AC-WGAN-GP: Augmenting ECG and GSR Signals using Conditional Generative Models for Arousal Classification

Abstract

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 realworld 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. CCS CONCEPTS• Computing methodologies → Machine learning; • Humancentered computing → Human computer interaction (HCI).

A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data

“…Reference [23] used three different cGAN architectures and an adapted diversity term to augment a pathological Photoplethysmogram (PPG) dataset, to further improve a classifier. The authors of [24] proposed a method that generates synthetic physiological data with a cGAN to classify the arousal state of human subjects. This work is close to our objective, but there was very little description of the methodology used.…”