One-dimensional convolutional neural networks for low/high arousal classification from electrodermal activity
Year: 2021
Type: article
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.
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,040
Funders: Federación Española de Enfermedades Raras, Ministerio de Ciencia e Innovación, Centro de Investigación Biomédica en Red de Salud Mental, University of the East, Ministerio de Educación y Formación Profesional, Instituto de Salud Carlos III, European Regional Development Fund, Agencia Estatal de Investigación
OpenAlex ID: W3206583365