Respiration-based emotion recognition with deep learning
Respiration-based emotion recognition with deep learning
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DOI：
10.1016/j.compind.2017.04.005
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发表时间：
2017-11-01
影响因子：
10
通讯作者：
Xia, Shanhong
中科院分区：
文献类型：
--
作者：
Zhang, Qiang;Chen, Xianxiang;Xia, Shanhong
不同的生理信号有不同的来源，可以描述人体的不同功能。本文仅研究呼吸（RSP）信号，以了解其检测心理活动的能力。提出了一种深度学习框架来提取和识别呼吸的情感信息。唤醒效价理论通过将情绪映射到二维空间来帮助识别情绪。深度学习框架包括一个稀疏自动编码器（SAE）来提取情感相关特征，以及两个逻辑回归，一个用于唤醒分类，另一个用于效价分类。为了开发这项工作，采用了国际情绪分类数据库（称为使用生理信号进行情绪分析数据集（DEAP））来建立模型。为了进一步评估所提出的方法对其他人的影响，模型建立后，我们使用了德国奥格斯堡大学建立的情感数据库。 DEAP 上的效价和唤醒分类准确率分别为 73.06% 和 80.78%，Augsburg 数据集的平均准确率为 80.22%。这项研究证明了利用可穿戴设备收集的呼吸来识别人类情绪的潜力。 (C) 2017 年由 Elsevier B.V. 出版
Different physiological signals are of different origins and may describe different functions of the human body. This paper studied respiration (RSP) signals alone to figure out its ability in detecting psychological activity. A deep learning framework is proposed to extract and recognize emotional information of respiration. An arousal-valence theory helps recognize emotions by mapping emotions into a two dimension space. The deep learning framework includes a sparse auto-encoder (SAE) to extract emotion related features, and two logistic regression with one for arousal classification and the other for valence classification. For the development of this work an international database for emotion classification known as Dataset for Emotion Analysis using Physiological signals (DEAP) is adopted for model establishment. To further evaluate the proposed method on other people, after model establishment, we used the affection database established by Augsburg University in Germany. The accuracies for valence and arousal classification on DEAP are 73.06% and 80.78% respectively, and the mean accuracy on Augsburg dataset is 80.22%. This study demonstrates the potential to use respiration collected from wearable deices to recognize human emotions. (C) 2017 Published by Elsevier B.V.