@inproceedings{c0ee8bbfe5cf4e2b878e1726432283cb,
title = "PPG and EMG based emotion recognition using convolutional neural network",
abstract = "Emotion recognition is an essential part of human computer interaction and there are many sources for emotion recognition. In this study, physiological signals, especially electromyogram (EMG) and photoplethysmogram (PPG) are used to detect the emotion. To classify emotions in more detail, the existing method of modeling emotion which represents the emotion as valence and arousal is subdivided by four levels. Convolutional Neural network (CNN) is adopted for feature extraction and emotion classification. We measure the EMG and PPG signals from 30 subjects using selected 32 videos. Our method is evaluated by what we acquired from participants.",
keywords = "Arousal, Convolutional neural network, EMG, PPG, Physiological signal, Valence",
author = "Lee, {Min Seop} and Cho, {Ye Ri} and Lee, {Yun Kyu} and Pae, {Dong Sung} and Lim, {Myo Taeg} and Kang, {Tae Koo}",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 ; Conference date: 29-07-2019 Through 31-07-2019",
year = "2019",
doi = "10.5220/0007797005950600",
language = "English",
series = "ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics",
publisher = "SciTePress",
pages = "595--600",
editor = "Oleg Gusikhin and Kurosh Madani and Janan Zaytoon",
booktitle = "ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics",
}