PPG and EMG based emotion recognition using convolutional neural network

Min Seop Lee, Ye Ri Cho, Yun Kyu Lee, Dong Sung Pae, Myo Taeg Lim, Tae Koo Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
EditorsOleg Gusikhin, Kurosh Madani, Janan Zaytoon
PublisherSciTePress
Pages595-600
Number of pages6
ISBN (Electronic)9789897583803
Publication statusPublished - 2019 Jan 1
Event16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 - Prague, Czech Republic
Duration: 2019 Jul 292019 Jul 31

Publication series

NameICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
Volume1

Conference

Conference16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019
CountryCzech Republic
CityPrague
Period19/7/2919/7/31

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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

Lee, M. S., Cho, Y. R., Lee, Y. K., Pae, D. S., Lim, M. T., & Kang, T. K. (2019). PPG and EMG based emotion recognition using convolutional neural network. In O. Gusikhin, K. Madani, & J. Zaytoon (Eds.), ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (pp. 595-600). (ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics; Vol. 1). SciTePress.