Support vector machine tuning for improving four-quadrant emotion prediction in Virtual Reality (VR) using wearable Electrodermography (EDG)

A F Bulagang and Mountstephens, James and Teo, Jason Tze Wi (2021) Support vector machine tuning for improving four-quadrant emotion prediction in Virtual Reality (VR) using wearable Electrodermography (EDG). In: Second International Conference on Emerging Electrical Energy, Electronics and Computing Technologies 2020, 28-29 October 2020, Melaka, Malaysia.

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Official URL: https://iopscience.iop.org/article/10.1088/1742-65...

Abstract

Electrodermography (EDG) / Galvanic Skin Response (GSR) indicates the psychophysiological of emotion, EDG is an emerging signal used in the field of emotion classification aside from Electroencephalography (EEG) and Electrocardiography (ECG). The Empatica E4 wearable device was used in collecting EDG signals and employed as the method in capturing the test subject's physiological signal of their skin activity. This experiment had 10 participants that use a Virtual Reality (VR) headset for viewing video stimuli in 360 degrees while collecting the EDG signals. Python with Support Vector Machine (SVM) was used in processing the 10 subjects' data. This paper aims to compare the accuracy of the SVM experiments with different parameters, different settings based on the data retrieved from the wearable. The emotions were classified into four distinct quadrants with inter-subject classifications yielding an accuracy of 54.3%, and intra-subject classification yielded an accuracy of 57.1% to 99.2%. The presented results show that it is possible to achieve results with higher accuracy when parameter tuning. Hence, promising results were demonstrated for emotion prediction in four quadrants using wearable EDG technology in virtual reality environments. This paper provides two contributions, the use of EDG signals in emotion prediction, and the parameter setting to increase the accuracy for SVM classification.

Item Type:	Conference or Workshop Item (Paper)
Keyword:	Vector machine tuning , Four-quadrant emotion prediction , Virtual Reality , VR , Wearable Electrodermography , EDG
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SAFRUDIN BIN DARUN -
Date Deposited:	06 Sep 2021 13:05
Last Modified:	06 Sep 2021 13:05
URI:	https://eprints.ums.edu.my/id/eprint/30268

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