A wavelet-based approach to emotion classification using EDA signals. (1st December 2018)

• Record Type: Journal Article
• Authors: Feng, Huanghao; Golshan, Hosein M.; Mahoor, Mohammad H.
• Is Part Of: Expert systems with applications. Volume 112(2018)
• Page Start: 77
• Page End: 86
• Publication Date: 2018-12-01
• Subjects: Emotion classification -- Feature extraction -- Time-frequency analysis -- Wearable device -- Eletrodermal activity; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.06.014
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Emotion classification based on the biological signals acquired from human subjects. Evaluating the capability of wearable assistive device (e.g., Q-sensors) in recognizing emotions. Wavelet-based feature extraction and time-frequency analysis of electrodermal activity (EDA) signals. ABSTRACT: 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 electrodermal 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.