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.
| Original language | English |
|---|---|
| Title of host publication | ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics |
| Editors | Oleg Gusikhin, Kurosh Madani, Janan Zaytoon |
| Publisher | SciTePress |
| Pages | 595-600 |
| Number of pages | 6 |
| ISBN (Electronic) | 9789897583803 |
| Publication status | Published - 2019 Jan 1 |
| Event | 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 - Prague, Czech Republic Duration: 2019 Jul 29 → 2019 Jul 31 |
Publication series
| Name | ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics |
|---|---|
| Volume | 1 |
Conference
| Conference | 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 19/7/29 → 19/7/31 |
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Keywords
- Arousal
- Convolutional neural network
- EMG
- Physiological signal
- PPG
- Valence
ASJC Scopus subject areas
- Information Systems
- Control and Systems Engineering
Cite this
PPG and EMG based emotion recognition using convolutional neural network. / Lee, Min Seop; Cho, Ye Ri; Lee, Yun Kyu; Pae, Dong Sung; Lim, Myo Taeg; Kang, Tae Koo.
ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics. ed. / Oleg Gusikhin; Kurosh Madani; Janan Zaytoon. SciTePress, 2019. p. 595-600 (ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - PPG and EMG based emotion recognition using convolutional neural network
AU - Lee, Min Seop
AU - Cho, Ye Ri
AU - Lee, Yun Kyu
AU - Pae, Dong Sung
AU - Lim, Myo Taeg
AU - Kang, Tae Koo
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Arousal
KW - Convolutional neural network
KW - EMG
KW - Physiological signal
KW - PPG
KW - Valence
UR - http://www.scopus.com/inward/record.url?scp=85073121968&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073121968&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85073121968
T3 - ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
SP - 595
EP - 600
BT - ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
A2 - Gusikhin, Oleg
A2 - Madani, Kurosh
A2 - Zaytoon, Janan
PB - SciTePress
ER -