Asian Affective and Emotional State (A2ES) Dataset of ECG and PPG for Affective Computing Research

• Published in: Algorithms Vol. 16; no. 3; p. 130
• Main Authors: Ab. Aziz, Nor Azlina, K., Tawsif, Ismail, Sharifah Noor Masidayu Sayed, Hasnul, Muhammad Anas, Ab. Aziz, Kamarulzaman, Ibrahim, Siti Zainab, Abd. Aziz, Azlan, Raja, J. Emerson
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accuracy; Affective computing; Algorithms; Bias; Blood; Coronaviruses; COVID-19; Data collection; Data mining; Datasets; Decision trees; Deep learning; Electrocardiogram; Electrocardiography; Electroencephalography; Emotion recognition; emotion recognition system; Emotional factors; Emotions; Ethnicity; Heart; Machine learning; Medical research; Mental health; Pandemics; physiological signals; Physiology; Self assessment; Skin; Stress; Support vector machines; Visual stimuli; Wearable computers; Malaysia
• DOI: 10.3390/a16030130
• ISSN: 1999-4893

Affective computing focuses on instilling emotion awareness in machines. This area has attracted many researchers globally. However, the lack of an affective database based on physiological signals from the Asian continent has been reported. This is an important issue for ensuring inclusiveness and avoiding bias in this field. This paper introduces an emotion recognition database, the Asian Affective and Emotional State (A2ES) dataset, for affective computing research. The database comprises electrocardiogram (ECG) and photoplethysmography (PPG) recordings from 47 Asian participants of various ethnicities. The subjects were exposed to 25 carefully selected audio–visual stimuli to elicit specific targeted emotions. An analysis of the participants’ self-assessment and a list of the 25 stimuli utilised are also presented in this work. Emotion recognition systems are built using ECG and PPG data; five machine learning algorithms: support vector machine (SVM), k-nearest neighbour (KNN), naive Bayes (NB), decision tree (DT), and random forest (RF); and deep learning techniques. The performance of the systems built are presented and compared. The SVM was found to be the best learning algorithm for the ECG data, while RF was the best for the PPG data. The proposed database is available to other researchers.