Emotion Recognition from Galvanic Skin Response Signal Based on Deep Hybrid Neural Networks

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曹義棟

Imam Yogie Susanto
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潘則佑

Tse-Yu Pan
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陳建文

Chien-Wen Chen

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 framework 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 Rasperberry Pi and design a real-time emotion interaction robot to verify the efficiency of this work.

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