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Learning deep physiological models of affect
Open accessAbstract
Feature extraction and feature selection are crucial phases in the process of affective modeling. Both, however, incorporate substantial limitations that hinder the development of reliable and accurate models of affect. For the purpose of modeling affect manifested through physiology, this paper builds on recent advances in machine learning with deep learning (DL) approaches. The efficiency of DL algorithms that train artificial neural network models is tested and compared against standard feature extraction and selection approaches followed in the literature. Results on a game data corpus - containing players' physiological signals (i.e., skin conductance and blood volume pulse) and subjective self-reports of affect - reveal that DL outperforms manual ad-hoc feature extraction as it yields significantly more accurate affective models. Moreover, it appears that DL meets and even outperforms affective models that are boosted by automatic feature selection, for several of the scenarios examined. As the DL method is generic and applicable to any affective modeling task, the key findings of the paper suggest that ad-hoc feature extraction and selection - to a lesser degree - could be bypassed.
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“For this purpose, they used CNNs trained in an unsupervised way via AEs to learn features from raw blood volume pulse and skin conductance signals, which outperformed models based on handcrafted features.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [172]
In the citing paper
“Martinez et al. [172] were the first to learn deep features directly from the peripheral physiology.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [172]
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Metadata
- Authors
- Martinez, H. P., Bengio, Y., & Yannakakis, G. N.
- Affiliations
- Université de Montréal (CA)
- University of Malta (MT)
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2013-05
- Volume
- 8
- Issue
- 2
- Pages
- 20-33
- ISSN
- 1556-603X (ISSN-L)
- OpenAlex
- W2103184652
- Cited by
- 287
- References
- 74
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 18 · 2023: 22 · 2022: 50 · 2021: 23 · 2020: 28 · 2019: 29 · 2018: 32 · 2017: 25 · 2016: 22 · 2015: 17 · 2014: 14 · 2013: 1
- FWCI
- 24.33
- Type
- journal article
- Language
- en
- Open access
- green
- www.um.edu.mt
- Locations
- IEEE Computational Intelligence Magazine (publishedVersion)
- OAR@UM (University of Malta) (submittedVersion, other-oa)
- Funders
- Canada Research Chairs
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- emotion modeling, physiological signals, deep learning, feature extraction, feature selection, skin conductance, blood volume pulse, self-reported affect, game data, artificial neural networks, affective computing
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