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About Sensors: A Deep-Learning Model For Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors
This document discusses a deep learning model for subject-independent human emotion recognition using electrodermal activity (EDA) sensors. The model uses a convolutional neural network architecture trained on EDA data from two datasets. For subject-independent classification, the model achieved 78% accuracy for one dataset and 82% for the other, classifying 4 emotions. For subject-dependent classification, accuracy was 81% for one dataset and 85% for the other. The model performs well for subject-independent emotion recognition using only EDA sensors, outperforming previous approaches using EDA and replacing the need for other physiological sensors.
Detailed Information
| Author: | ['ennio888'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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