Emotion recognition from galvanic skin response signal based on deep hybrid neural networks
Journal
ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval
ISBN
9781450370875
Date Issued
2020-06-08
Author(s)
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.
Subjects
Deep neural networks | Electrodermal activity | Emotion recognition | Galvanic skin response | Healthcare
Type
conference paper