Emotion recognition from galvanic skin response signal based on deep hybrid neural networks

Imam Yogie Susanto, Tse Yu Pan, Chien Wen Chen, Min Chun Hu, Wen Huang Cheng

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

Abstract

Emotion reacts human beings' physiological and psychological status. Galvanic Skin Response (GSR) can reveal the electrical characteristics of human skin and is widely used to recognize the presence of emotion. In this work, we propose an emotion recognition frame-work based on deep hybrid neural networks, in which 1D CNN and Residual Bidirectional GRU are employed for time series data analysis. The experimental results show that the proposed method can outperform other state-of-the-art methods. In addition, we port the proposed emotion recognition model on Raspberry Pi and design a real-time emotion interaction robot to verify the efficiency of this work.

Original languageEnglish
Title of host publicationICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages341-345
Number of pages5
ISBN (Electronic)9781450370875
DOIs
StatePublished - 8 Jun 2020
Event10th ACM International Conference on Multimedia Retrieval, ICMR 2020 - Dublin, Ireland
Duration: 8 Jun 202011 Jun 2020

Publication series

NameICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval

Conference

Conference10th ACM International Conference on Multimedia Retrieval, ICMR 2020
CountryIreland
CityDublin
Period8/06/2011/06/20

Keywords

  • Deep neural networks
  • Electrodermal activity
  • Emotion recognition
  • Galvanic skin response
  • Healthcare

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  • Cite this

    Susanto, I. Y., Pan, T. Y., Chen, C. W., Hu, M. C., & Cheng, W. H. (2020). Emotion recognition from galvanic skin response signal based on deep hybrid neural networks. In ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval (pp. 341-345). (ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3372278.3390738