Fast emotion recognition based on single pulse PPG signal with convolutional neural network

Min Seop Lee, Yun Kyu Lee, Dong Sung Pae, Myo Taeg Lim, Dong Won Kim, Tae Koo Kang

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)


Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using a single PPG signal pulse. We used a one-dimensional convolutional neural network (1D CNN) to extract PPG signal features to classify the valence and arousal. We split the PPG signal into a single 1.1 s pulse and normalized it for input to the neural network based on the personal maximum and minimum values. We chose the dataset for emotion analysis using physiological (DEAP) signals for the experiment and tested the 1D CNN as a binary classification (high or low valence and arousal), achieving the short-term emotion recognition of 1.1 s with 75.3% and 76.2% valence and arousal accuracies, respectively, on the DEAP data.

Original languageEnglish
Article number3355
JournalApplied Sciences (Switzerland)
Issue number16
Publication statusPublished - 2019 Aug 1

Bibliographical note

Funding Information:
Funding: This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2016R1D1A1B01016071 and NRF-2016R1D1A1B03936281).

Publisher Copyright:
© 2019 by the authors.


  • One-dimensional convolutional neural network
  • PPG
  • Personal normalization
  • Short term emotion recognition

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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