Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning
This work addresses emotion recognition for enhancing VR experiences, but it is incremental as it builds on existing methods like pupillometry and machine learning.
The study tackled emotion recognition in Virtual Reality by analyzing pupil diameter responses to stimuli and using feature selection with a Gradient Boosting model, achieving 98.8% accuracy with feature engineering compared to 84.9% without it.
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.