The DOI matches its Crossref record; 69 of the 74 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Self-citationPicard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Self-citationApproximate entropy as a measure of system complexity.
Pincus, S. M. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.6.2297 · Cited by 5,811
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Self-citation
Research works printed but not linked: 6
- susskind, 2010, The Toronto face dataset, Tech Rep TR- 2010-001
- martínez, 2010, Extending neuro-evolution preference learning through player modeling, Proc IEEE Conf Computational Intelligence and Games, 313
- ekman, 1988, Facial action coding system: A technique for the measurement of facial movement, From Appraisal to Emotion D ifferences Among Unpleasant Feelings Motivation and Emotion, 12, 271
- b engio, 2007, Greedy layerwise training of deep networks, Advances in neural information processing systems, 19, 153
- v yzas, 1998, Affective pattern classification, Proc AAAI Fall Symp Series Emotional and Intelligent The Tangled Knot of Cognition, 176
- 10.1016/j.ijhcs.2008.06.004 · DOI
Cited by
Sorted by citations
Zheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,477
Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]
Cambria, E., & White, B. (2014) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2014.2307227 · Cited by 1,399
A review of emotion recognition using physiological signals
Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018) · Sensors · doi:10.3390/s18072074 · Cited by 923
Wearables and the Medical Revolution
Dunn, J., Runge, R., & Snyder, M. (2018) · Personalized Medicine · doi:10.2217/pme-2018-0044 · Cited by 654
A Review of Human Activity Recognition Methods
Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00028 · Cited by 571
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
