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Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network

1
School of Electrical Engineering, Korea University, Seoul 02841, Korea
2
Department of Digital Electronics, Inha Technical College, Incheon 22212, Korea
3
Department of Human Intelligence and Robot Engineering, Sangmyung University, Cheonan 31066, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3355; https://doi.org/10.3390/app9163355
Received: 24 July 2019 / Revised: 10 August 2019 / Accepted: 12 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)
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Abstract

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. View Full-Text
Keywords: short term emotion recognition; one-dimensional convolutional neural network; PPG; personal normalization short term emotion recognition; one-dimensional convolutional neural network; PPG; personal normalization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lee, M.S.; Lee, Y.K.; Pae, D.S.; Lim, M.T.; Kim, D.W.; Kang, T.K. Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network. Appl. Sci. 2019, 9, 3355.

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