Key result
Emotion recognition systems built using the A2ES dataset showed that SVM was the best learning algorithm for ECG data, while Random Forest was the best for PPG data.
Why the study?
Affective databases based on physiological signals from the Asian continent have been lacking, which is important for ensuring inclusiveness and avoiding bias in affective computing.
The introduction of the A2ES dataset provides a valuable resource of ECG and PPG signals from Asian participants for affective computing research, addressing a gap in diverse physiological data.
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.
No takes yet. Share an insight, caveat, or question.
Aziz et al. (2023) studied Emotion recognition (n=47). Machine learning algorithms (SVM, KNN, NB, DT, RF) and deep learning vs. Comparison among algorithms was evaluated on Emotion recognition performance. Emotion recognition systems built using the A2ES dataset showed that SVM was the best learning algorithm for ECG data, while Random Forest was the best for PPG data.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: