Computational recognition of human emotion using Deep Learning techniques requires learning from large collections of data. However, the complex processes involved in collecting and annotating physiological data lead to datasets with small sample sizes. Models trained on such limited data often do not generalize well to real-world settings. To address the problem of data scarcity, we use an Auxiliary Conditioned Wasserstein Generative Adversarial Network with Gradient Penalty (AC-WGAN-GP) to generate synthetic data. We compare the recognition performance between real and synthetic signals as training data in the task of binary arousal classification. Experiments on GSR and ECG signals show that generative data augmentation significantly improves model performance (avg. 16.5%) for binary arousal classification in a subject-independent setting.

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doi.org/10.1145/3460418.3479301
2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2021 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2021
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Furdui, A, Zhang, T, Worring, M, César Garcia, P.S, & El Ali, A. (2021). AC-WGAN-GP: Augmenting ECG and GSR Signals using Conditional Generative Models for Arousal Classification. In Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers (pp. 21–22). doi:10.1145/3460418.3479301