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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.18538 |
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| _version_ | 1866912392334540800 |
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| author | Wei, Xin Liu, Huakun Hirao, Yutaro Perusquia-Hernandez, Monica Masai, Katsutoshi Uchiyama, Hideaki Kiyokawa, Kiyoshi |
| author_facet | Wei, Xin Liu, Huakun Hirao, Yutaro Perusquia-Hernandez, Monica Masai, Katsutoshi Uchiyama, Hideaki Kiyokawa, Kiyoshi |
| contents | Refractive errors are among the most common visual impairments globally, yet their diagnosis often relies on active user participation and clinical oversight. This study explores a passive method for estimating refractive power using two eye movement recording techniques: electrooculography (EOG) and video-based eye tracking. Using a publicly available dataset recorded under varying diopter conditions, we trained Long Short-Term Memory (LSTM) models to classify refractive power from unimodal (EOG or eye tracking) and multimodal configuration. We assess performance in both subject-dependent and subject-independent settings to evaluate model personalization and generalizability across individuals. Results show that the multimodal model consistently outperforms unimodal models, achieving the highest average accuracy in both settings: 96.207\% in the subject-dependent scenario and 8.882\% in the subject-independent scenario. However, generalization remains limited, with classification accuracy only marginally above chance in the subject-independent evaluations. Statistical comparisons in the subject-dependent setting confirmed that the multimodal model significantly outperformed the EOG and eye-tracking models. However, no statistically significant differences were found in the subject-independent setting. Our findings demonstrate both the potential and current limitations of eye movement data-based refractive error estimation, contributing to the development of continuous, non-invasive screening methods using EOG signals and eye-tracking data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind Your Vision: Multimodal Estimation of Refractive Disorders Using Electrooculography and Eye Tracking Wei, Xin Liu, Huakun Hirao, Yutaro Perusquia-Hernandez, Monica Masai, Katsutoshi Uchiyama, Hideaki Kiyokawa, Kiyoshi Image and Video Processing Machine Learning Refractive errors are among the most common visual impairments globally, yet their diagnosis often relies on active user participation and clinical oversight. This study explores a passive method for estimating refractive power using two eye movement recording techniques: electrooculography (EOG) and video-based eye tracking. Using a publicly available dataset recorded under varying diopter conditions, we trained Long Short-Term Memory (LSTM) models to classify refractive power from unimodal (EOG or eye tracking) and multimodal configuration. We assess performance in both subject-dependent and subject-independent settings to evaluate model personalization and generalizability across individuals. Results show that the multimodal model consistently outperforms unimodal models, achieving the highest average accuracy in both settings: 96.207\% in the subject-dependent scenario and 8.882\% in the subject-independent scenario. However, generalization remains limited, with classification accuracy only marginally above chance in the subject-independent evaluations. Statistical comparisons in the subject-dependent setting confirmed that the multimodal model significantly outperformed the EOG and eye-tracking models. However, no statistically significant differences were found in the subject-independent setting. Our findings demonstrate both the potential and current limitations of eye movement data-based refractive error estimation, contributing to the development of continuous, non-invasive screening methods using EOG signals and eye-tracking data. |
| title | Mind Your Vision: Multimodal Estimation of Refractive Disorders Using Electrooculography and Eye Tracking |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2505.18538 |