PPG and EMG Based Emotion Recognition using Convolutional Neural Network

被引：8
|
作者：
Lee, Min Seop [1 ]
Cho, Ye Ri [1 ]
Lee, Yun Kyu [1 ]
Pae, Dong Sung [1 ]
Lim, Myo Taeg [1 ]
Kang, Tae Koo [2 ]
机构：
[1] Korea Univ, Sch Elect Engn, Seoul, South Korea
[2] Sangmyung Univ, Dept Human Intelligence & Robot Engn, Cheonan, South Korea
来源：
ICINCO: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 1 | 2019年
关键词：
Valence; Arousal; Convolutional Neural Network; Physiological Signal; PPG; EMG;
D O I：
10.5220/0007797005950600
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：595 / 600
页数：6
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