A wavelet-based approach to emotion classification using EDA signals

被引:51
作者
Feng, Huanghao [1 ]
Golshan, Hosein M. [1 ]
Mahoor, Mohammad H. [1 ]
机构
[1] Univ Denver, Comp Vis Lab, ECE Dept, Denver, CO 80208 USA
基金
美国国家科学基金会;
关键词
Emotion classification; Feature extraction; Time-frequency analysis; Wearable device; Eletrodermal activity; SKIN-CONDUCTANCE RESPONSES; ELECTRODERMAL ACTIVITY; CIRCUMPLEX MODEL; RECOGNITION; CHILDREN; AROUSAL; DECOMPOSITION; INDIVIDUALS; BEHAVIOR; SYSTEM;
D O I
10.1016/j.eswa.2018.06.014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:77 / 86
页数:10
相关论文
共 61 条
[1]   Principal component analysis [J].
Abdi, Herve ;
Williams, Lynne J. .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (04) :433-459
[2]  
[Anonymous], TECHNICAL REPORT
[3]  
[Anonymous], 2008, THESIS U
[4]  
[Anonymous], 2013, 53 ANN M SOC PSYCH R
[5]  
[Anonymous], 8 INT C INT TUT SYST
[6]  
[Anonymous], 2011, ACM T INTEL SYST TEC, DOI DOI 10.1145/1961189.1961199
[7]   Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers [J].
Atkinson, John ;
Campos, Daniel .
EXPERT SYSTEMS WITH APPLICATIONS, 2016, 47 :35-41
[8]   Assessing the potential of electrodermal activity as an alternative access pathway [J].
Blain, Stefanie ;
Mihailidis, Alex ;
Chau, Tom .
MEDICAL ENGINEERING & PHYSICS, 2008, 30 (04) :498-505
[9]  
Cacioppo J. T., 2000, The Handbook of Emotion
[10]   Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications [J].
Calvo, Rafael A. ;
D'Mello, Sidney .
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2010, 1 (01) :18-37