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.

使用瞳孔测量的虚拟现实情绪识别方法，通过分析虚拟现实头戴设备对视觉和听觉刺激的瞳孔直径响应，从时域、频域和时频域提取关键特征并利用特征选择获取最重要的特征，再应用集成学习技术中的梯度提升模型，通过特征工程将准确率从 84.9% 提升至 98.8%，该研究为 Thelxino"e 框架作出了显著贡献，旨在通过整合多个传感器数据实现更真实和情感共鸣的触感交互，研究结果为进一步发展沉浸式交互虚拟现实环境以及虚拟触觉技术打开了新的可能性。