One-dimensional convolutional neural networks for low/high arousal classification from electrodermal activity

Abstract

The rapid identification of arousal is of great interest in various applications such as health care for the elderly, athletes, drivers and students, among others. Therefore, advanced methods are needed to classify the level of activation autonomously. In this paper, three architectures based on one-dimensional convolutional networks (1D-CNN) using electrodermal activity as physiological input are proposed. These have been designed for low and high arousal discrimination, elicited through video clips. The first architecture, based on a purely convolutional architecture, has yielded an F1-score of 81.95%. Two other architectures (hybrid), based on 1D-CNN-LSTM (long short-term memory) and 1D-CNN-BiLSTM (bidirectional LSTM), have outperformed the first one, obtaining 88.95% and 91.02% F1-score, respectively. Furthermore, a comparison of these methods has been performed with widely used network architectures such as AlexNet, GoogLeNet, VGG16, VGG19 and ResNet-50, which have obtained F1-scores 82.09%, 83.14%, 82.69%, 83.95% and 82.00%, respectively. Our architectures offer good performance with shorter training time compared to pretrained architectures.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 43 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.