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Early Explorations using KNN to Classify Emotions in Virtual Reality based on Heart Rate (HR) and Electrodermography (EDG)

Authors :
Bulagang Aaron Frederick
Mountstephens James
Teo Jason
Source :
ITM Web of Conferences, Vol 63, p 01002 (2024)
Publication Year :
2024
Publisher :
EDP Sciences, 2024.

Abstract

To detect multimodal emotions using Virtual Reality (VR), this research demonstrates the findings and results of using a KNN Classifier by merging Heart Rate and Electrodermography signals. The participants in the study were shown 360-degree videos using a VR headset to elicit their emotional reactions. A wearable that measures skin activity and pulse rate in real time was used to record their emotional response. The experiment had a total of 30 participants, and the KNN classifier was used to classify intra-subject data. With the HR combined with EDG signals paired with KNN as the classifier, the study’s 30 participants’ data went through intra-subject classification where 11 out of 30 participants achieved a peak accuracy of 100%. These findings show that by combining HR and EDG signals, KNN may be used as the classifier to produce highly accurate results. This study’s possible applications include VR rehabilitation, gaming, and entertainment.

Details

Language :
English
ISSN :
22712097
Volume :
63
Database :
Directory of Open Access Journals
Journal :
ITM Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.590aa240d47a46078edf49b733fbc2ab
Document Type :
article
Full Text :
https://doi.org/10.1051/itmconf/20246301002