Aaron Frederick Bulagang and James Mountstephens and Jason Teo (2021) Electrodermography and heart rate sensing for multiclass emotion prediction in virtual reality: A preliminary investigation.
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Electrodermography and heart rate sensing for multiclass emotion prediction in virtual reality, A preliminary investigation.ABSTRACT.pdf Download (60kB) |
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Abstract
This paper demonstrates a method for classifying multi-model emotions using a combination of Heart Rate (HR) and Electrodermography (EDG) signals with SVM (Support Vector Machine) as the classifier in Virtual Reality (VR). A wearable was used during the experiment to acquire the subject's HR and EDG signals simultaneously while watching 360O videos in VR. The acquired signals are then classified with SVM in a multi-class model for valence and arousal. The experiment conducted is for 10 intra-subject classifications, in which two subjects achieved the best accuracy of 99.5%, while for inter-subject classification of 10 subjects achieved 66.0%, This paper demonstrates that combined signals of HR and EDG can provide high accuracy for multi-class emotion classification in VR.
| Item Type: | Proceedings |
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| Keyword: | Emotion classification , Heart rate , Electrodermography , SVM , Virtual reality |
| Subjects: | B Philosophy. Psychology. Religion > BF Psychology > BF1-990 Psychology T Technology > T Technology (General) > T1-995 Technology (General) |
| Department: | FACULTY > Faculty of Computing and Informatics |
| Depositing User: | DG MASNIAH AHMAD - |
| Date Deposited: | 19 May 2022 10:20 |
| Last Modified: | 19 May 2022 10:20 |
| URI: | https://eprints.ums.edu.my/id/eprint/32527 |
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