Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)
Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)
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DOI：
10.1109/access.2018.2883213
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Arunkumar, N.
中科院分区：
文献类型：
--
作者：
Santamaria-Granados, Luz;Munoz-Organero, Mario;Arunkumar, N.
推荐系统一直基于上下文和内容，现在基于用户情绪状态进行个性化推荐的技术挑战来自于从设备或传感器获得的生理信号。本文将深度卷积神经网络的深度学习方法应用于生理信号(心电和皮肤电反应)的数据集，在这种情况下，是Amigos数据集。情绪的检测是通过将这些生理信号与该数据集的唤醒和效价数据相关联来完成的，以分类一个人的情感状态。此外，还提出了一种基于经典机器学习算法的情感识别应用，以提取生理信号在时间域、频率域和非线性域的特征。该应用使用卷积神经网络对生理信号进行自动特征提取，并通过全连接的网络层进行情感预测。在Amigos数据集上的实验结果表明，与该数据集作者最初获得的分类结果相比，本文提出的方法获得了更高的情感状态分类精度。
Recommender systems have been based on context and content, and now the technological challenge of making personalized recommendations based on the user emotional state arises through physiological signals that are obtained from devices or sensors. This paper applies the deep learning approach using a deep convolutional neural network on a dataset of physiological signals (electrocardiogram and galvanic skin response), in this case, the AMIGOS dataset. The detection of emotions is done by correlating these physiological signals with the data of arousal and valence of this dataset, to classify the affective state of a person. In addition, an application for emotion recognition based on classic machine learning algorithms is proposed to extract the features of physiological signals in the domain of time, frequency, and non-linear. This application uses a convolutional neural network for the automatic feature extraction of the physiological signals, and through fully connected network layers, the emotion prediction is made. The experimental results on the AMIGOS dataset show that the method proposed in this paper achieves a better precision of the classification of the emotional states, in comparison with the originally obtained by the authors of this dataset.