Thelxinoë for Emotion Recognition and Touch Processing

Barker, Darlene; Levkowitz, Haim

doi:10.1109/ic_aset58101.2023.10150503

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…The idea is that you can manipulate objects in VR but not feel what you touch, or feel being touched. Our research is aimed at creating a framework to support this happening with the capturing of emotions of the parties involved using sensors, including cameras, to be able to capture the emotions from the brain, face, speech, and body movement [5].…”

Section: Introductionmentioning

confidence: 99%

Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning

Barker,

Levkowitz

2024

MLAIJ

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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 Thelxinoë 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.

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Section: Introductionmentioning

confidence: 99%