Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910388368441344 |
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| author | Barker, Darlene Levkowitz, Haim |
| author_facet | Barker, Darlene Levkowitz, Haim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19014 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning Barker, Darlene Levkowitz, Haim Machine Learning Human-Computer Interaction 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. |
| title | Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.19014 |