A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals

Cheng Jie Yang; Nicolas Fahier; Wei Chih Li; Wai Chi Fang

doi:10.1109/ICCE-Taiwan49838.2020.9258341

A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals

Cheng Jie Yang, Nicolas Fahier, Wei Chih Li, Wai Chi Fang

電子研究所

研究成果: Conference contribution › 同行評審

20 引文斯高帕斯（Scopus）

摘要

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.

原文	English
主出版物標題	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781728173993
DOIs	https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258341
出版狀態	Published - 28 9月 2020
事件	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, 台灣持續時間: 28 9月 2020 → 30 9月 2020

出版系列

名字	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
國家/地區	台灣
城市	Taoyuan
期間	28/09/20 → 30/09/20

UN SDG

此研究成果有助於以下永續發展目標

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10.1109/ICCE-Taiwan49838.2020.9258341

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Link to publication in Scopus

引用此

Yang, C. J., Fahier, N., Li, W. C., & Fang, W. C. (2020). A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals. 於 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 文章 9258341 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258341

Yang, Cheng Jie ; Fahier, Nicolas ; Li, Wei Chih 等. / A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals. 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020).

@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",

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Yang, CJ, Fahier, N, Li, WC & Fang, WC 2020, A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals. 於 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020., 9258341, 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Taoyuan, 台灣, 28/09/20. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258341

A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals. / Yang, Cheng Jie; Fahier, Nicolas; Li, Wei Chih 等.
2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9258341 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals

AU - Yang, Cheng Jie

AU - Fahier, Nicolas

AU - Li, Wei Chih

AU - Fang, Wai Chi

PY - 2020/9/28

Y1 - 2020/9/28

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

BT - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Yang CJ, Fahier N, Li WC, Fang WC. A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals. 於 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9258341. (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020). doi: 10.1109/ICCE-Taiwan49838.2020.9258341

A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals

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