Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning

In this study, we present a method for emotion recognition in Virtual Reality (VR) using pupillometry. We analyze pupil diameter responses to both visual and auditory stimuli via a VR headset and focus on extracting key features in the time-domain, frequency-domain, and time-frequency domain from VR...

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Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: Barker, Darlene, Levkowitz, Haim
Format: Article
Language:English
Subjects:
Auditory stimuli
Decision trees
Emotion recognition
Emotions
Ensemble learning
Frequency domain analysis
Machine learning
Pupillometry
Redundancy
Virtual reality
Visual stimuli
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Summary:In this study, we present a method for emotion recognition in Virtual Reality (VR) using pupillometry. We analyze pupil diameter responses to both visual and auditory stimuli via a VR headset and focus on extracting key features in the time-domain, frequency-domain, and time-frequency domain from VR generated data. Our approach utilizes feature selection to identify the most impactful features using Maximum Relevance Minimum Redundancy (mRMR). By applying a Gradient Boosting model, an ensemble learning technique using stacked decision trees, we achieve an accuracy of 98.8% with feature engineering, compared to 84.9% without it. This research contributes significantly to the Thelxino\"e framework, aiming to enhance VR experiences by integrating multiple sensor data for realistic and emotionally resonant touch interactions. Our findings open new avenues for developing more immersive and interactive VR environments, paving the way for future advancements in virtual touch technology.
ISSN:2331-8422
DOI:10.48550/arxiv.2403.19014