained on all signals (features n = 30). The average AUC ROC scores for the fusion set were XGB = 0.68 (0.10), LGBM = 0.69 (0.07), and HGBC = 0.70 (0.11), compared to the lower scores for the set of all signals (XGB = 0.65 (0.11), LGBM = 0.66 (0.11), HGBC = 0.64 (0.10)). The results also show that the signal fusion set based on skin temperature outperforms the fusion sets of the other three signals. The main finding of this study is the role of specific physiological signals and how their fusion aids in more effective modeling of ad engagement while reducing the number of features.https://www.mdpi.com/1424-8220/23/15/6916physiologyaffectengagementadvertisementuser modelingclassification |
| spellingShingle | Gregor Strle Andrej Košir Urban Burnik Physiological Signals and Affect as Predictors of Advertising Engagement Sensors physiology affect engagement advertisement user modeling classification |
| title | Physiological Signals and Affect as Predictors of Advertising Engagement |
| title_full | Physiological Signals and Affect as Predictors of Advertising Engagement |
| title_fullStr | Physiological Signals and Affect as Predictors of Advertising Engagement |
| title_full_unstemmed | Physiological Signals and Affect as Predictors of Advertising Engagement |
| title_short | Physiological Signals and Affect as Predictors of Advertising Engagement |
| title_sort | physiological signals and affect as predictors of advertising engagement |
| topic | physiology affect engagement advertisement user modeling classification |
| url | https://www.mdpi.com/1424-8220/23/15/6916 |
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