A wavelet-based approach to emotion classification using EDA signals
A wavelet-based approach to emotion classification using EDA signals
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DOI：
10.1016/j.eswa.2018.06.014
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发表时间：
2018-12-01
影响因子：
8.5
通讯作者：
Mahoor, Mohammad H.
中科院分区：
文献类型：
--
作者：
Feng, Huanghao;Golshan, Hosein M.;Mahoor, Mohammad H.
情绪是一种强烈的心理体验，通常表现为快速的心跳、呼吸、出汗和面部表情。从这些生理信号中进行情绪识别对于开发可穿戴辅助设备和智能人机界面等有趣的应用来说是一个具有挑战性的问题。本文提出了一种利用皮肤电活动 (EDA) 信号对儿童进行情绪分类的自动化方法。对获取的原始 EDA 进行时频分析提供了一个可以识别不同情绪的特征空间。为此，对记录的 EDA 信号应用复数 Morlet (C-Morlet) 小波函数。本文使用的数据集包括一组社交和交流行为的多模式记录以及 100 名 30 个月以下儿童的 EDA 记录。该数据集由两位专家进行注释，提取出“Joy”、“Boredom”和“Acceptance”三种主要情绪对应的时间序列。注释过程的执行考虑了儿童面部表情和 EDA 时间序列之间的同步性。对带注释的 EDA 信号进行各种实验，以使用支持向量机 (SVM) 分类器对情绪进行分类。定量结果表明，当使用所提出的基于小波的特征时，与其他方法相比，情感分类性能显着提高。 (C) 2018 Elsevier Ltd. 保留所有权利。
Emotion is an intense mental experience often manifested by rapid heartbeat, breathing, sweating, and facial expressions. Emotion recognition from these physiological signals is a challenging problem with interesting applications such as developing wearable assistive devices and smart human-computer interfaces. This paper presents an automated method for emotion classification in children using electro-dermal activity (EDA) signals. The time-frequency analysis of the acquired raw EDAs provides a feature space based on which different emotions can be recognized. To this end, the complex Morlet (C-Morlet) wavelet function is applied on the recorded EDA signals. The dataset used in this paper includes a set of multimodal recordings of social and communicative behavior as well as EDA recordings of 100 children younger than 30 months old. The dataset is annotated by two experts to extract the time sequence corresponding to three main emotions including "Joy", "Boredom", and "Acceptance". The annotation process is performed considering the synchronicity between the children's facial expressions and the EDA time sequences. Various experiments are conducted on the annotated EDA signals to classify emotions using a support vector machine (SVM) classifier. The quantitative results show that the emotion classification performance remarkably improves compared to other methods when the proposed wavelet-based features are used. (C) 2018 Elsevier Ltd. All rights reserved.