@inproceedings{6881e916b977412394b44cac9d046581,
title = "A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals",
abstract = "The detection and recognition of human emotional states have raised recent research interests for various applications from e-learning to chronic health conditions prevention. In this paper, we proposed an emotion recognition system based on the electrocardiogram (ECG) and photoplethysmogram (PPG) signals as objectives data input sources. Three emotion states (positive, neutral, negative) were defined as classification outputs. The training and validation data were collected by Kaohsiung Medical University (KMU) from 47 participants aged from 30 to 50 years old diagnosed with chronic cardiovascular health conditions. A convolution neural network (CNN) was built to efficiently map the subject's emotions with the extracted features from both ECG and PPG signals. This emotion recognition system achieved an accuracy of 75.4% for 3 classes outputs higher or similar than other models used in other works.",
author = "Yang, {Cheng Jie} and Nicolas Fahier and Li, {Wei Chih} and Fang, {Wai Chi}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 ; Conference date: 28-09-2020 Through 30-09-2020",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/ICCE-Taiwan49838.2020.9258341",
language = "English",
series = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
address = "United States",
}
